Callum D Mole

Otto Lappi

Oscar Giles

& Wilkie

Richard M Wilkie
email: r.m.wilkie@leeds.ac.uk

Gustav Markkula

Franck Mars

Getting back into the loop: the perceptual-motor determinants of successful transitions out of automated driving

Keywords: Perception, Action, Steering, Gaze Coordination, Motor Control, Automated Driving, Human Computer Interaction Précis

Two key perceptual-motor control mechanisms are identified as determinants of successful steering transitions after automated driving: perceptual motor calibration and coordination of gaze and steering. We present a conceptual framework and pose research questions that will advance understanding of driver behavior during transitions.

Introduction

T me form of driver assistance [START_REF] Sae | Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles[END_REF]. Many level 2 (SAE, 2016) 'semi-automated' systems (where lateral and hands typically remain on the wheel) are already commercially available (e.g. Traffic jam assist, automated parking; Chan, 2017; Sousa, Almeida, Coutinho-Rodrigues, & Natividade-Jesus, 2017).

There is a long list of car companies trying to rapidly develop vehicles with higher levels of automation, who are promising widespread deployment of AVs by the early 2020s (Chan, 2017;[START_REF] Bagloee | Autonomous vehicles: challenges, opportunities, and future implications for transportation policies[END_REF] with a number of such systems already being piloted on public roads. Most of these AVs will not be 'driverless' (Level 5;[START_REF] Sae | Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles[END_REF], rather they will be Level 3 or 4 systems that are largely automated but still requires a supervising driver who receives handover of control (during a period which we will refer to as a control 'transition') to manage situations where the AV is unable to safely manoeuvre. Transitions might occur in systematic and planned ways (e.g. the AV always hands over control when leaving the motorway), but also for a variety of

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where the AV no longer operates safely, so they could occur at a variety of timescales with differing degrees of warning for the driver depending on whether the AV system is able to identify that a failure state has occurred. Likely examples of AV failure states include situations where information about the environment has become uncertain: e.g. if a road has degraded lane markings or unusual signage, or if the vehicle sensor signals are disrupted due to weather conditions or the GPS signal is weak; [START_REF] Sousa | Dawn of autonomous vehicles: review and challenges ahead[END_REF]. Whilst AV reliability will continue to improve, preventing failure conditions entirely is a massive challenge and so it is likely that transitions are going to be a feature of AVs for the foreseeable future. The AV systems that are able to successfully transition control to the human driver will be the ones that can be deployed most readily, since the AV can be given control during easier-to-automate situations where there is less uncertainty about the vehicle position relative to the external environment (such as driving along motorways) whilst also being able to relinquish control to the human driver when the environment is too complex or uncertain (e.g. driving through a busy city-centre or negotiating country lanes).

The core assumption of level 2-4 AV systems is that humans can and will safely and rapidly take-over control of a moving vehicle. Depending on the situation, however, the human driver may be faced with a set of environmental and vehicle characteristics that are drastically different from when they T that are appropriate for the conditions is not well aligned with our current understanding of how humans perform highly dynamic active control tasks such as steering (see [START_REF] Lappi | Visuomotor Control, Eye Movements, and Steering: A Unified Approach for Incorporating Feedback, Feedforward, and Internal Models[END_REF][START_REF] Van Der El | Measuring and modeling driver steering behavior: From compensatory tracking to curve driving[END_REF] for recent reviews). Nor is it supported by ergonomics research into human rarely required [START_REF] Molloy | Monitoring an Automated System for a Single Failure: Vigilance and Task Complexity Effects[END_REF].

To illustrate the problem, consider the case of a driver relinquishing control to an AV prior to joining a motorway. When the driver was in control of steering they were travelling at fairly slow speeds on a dry road. Once the AV was given control the driver does not feel a pressing need to keep gaze directed to the road ahead, they may look around at the scenery or even direct gaze to other tasks such as reading email. The AV then detects road-works ahead and alerts the driver to take-over T smoothly generate a safe path during the transition event, but in the intervening period (since the driver was last in control of the vehicle) it has rained, causing reduced road friction, and the car is also travelling at a higher speed than previously. If the driver is explicitly aware of the changing conditions they could take some form of tactical precautionary measure (i.e. an arbitrary reduction dynamics (due to lower adherence and increased vehicle speed). Since the driver has not been in control of the vehicle their sensorimotor system may not be well-calibrated to the new environmental conditions and/or vehicle dynamics, and they may not have access to useful perceptual information that would be needed to plan and execute the possible driving actions (either due to not looking at or attending to the road ahead). In this example it seems likely that the ability of the human driver to successfully steer will be diminished (compared to a situation where control had been manual throughout) resulting in less safe lane-keeping or collision-avoidance manoeuvres by the human driver.

Highly automated vehicles may populate our streets in the not-so-distant future (Chan, 2017), yet currently we do not have sufficient understanding of the factors affecting driving performance in transition scenarios to inform the design of these systems. This review examines control transitions from the perspective offered by the extensive literature on human perceptual-motor control. In order to apply these findings it is first important to identify the theoretical framework in which we will situate this research.

Identifying a Framework for Examining Transitions

Driving is complex and can be broken down into numerous subcomponents, the nature of which will depend on the environment being driven through, the familiarity of the driver with the environment,

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framework that employs a hierarchy of distinct control loops (Donges, 1978[START_REF] Donges | A Conceptual Framework for Active Safety in Road Traffic[END_REF][START_REF] Hollnagel | A systemic model for driver-in-control[END_REF][START_REF] Lappi | Visuomotor Control, Eye Movements, and Steering: A Unified Approach for Incorporating Feedback, Feedforward, and Internal Models[END_REF][START_REF] Mcruer | New results in driver steering control models[END_REF][START_REF] Michon | A critical view of driver behavior models: what do we know, what should we do?[END_REF][START_REF] Salvucci | Modeling Driver Behavior in a Cognitive Architecture[END_REF]. Whilst the description of each control level varies across frameworks, many of the underpinning principles are shared. At the highest level, the driver sets navigation goals. The middle levels are responsible for composing the route from a sequence of actions, e.g. changing lanes within the constraints of the current traffic environment. The lowest level is responsible for controlling the underlying perceptualmotor behaviours (lateral and longitudinal control actions) that propel the vehicle along the desired trajectory (Figure 1). Whilst lateral (steering) control and longitudinal (speed) control are clearly related (e.g. speed choice can preclude certain steering responses, and steering response gain will depend on speed) it is also the case that different perceptual variables provide useful information about lateral and longitudinal control. For this reason research into human perceptual-motor control often considers these behaviours independently. The primary behaviour considered in this review is the effect of automation on lateral (steering) control: adjusting the direction of travel to meet the current and upcoming requirements specified by the road.

Within the framework presented in Figure 1, the lower the control level, the higher the feedback frequency [START_REF] Donges | A Conceptual Framework for Active Safety in Road Traffic[END_REF][START_REF] Hollnagel | A systemic model for driver-in-control[END_REF][START_REF] Mcruer | New results in driver steering control models[END_REF][START_REF] Michon | A critical view of driver behavior models: what do we know, what should we do?[END_REF][START_REF] Salvucci | Modeling Driver Behavior in a Cognitive Architecture[END_REF]. Successful steering control (the lowest-level, operational control) is supported by rapid perceptual-motor loops. System lags mean that for smooth control drivers require perceptual inputs not only of the current vehicle lane position and heading, but also preview information obtained from 1-2s ahead, in the direction of where the driver wishes to go (Chattington, Wilson, Ashford, & Marple-Horvat, 2007;[START_REF] Land | Where we look when we steer[END_REF][START_REF] Lappi | Beyond the tangent point: gaze targets in naturalistic driving[END_REF][START_REF] Lehtonen | Effect of driving experience on anticipatory look-ahead fixations in real curve driving[END_REF][START_REF] Lehtonen | Look-ahead fixations in curve driving[END_REF][START_REF] Salvucci | A two-point visual control model of steering[END_REF][START_REF] Wilkie | Active gaze, visual look-ahead, and locomotor control[END_REF][START_REF] Wilkie | Using vision to control locomotion: Looking where you want to go[END_REF] or even further along the road (if road regions are visible beyond this typical preview distance, drivers may make anticipatory look-ahead fixations; [START_REF] Lehtonen | Effect of driving experience on anticipatory look-ahead fixations in real curve driving[END_REF][START_REF] Lehtonen | Look-ahead fixations in curve driving[END_REF][START_REF] Lehtonen | Anticipatory eye movements when approaching a curve on a rural road depend on working memory load[END_REF][START_REF] Mars | Where we look when we drive with or without active steering wheel control[END_REF]. Based on these perceptual inputs the driver needs to determine quickly which path to take and how to coordinate steering actions to get there. The resulting motor commands set the conditions for new perceptual inputs, and the loop repeats. A consequence of this operational (perceptual-motor) control loop is that vehicle control and gaze tend to be tightly coupled during manual control of driving.

This manuscript focuses on the consequences of automated driving, specifically the impact of automation upon the operational control loop. The operational control loop is the first loop to be disengaged, and is critical for all take-over scenarios since higher level loops can only affect ongoing activity through the pathway provided by perceptual-motor control. This focus is intended to

- M O L W t O L M " A E
-level descriptions for capturing the nature of driving, this manuscript aims to bring greater clarity to the underlying role of perceptual-motor control. This focus necessitates the omission of a number of topics (such as driver distraction) that are important research challenges for control transitions but are outside the scope of the current manuscript (and for which reviews already exist, e.g. Engstrom et al., 2017). By concentrating on perceptual-motor control the manuscript will highlight concepts that may be less familiar to the human factors readership but in our view are no less critical.

Manuscript Overview & Method

Section 2 of this review highlights the literature that underpins our current understanding of the human perceptual-motor loop for the operational control of steering. Section 3 then considers the likely impact of automation on the perceptual-motor control loop, and the possible implications of transitions of control. Section 4 then relates these predictions to current evidence examining human performance when taking over control from AVs. Finally, in Section 5 we look to the future and assess how current technological advances may address some of the issues raised in the previous sections.

This manuscript is not intended to be a systematic review, rather the purpose of the manuscript is to take knowledge from one theoretical domain (the area of human perceptual-motor control) and apply it within the context of a newly emerging, distinct but related field (transitions out of automated driving). This aim has been met through the pursuit and reporting of two distinct literature searches. The first is a structured, narrative review of papers in the domain of perceptualmotor control, selected based on the accumulated expertise of the authors in order to highlight literature that can be best related to transitions of control (Sections 2 and 3). Whilst this review is extensive, there were no strict inclusion criteria. Section 4 presents a semi-structured review of the way that perceptual-motor control is examined within the existing literature on transitions. Google Scholar was used to conduct specific searches on perceptual-motor calibration and gaze and steering coordination (the mechanisms that are the focus of this manuscript) during automated driving (see Table A1 for search terms). Ten relevant papers were found using these searches. Further candidate articles were identified using existing reviews reported in [START_REF] Lu | Human factors of transitions in automated driving: A general framework and literature survey[END_REF]Eriksson &Stanton, 2017a, that were then complemented with additional articles found through ad-hoc searches and citation networks. Whilst there were no strict inclusion criteria, preference was given to papers that could be accessed that were in English, and that: 1) described transitions out of a period of automated driving to human control of driving, 2) considered automation of lateral control, 3) reported empirical objective metrics on driver perceptual-motor control (recorded actions of the driver or the vehicle, rather than subjective report), 4) were published from the year 2010 onwards to ensure relevance to automation of lateral control. In total Section 4 is supported by 53 papers on transitions out of automated driving, which was deemed sufficient for assessing the current transition literature (see Table A2 for the full list of references). Operational Control, involves rapid perceptual-motor control loops that execute the necessary steering commands to keep the vehicle on the selected trajectory. The lowest operational control level is the primary focus of this manuscript.

The perceptual-motor loop (outside of Transitions)

In order to understand how automation could affect the perceptual-motor level in driving transitions it is necessary to establish how the human perceptual-motor systems are normally involved during successful steering control. The following primer is aimed at readers unfamiliar with the existing perceptual-motor control literature, to provide sufficient background to appreciate how the issues raised apply to control transitions out of automated driving (for more in-depth reviews an interested reader is referred to: [START_REF] Land | Looking and acting: Vision and eye movements in natural behaviour[END_REF][START_REF] Lappi | Future path and tangent point models in the visual control of locomotion in curve driving[END_REF][START_REF] Lappi | Visuomotor Control, Eye Movements, and Steering: A Unified Approach for Incorporating Feedback, Feedforward, and Internal Models[END_REF][START_REF] Regan | Visually guided collision avoidance and collision achievement[END_REF]. In particular, we introduce two key concepts in the following subsections: 1) perceptual-motor calibration and 2) gaze and steering coordination. Perceptual-motor calibration (Section 2.1) refers to how individuals maintain appropriately scaled movements in conditions where there are changing task dynamics; whilst this is a common conceptual framework in the motor control literature (see [START_REF] Brand | Recalibration in functional perceptual-motor tasks: A systematic review[END_REF][START_REF] Van Andel | A systematic review on perceptual-motor calibration to changes in action capabilities[END_REF] for recent reviews), the issue is often not explicitly considered in the steering control literature, despite having important implications for control transitions. Gaze and steering coordination (Section 2.2) refers to the way that drivers use head-and eye-movements to anticipate upcoming steering requirements (and how the steering requirements themselves alter gaze patterns), and we (and others) consider this coordination to be central to understanding how humans drive [START_REF] Lappi | Visuomotor Control, Eye Movements, and Steering: A Unified Approach for Incorporating Feedback, Feedforward, and Internal Models[END_REF][START_REF] Land | Where we look when we steer[END_REF]Mars, 2008a;[START_REF] Wilkie | Active gaze, visual look-ahead, and locomotor control[END_REF][START_REF] Wilkie | Using vision to control locomotion: Looking where you want to go[END_REF][START_REF] Lappi | Future path and tangent point models in the visual control of locomotion in curve driving[END_REF][START_REF] Lehtonen | Effect of driving experience on anticipatory look-ahead fixations in real curve driving[END_REF][START_REF] Land | Predictable eye-head coordination during driving[END_REF][START_REF] Land | Steering with the head: The visual strategy of a racing driver[END_REF][START_REF] Wilson | Eye movements coordinated with steering benefit performance even when vision is denied[END_REF][START_REF] Land | The visual control of steering[END_REF]. In Section 3 we will apply these concepts to consider the case of control transitions.

Perceptual-motor calibration

Steering is a specific example of a broader set of actions that rely on visual information to guide movement (e.g. steering has been modelled as a reaching task; [START_REF] Kolekar | Modeling Intradriver Steering Variability Based on Sensorimotor Control Theories[END_REF] and there is also evidence linking steering ability with manually tracing paths; [START_REF] Raw | Movement control in older adults: does old age mean middle of the road?[END_REF]. In the fields of experimental psychology and vision science, such visually guided actions have historically been modelled using mathematically specifiable control laws that translate perceptual cues more or less directly into movement commands, for example braking [START_REF] Lee | A theory of visual control of braking based on information about time-to-collision[END_REF][START_REF] Fajen | Rapid recalibration based on optic flow in visually guided action[END_REF][START_REF] Fajen | Learning novel mappings from optic flow to the control of action[END_REF] or steering [START_REF] Land | Where we look when we steer[END_REF][START_REF] Fajen | Behavioral dynamics of steering, obstacle avoidance, and route selection[END_REF][START_REF] Salvucci | A two-point visual control model of steering[END_REF][START_REF] Wilkie | Driving as night falls: the contribution of retinal flow and visual direction to the control of steering[END_REF]. There are a number of perceptual cues made optically available to a human by their environment (such as optic flow, [START_REF] Gibson | Visually controlled locomotion and visual orientation in animals[END_REF]or optic expansion, Lee, 1976). A driver can learn relationships between available perceptual variables and the control states that produce desired task performance [START_REF] Fajen | Perceiving possibilities for action: On the necessity of calibration and perceptual learning for the visual guidance of action[END_REF]. The learned relationship can be referred to as a perceptual-motor mapping. However, the exact mechanisms of the sensorimotor learning underpinning skilled actions is often unclear. Motor learning is routinely described as a set of internal models that support predictive feedforward control [START_REF] Wolpert | Principles of sensorimotor learning[END_REF]), yet the presence of internal models is also contested in some online control accounts of visually guided action [START_REF] Zhao | On-line and model-based approaches to the visual control of action[END_REF]. Irrespective of the precise mechanisms underlying perceptual-motor mapping and learning, adequate perceptualmotor mappings need to be established, maintained and updated over time and across different conditions (see [START_REF] Lappi | Visuomotor Control, Eye Movements, and Steering: A Unified Approach for Incorporating Feedback, Feedforward, and Internal Models[END_REF], for a more detailed discussion of the role of internal models in steering control).

The mappings from perceptual cues to motor actions will vary depending on environmental conditions, vehicle dynamics and driver experience. For example, if someone tries to drive a new vehicle the steering characteristics (e.g. wheel sensitivity) is likely to differ from their usual experience: given identical perceptual stimuli a different motor response will be needed, and so a new mapping needs to be acquired. If the new vehicle is more responsive (e.g. it has power-assisted steering, whereas the previous vehicle did not) the driver risks excessive initial steering inputs and/or over-correcting for errors because responses will reflect an incorrect mapping. Analogous changes will occur even during a continuous drive of the same vehicle. Steering dynamics will alter across time due to changes in the vehicle (e.g. increased speed, reduced fuel load, wear in tyre tread), the environment (change in surface texture, e.g. gravel vs. Tarmac, different weather conditions), and the driver (e.g. muscle fatigue). During such periods a driver needs to adapt to frequently changing action and remain well-attuned to their environment [START_REF] Fajen | Perceiving possibilities for action: On the necessity of calibration and perceptual learning for the visual guidance of action[END_REF][START_REF] Shadmehr | Error correction, sensory prediction, and adaptation in motor control[END_REF].

We can define, at the broadest level, perceptual-motor calibration as maintaining appropriately scaled movements when conditions change [START_REF] Fajen | Perceiving possibilities for action: On the necessity of calibration and perceptual learning for the visual guidance of action[END_REF]. We will follow recent reviews of how humans scale movements under changing conditions [START_REF] Brand | Recalibration in functional perceptual-motor tasks: A systematic review[END_REF][START_REF] Van Andel | A systematic review on perceptual-motor calibration to changes in action capabilities[END_REF][START_REF] Redding | Applications of prism adaptation: a tutorial in theory and method[END_REF] and refer to this as perceptual-motor calibration, but note that calibration is a type of sensorimotor adaptation (Krakauer & Mazzoni, 2011;[START_REF] Wolpert | Principles of sensorimotor learning[END_REF], so often either term is used to describe changes very similar in nature (e.g. [START_REF] Bourgeois | Effect of visuomotor calibration and uncertainty on the perception of peripersonal space[END_REF][START_REF] Benson | A spatial explicit strategy reduces error but interferes with sensorimotor adaptation[END_REF] 1 .

The process of maintaining perceptual-motor mappings attuned to the environment can be examined at different levels of the sensorimotor system, from neuromuscular changes [START_REF] Franklin | Rapid visuomotor feedback gains are tuned to the task dynamics[END_REF], to scaling movements such as swinging a baseball bat [START_REF] Scott | Switching tools: Perceptual-motor recalibration to weight changes[END_REF], braking [START_REF] Fajen | Rapid recalibration based on optic flow in visually guided action[END_REF] or reaching-to-grasp (Coats, Bingham, & Mon-Williams, 2007), to endpoint accuracy of complex tasks with multiple coordinated sub-movements such as during driving simulator adaptation experiments [START_REF] Mcgehee | Quantitative analysis of steering adaptation on a high performance fixed-base driving simulator[END_REF]Sahami & Sayed, 2011;[START_REF] Ronen | The adaptation period to a driving simulator[END_REF]. Given role of calibration in supporting successful action it is important to consider how AVs might affect the acquisition and maintenance of well calibrated steering responses. We examine the likely impact of automation on calibration in Section 3 once we have completed outlining the fundamentals of operational control loop underpinning steering control behaviours.

Gaze and Steering Coordination

There appears to be a growing consensus within the recent perceptual-motor steering control literature that successful steering naturally relies on close coordination with gaze behaviours [START_REF] Lappi | Visuomotor Control, Eye Movements, and Steering: A Unified Approach for Incorporating Feedback, Feedforward, and Internal Models[END_REF][START_REF] Land | Where we look when we steer[END_REF][START_REF] Wilkie | Active gaze, visual look-ahead, and locomotor control[END_REF][START_REF] Wilkie | Using vision to control locomotion: Looking where you want to go[END_REF][START_REF] Lappi | Future path and tangent point models in the visual control of locomotion in curve driving[END_REF][START_REF] Lehtonen | Effect of driving experience on anticipatory look-ahead fixations in real curve driving[END_REF][START_REF] Land | Predictable eye-head coordination during driving[END_REF][START_REF] Land | Steering with the head: The visual strategy of a racing driver[END_REF][START_REF] Wilson | Eye movements coordinated with steering benefit performance even when vision is denied[END_REF][START_REF] Land | The visual control of steering[END_REF][START_REF] Vansteenkiste | Cycling around a curve: the effect of cycling speed on steering and gaze behavior[END_REF][START_REF] Matthis | Gaze and the control of foot placement when walking in natural terrain[END_REF]Hollands et al., 2002;[START_REF] Land | In what ways do eye movements contribute to everyday activities?[END_REF]Mars, 2008a;[START_REF] Jahn | Eccentric eye and head positions in darkness induce deviation from the intended path[END_REF]. Gaze behaviours that are tightly coupled with vehicle control manoeuvres lead to fairly 1 For readers unfamiliar with the perceptual-motor control literature it is worth being explicit about the relationship between calibration and adaptation (Tresillian, 2012). Adaptation is broader in scope, and refers to adjusting existing skills in new circumstances to maintain levels of performance. Calibration refers to a specific case of adaptation involving adjusting existing/learning new perceptual-motor mappings. All (re)calibration is adaptation, but not all adaptation is calibration, for example one can stiffen muscles to resist uncertain forces without updating perceptual-motor mappings. Both adaptation and calibration are distinct from acquisition, which involves learning a new skill (Tresillian, 2012). All are forms of learning.

stereotypical behaviours (albeit with some inter-individual variation) during routine driving (for illustrative examples see [START_REF] Lappi | Systematic Observation of an Expert Driver's Gaze Strategy An On-Road Case Study[END_REF]. It seems that to steer smoothly drivers usually employ guiding fixations (GF): fixations directed about 1-2s ahead [START_REF] Land | Predictable eye-head coordination during driving[END_REF][START_REF] Land | Where we look when we steer[END_REF][START_REF] Lappi | Beyond the tangent point: gaze targets in naturalistic driving[END_REF][START_REF] Lehtonen | Effect of driving experience on anticipatory look-ahead fixations in real curve driving[END_REF]. However, GFs are sometimes interleaved with rarer fixations even further ahead referred to as Lookahead fixations (LAFs; [START_REF] Lehtonen | Look-ahead fixations in curve driving[END_REF]. This gaze polling behaviour of alternating GF/LAF [START_REF] Wilkie | Active gaze, visual look-ahead, and locomotor control[END_REF] has been observed in both laboratory and real-world tasks [START_REF] Wilkie | Active gaze, visual look-ahead, and locomotor control[END_REF][START_REF] Lehtonen | Effect of driving experience on anticipatory look-ahead fixations in real curve driving[END_REF][START_REF] Lappi | Systematic Observation of an Expert Driver's Gaze Strategy An On-Road Case Study[END_REF].

It has been hypothesised that these two classes of fixation have different functional roles. GFs seem to be useful for path modification when responding to changes determined by the upcoming road curvature, whereas LAFs are more useful for route planning decisions further ahead in both time and space [START_REF] Mars | Where we look when we drive with or without active steering wheel control[END_REF][START_REF] Wilkie | Active gaze, visual look-ahead, and locomotor control[END_REF][START_REF] Lehtonen | Look-ahead fixations in curve driving[END_REF]2014;[START_REF] Mennie | Look-ahead fixations: anticipatory eye movements in natural tasks[END_REF][START_REF] Pelz | Oculomotor behavior and perceptual strategies in complex tasks[END_REF]; see Lappi & Mole, 2018 for a review of the relevant evidence).

During routine steering gaze patterns are very active, with a move-dwell-move pattern occurring 2-3 times per second [START_REF] Wilkie | Using vision to control locomotion: Looking where you want to go[END_REF][START_REF] Lappi | Systematic Observation of an Expert Driver's Gaze Strategy An On-Road Case Study[END_REF][START_REF] Ahissar | Perception as a closed-loop convergence process[END_REF]. Whilst it appears that gaze behaviours by themselves are important, the interplay between steering and gaze control is more nuanced than simply needing to look ahead to observe the scene features relevant for determining the upcoming steering requirements. Gaze patterns change when the need for active vehicular control is removed: even when the viewed scene is identical drivers look further ahead, making more LAFs, than when they are no longer required to steer [START_REF] Mars | Where we look when we drive with or without active steering wheel control[END_REF][START_REF] Mackenzie | Eye movements and hazard perception in active and passive driving[END_REF].

Complementing the visual input, the (forward) orientation of the head and eyes itself provides proprioceptive gaze direction information (from the muscles controlling the head and eyes) that is a useful input for steering control over and above the visual pattern on the retina (Authie et al., 2015;Wilkie & Wann, 2003;[START_REF] Wilson | Eye movements coordinated with steering benefit performance even when vision is denied[END_REF]. If a driver is looking where they wish to go, the direction and magnitude of gaze relative to the current direction of locomotion in part signals the steering required to pass through the point of fixation. Steering can be biased by preventing normal eye movements [START_REF] Robertshaw | Does gaze influence steering around a bend?[END_REF] or requiring drivers to fixate a point to the side of the path [START_REF] Kountouriotis | The role of gaze and road edge information during highspeed locomotion[END_REF][START_REF] Readinger | Gaze-eccentricity effects on road position and steering[END_REF][START_REF] Jahn | Eccentric eye and head positions in darkness induce deviation from the intended path[END_REF]Mars 2008a). It seems therefore that active gaze control is also critically important for maintaining locomotor perceptual-motor mappings. These eye and head movements actively shape the samples from the sensory array (including but not limited to the retina) that the brain receives as input [START_REF] Ahissar | Perception as a closed-loop convergence process[END_REF], Therefore, a notable aspect of the coupling between steering and gaze is that it appears to be bidirectional: gaze influences steering, and steering influences gaze.

The evidence highlighted so far in this subsection demonstrates that gaze and steering coordination can be well captured within the framework of a perception-action loop. Neither behaviour in isolation (gaze or steering) wholly determines the other: rather, where a driver will look (and consequently what is sampled retinally and extra-retinally) depends on the current steering intentions, but the current steering is in turn influenced by where the driver is looking. It is worth mentioning that because these perception-action loops are modelled on successful human steering behaviours in laboratory steering tasks, they tend to describe well-calibrated behaviors (see e.g. [START_REF] Wilkie | Active gaze, visual look-ahead, and locomotor control[END_REF]Mars & Chevral, 2017;[START_REF] Salvucci | A two-point visual control model of steering[END_REF][START_REF] Boer | What Preview Elements do Drivers Need[END_REF], however the way that the human has become calibrated, and how calibration can adapt in more dynamically complex and labile environments, tends not to be explicitly addressed.

One cannot have synergy between steering and gaze without appropriately attuned bidirectional perceptual-motor mappings (section 2.1). Section 3.2 examines how gaze and steering coordination might be affected by automated driving and the potential impact on control transitions.

An operational control loop that reflects the perceptual-motor demands of steering

The operational control level outlined in Figure 1 can be expanded to highlight the components of perceptual-motor steering control that are most relevant to control transitions (Figure 2). Successful steering is here depicted as relying on a frequently updated perceptual-motor loop, where the perceptual inputs used to inform steering are supplied by Gaze and Steering control acting synergistically. The role of Calibration is included as a critical property of this loop, attuning gaze and steering control outputs to the current vehicle and environmental conditions.

When attempting to capture the nature of complex human behaviours (such as driving) it is essential to be explicit about the level of description being used. This is because these phenomenon are partly determined by the character of the sub-components of the perceptual-motor loop: perceptual variables, steering control, and gaze control. There is considerable ongoing research that is trying to improve our understanding of these components, and there are a number of steering models proposing different candidate perceptual variables and alternative mechanisms for how candidate variables translate to vehicle control (e.g. [START_REF] Salvucci | A two-point visual control model of steering[END_REF][START_REF] Wilkie | Active gaze, visual look-ahead, and locomotor control[END_REF]; also see Lappi, 2014 for a review). However, the level of description used in Figure 2 is sufficient for assessing the impact of these components upon smooth and safe control transitions. Control (determining how rapidly, and by how much, to turn the steering wheel to produce the desired wheel angle, [START_REF] Benderius | Evidence for a fundamental property of steering[END_REF] and Gaze Control (e.g. saccading to, then tracking, a waypoint on the future path then tracking this waypoint, [START_REF] Wann | Steering with or without the flow: is the retrieval of heading necessary[END_REF][START_REF] Wilkie | Driving as night falls: the contribution of retinal flow and visual direction to the control of steering[END_REF]. Mappings are frequently updated so the driver remains well-calibrated to the environment. Note the bidirectional information flow between Gaze Control and Steering Control.

Steering and gaze outputs, mediated through vehicle dynamics, produce a change in the world. The large black arrow represents the general direction of information flow in the action and perception loop, though not all perceptual information has to be mediated by the vehicle and the environment: perceptual variables can be obtained directly from driver outputs (e.g. predictions of steering commands, Markkula et al., 2017) One characteristic of the loop shown in Figure 2 is that for steering actions to remain well-calibrated with respect to the environment, it will need to operate at a sufficiently high frequency. In the realworld, drivers are able to look away from the road (or have the scene occluded) for around 1-2s intermittently without a major impact upon performance [START_REF] Senders | The attentional demand of automobile driving[END_REF][START_REF] Horrey | In-vehicle glance duration: distributions, tails, and model of crash risk[END_REF][START_REF] Pekkanen | Task-difficulty homeostasis in car following models: experimental validation using self-paced visual occlusion[END_REF], 2018 E will be intermittency in the gaze input due to gaze polling behaviours [START_REF] Lappi | Systematic Observation of an Expert Driver's Gaze Strategy An On-Road Case Study[END_REF]. Also, recent modelling advances suggest that intermittent control could be a fundamental property of steering (Markkula et al., 2017).

Nevertheless, while the perceptual-motor loop can handle intermittent and irregular inputs, the manageable time-scales of such interruptions appear to be in the order of seconds rather than minutes [START_REF] Johns | Measurement and mathematical model of a driver's intermittent compensatory steering control[END_REF][START_REF] Pekkanen | Task-difficulty homeostasis in car following models: experimental validation using self-paced visual occlusion[END_REF]. During periods of automated driving the perceptual-motor loop is likely to be interrupted for considerably longer timescales, in the order of many tens of minutes, up to a number of hours. The next section will explore how the perceptualmotor control loop components identified so far may be disrupted during automated driving that takes place for prolonged periods.

Automated driving will break the perceptual-motor control loop

During automated driving the perceptual-motor loop depicted in Figure 2 will be disrupted.

Specifically, the requirement for the human driver to produce Steering Control commands is removed, since the AV now has control over steering (Figure 3). This change effectively breaks the perceptual-motor loop, which may have an impact on perceptual-motor calibration, and also have consequences for the other behaviours normally exhibited during driving (e.g. eye-movement patterns). Without a perception-action loop it is also unlikely that the driver will remain well-calibrated to the environment.

How will AVs impact upon perceptual-motor calibration?

During manual control of steering a driver remains calibrated to the vehicle dynamics and environmental conditions despite frequent changes to the mapping between motor action and resultant vehicle motion (see section 2.1). Most (if not all) current steering models implicitly assume that the driver is well-calibrated (e.g. [START_REF] Wilkie | Active gaze, visual look-ahead, and locomotor control[END_REF][START_REF] Salvucci | A two-point visual control model of steering[END_REF][START_REF] Boer | What Preview Elements do Drivers Need[END_REF][START_REF] Mars | Modelling human control of steering for the design of advanced driver assistance systems[END_REF]. However, if a driver relinquishes control to an automated vehicle and then later takes back control (after some unspecified duration) some degree of miscalibration should be expected. This situation is analogous to classic recalibration paradigms (see [START_REF] Brand | Recalibration in functional perceptual-motor tasks: A systematic review[END_REF][START_REF] Brand | Recalibration in functional perceptual-motor tasks: A systematic review[END_REF][START_REF] Redding | Applications of prism adaptation: a tutorial in theory and method[END_REF] for detailed reviews) where an individual is initially calibrated to baseline conditions (in our case, during a period of manual driving), with a subsequent disturbance whereby perceptual-motor mappings are altered (the automated driving period), followed by a rearrangement period (in the terminology used by [START_REF] Brand | Recalibration in functional perceptual-motor tasks: A systematic review[END_REF] where perceptualmotor mappings are adjusted and reacquired (control transitions from automation back into manual control).

In this context the relevant question to ask about perceptual-motor calibration is how long the system remains well-calibrated once the driver is no longer in active control of the vehicle? There is evidence that when perceptual feedback is removed the sensorimotor system quickly becomes inaccurate: for example proprioception accuracy has been shown to deteriorate within one minute without visual feedback [START_REF] Wann | Does limb proprioception drift?[END_REF], and the movements of drivers when visual and kinaesthetic motion feedback is denied in a simulator can become inaccurate within a few seconds [START_REF] Wallis | An unexpected role for visual feedback in vehicle steering control[END_REF][START_REF] Wallis | The role of visual and nonvisual feedback in a vehicle steering task[END_REF]. These studies clearly demonstrate that perceptual-motor calibration can deteriorate rapidly without feedback, but a complete absence of feedback is the limit case and it is unlikely to occur during automated driving. The driver will continue to receive positional feedback (e.g. from having the hands on the wheel) as well as visual feedback (when looking at the road ahead). However, even though such feedback may be available, the signals could be considered diminished compared to what is available when driving manually: drivers seem to be less likely to look at the road ahead during automated driving (see Section 3.2), and in L3/L4 automation they will not necessarily have their hands on the wheel (thus eliminating haptic/proprioceptive signals from their hands/arms).

Even if the perceptual-motor system is able to use some of the available information to prevent calibration drift (the decay/misalignment of perceptual-motor mappings), it is likely that during longer automation periods the environmental conditions will have altered (e.g. changes to the grip of road surface due to rain). In such cases the driver is likely to need an acclimatisation period to ensure recalibration. Research investigating recalibration tends to examine discrete movements over short trials, for example swinging a baseball bat that has increased mass [START_REF] Scott | Switching tools: Perceptual-motor recalibration to weight changes[END_REF] or decelerating a vehicle using a stronger brake [START_REF] Fajen | Rapid recalibration based on optic flow in visually guided action[END_REF]. Such studies show that participants are able to recalibrate fairly quickly (in the order of around 10 trials), even when feedback is restricted to 1s per trial [START_REF] Fajen | Rapid recalibration based on optic flow in visually guided action[END_REF]. However, these are repetitions of specific movements in controlled environments with consistent feedback conditions that are likely to be favourable to rapid recalibration [START_REF] Huang | Rethinking motor learning and savings in adaptation paradigms: model-free memory for successful actions combines with internal models[END_REF]Castro et al., 2014). In the real-world drivers do not have the luxury of repeating a movement until it is optimal, rather they execute steering corrections of different magnitude in response to an ever-changing environment, and so rearrangement of steering may take much longer. Deborne et al. (2012) showed that drivers on a simulated circular track could recalibrate (stabilise steering wheel angle) in response to a sudden increase in steering wheel selfaligning torque within a few seconds. However, the drivers in Deborne et al. ( 2012) were in active control the entire time and there is evidence that drivers adapt to changes in steering torque particularly quickly [START_REF] Toffin | Role of steering wheel feedback on driver performance: driving simulator and modeling analysis[END_REF][START_REF] Russell | Motor learning affects car-to-driver handover in automated vehicles[END_REF]. So, whilst this evidence supports the idea that drivers can rapidly recalibrate to some changing conditions during active control, it may not be directly applicable to control transitions where drivers are taking over vehicle control after a period of disengagement when there may have been multiple changes to both the vehicle dynamics and environmental conditions.

An indirect way to examine the issue of a change in vehicle and environment is by measuring the behaviour of drivers when they first use a driving simulator. Simulators differ from real-world driving in terms of vehicle dynamics and the available perceptual information (e.g. a fixed-base simulator will not provide vestibular feedback), so drivers usually need to have a period of time over which they adjust their perceptual-motor mappings. Simulator studies report that driver performance can M G report a stabilisation time of 4 minutes (for wheel reversals), whereas [START_REF] Ronen | The adaptation period to a driving simulator[END_REF] report longer timescales that vary from 6.5-15 minutes depending on road-type. Sahami & Sayed (2011) report considerable inter-individual variation in rate of stabilisation, averaging ~7.5 minutes. It is reasonable to expect that the recalibration period during real-world control transitions would be shorter than these estimates because changes within the same vehicle are likely to be relatively small compared to the difference between a real vehicle and first-time use of a simulator (presuming the conditions during take-over are close to the pre-existing perceptual-motor mappings). To the best of our knowledge, there is only one study to date which has looked in detail at motor recalibration in real cars during transitions out of automation. [START_REF] Russell | Motor learning affects car-to-driver handover in automated vehicles[END_REF] used a paradigm similar to classic motor control studies, where participants first experienced multiple handovers of lateral control with one set of vehicle dynamics, then vehicle dynamics were altered (the steering was made more sensitive, or the steering wheel self-aligning torque increased). An increase in steering sensitivity initially led to jerkier steering as drivers overshot the required wheel angle.

Within 10 trials (more than 1 minute) on a controlled lane change task (each trial contained approximately 15s of manual lateral control) drivers were able to bring steering back to levels of smoothness comparable to the baseline trials. The results of [START_REF] Russell | Motor learning affects car-to-driver handover in automated vehicles[END_REF] establish the existence, at least for some aspects of vehicle control (there was little effect of changing steering torque), that there is a critical period after take-over where the driver may be miscalibrated if conditions have changed from when they were last in manual control. The time needed to recalibrate in [START_REF] Russell | Motor learning affects car-to-driver handover in automated vehicles[END_REF] is considerably quicker than the 4-15 minute timescales reported in the simulator studies discussed previously (though it is possible that the repeated controlled conditions favours rapid recalibration). Yet a timescale of around 1 minute is still considerably slower than the ~10s exposure time extrapolated from the perceptual-motor literature. It seems reaction times; see Section 4.1 for further evaluation), they may be prone to making miscalibrated steering responses during early phases of the transition (such as rapid evasive manoeuvres; [START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF][START_REF] Russell | Motor learning affects car-to-driver handover in automated vehicles[END_REF].

It is critical to improve our understanding of which behaviours could be supported during automated driving to minimise the decay of perceptual-motor mappings and reduce the time required to recalibrate. Unfortunately, the task demands placed on the human operator during automated driving is really at odds with the indications of the perceptual-motor control literature, which suggests that a crucial requirement of successful calibration is active motor control (often termed

B O F P B
treadmill runners recalibrate their relationship between locomotor speed and optic flow, so that after treadmill running there is a period of illusory accelerated self-motion that causes participants to walk more slowly than usual, however, the effect of recalibration was eliminated if participants were pushed on a wheelchair (so they had no active control, [START_REF] Pelah | Visual illusion from running[END_REF]. Furthermore, research suggests that the state of calibration is often unavailable for self-report, with participants able to recalibrate to a change in brake strength despite being unable to accurately detect the brake strength change [START_REF] Fajen | Rapid recalibration based on optic flow in visually guided action[END_REF]. Or, in contrast, participants are made aware of the manipulation (and were instructed to ignore it) but nevertheless were unable to resist recalibration (Mon-Williams & Bingham, 2007, Exp. 4; see also [START_REF] Benson | A spatial explicit strategy reduces error but interferes with sensorimotor adaptation[END_REF]. This evidence suggests that simply informing drivers of the need to recalibrate may not be sufficient (although it could help drivers reduce error if there are explicit strategies available, [START_REF] Benson | A spatial explicit strategy reduces error but interferes with sensorimotor adaptation[END_REF][START_REF] Taylor | Explicit and implicit contributions to learning in a sensorimotor adaptation task[END_REF]. This point would seem to be supported by the evidence of [START_REF] Russell | Motor learning affects car-to-driver handover in automated vehicles[END_REF], who informed participants about an upcoming change in steering but drivers nevertheless required more than 1 minute to recalibrate.

T driver is able to subjectively report changes in conditions during the automated drive yet their perceptual-motor calibration does not shift appropriately.

On the basis of the research reviewed in this section we identify three key open research questions pertinent to the design of safe AV systems: RQ1) How long does a wellmapping persist without active steering control? RQ2) What factors determine how quickly a driver can recalibrate to new conditions after a control transition? RQ3) How can we help drivers remain well-calibrated during automated driving?

How will AVs affect Gaze and Steering Coordination?

In Section 2.2 we introduced the extensive literature suggesting that the coordination of gaze and steering is fundamental to effective steering behaviour. This point is reinforced by real-world studies of the manual control of driving demonstrating that even coarse indicators of gaze behaviour (on-or off-road glances) can be reliable indicators of collision risk (Victor et al., 2015;Seppelt et al., 2017). It seems plausible, therefore, that steering actions during control transitions will be influenced by where drivers look, both in the seconds before take-over (which will affect the nature of the available perceptual information) and during the initial period of the control transition.

It is clear from existing empirical data that gaze behaviours during automated driving should be expected to be markedly different than during manual driving. Gaze patterns are characteristically less concentrated during automation than during manual driving: eye metric positions are more variable [START_REF] Mackenzie | Eye movements and hazard perception in active and passive driving[END_REF]Dambock et al., 2013;Shen & Neyans, 2017), gaze dispersion metrics are higher [START_REF] Louw | Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation[END_REF], and less cumulative time is spent looking towards the road ahead [START_REF] Eriksson | [END_REF][START_REF] Louw | Driver inattention during vehicle automation: how does driver engagement affect resumption of control?[END_REF][START_REF] Louw | Were they in the loop during automated driving ? Links between visual attention[END_REF][START_REF] Carsten | Control task substitution in semiautomated driving: Does it matter Accidents and Reaction Times[END_REF][START_REF] Jamson | Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions[END_REF].

Louw et al (2016) linked changes in gaze behaviour during automation to detrimental road safety outcomes, showing that drivers who looked the least often to the road ahead were most likely to crash (i.e. did not execute an evasive manoeuvre quickly enough; see also [START_REF] Zeeb | What determines the take-over time? An integrated model approach of driver take-over after automated driving[END_REF]. It may seem obvious that drivers who do not look at what is in front of them are unable to respond to events that they did not see. But the erratic patterns of sampling often observed during automation [START_REF] Louw | Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation[END_REF] may in and of itself affect steering control by disrupting the coordination of gaze and steering (independent of the opportunity to sample useful visual information). Looking away from the direction of travel for long periods may lead to subsequent steering control being biased by where the driver was previously looking (e.g. steering response are coupled to gaze direction with a 1-2s lag; [START_REF] Land | Where we look when we steer[END_REF]Wilkie & Wann, 2003; see Section 2.2). Alternatively, if drivers have decoupled gaze from steering during automation (so gaze direction is no longer informing steering) then recoupling will need to take place after take-over: this could lead to a period whereby gaze direction does not appropriately inform steering. Evidence during manual steering suggests that drivers can find it difficult to decouple gaze and steering, and may unintentionally steer where they look [START_REF] Robertshaw | Does gaze influence steering around a bend?[END_REF][START_REF] Kountouriotis | The role of gaze and road edge information during highspeed locomotion[END_REF]. It is currently unknown whether gaze direction information picked-up during automation will to manual steering after a handover, but if carry-over is possible then there hypothetically exists a critical period immediately after handover where a driver could be influenced by where they had previously been looking (during automation), not where they are currently looking (during manual control). Whether carry-over exists, and the timescales of any effect, will have important implications for where a driver should be looking before a transition and so these factors need to be empirically tested.

Whilst ensuring the driver is -and looking to the road ahead should aid steering during transitions, it may also be the case that the gaze patterns produced during automation differ compared to manual driving [START_REF] Mars | Where we look when we drive with or without active steering wheel control[END_REF][START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF]. The bidirectionality of gaze and steering coordination (Section 2.2) suggests that executing steering commands provides a driver with valuable information (e.g. efference copy, [START_REF] Franklin | Computational mechanisms of sensorimotor control[END_REF] of the likely consequences of current steering actions [START_REF] Mars | Where we look when we drive with or without active steering wheel control[END_REF][START_REF] Nash | A review of human sensory dynamics for application to models of driver steering and speed control[END_REF][START_REF] Blaauw | Optimal control model applications and field measurements with respect to car driving[END_REF]Markkula et al., 2017), informing the driver of where they need to look in order to obtain perceptual inputs for the next motor command controlling steering. In contrast during periods of automation, the driver has to decide where to look based on the control outputs from the AV rather than their own sensorimotor system. [START_REF] Mars | Where we look when we drive with or without active steering wheel control[END_REF] showed that during automated driving gaze patterns changed compared to when the driver was in full manual control (guiding fixations were executed less often, and look-ahead fixations more often). [START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF] demonstrated that these differences are amplified in critical scenarios: during obstacle avoidance drivers spent less time sampling the region where GFs normally occur (compared to manual driving). [START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF] interpret this result as a reorganisation of the requirements for perceptual inputs since drivers are not actively controlling their trajectory (semi-automated control was used where steering was automated but the driver remained in control of speed) they made fewer GFs, and prioritised perceptual inputs from further ahead in the scene. Interestingly, in [START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF] the manual drivers evaded obstacles less aggressively (had lower steering amplitudes and accelerations) than recently transitioned drivers, despite both groups looking to the obstacle at similar times (so had a similar preview time in which to prepare the evasive manoeuvre). Unstable steering post-transition may be directly linked to gaze reorganisation that takes place during automation: drivers make fewer GFs so do not have available the necessary perceptual signals for smooth steering, and it takes time to re-establish successful coordination. However, it could be the case that even with optimal gaze sampling patterns unstable steering occurs due to a decay in perceptual-motor mappings (resulting in poor calibration; section 3.1). When a driver is poorly calibrated erratic steering is expected because there will be a mismatch between intended and executed actions (causing positional error that needs corrected for). Of course, it is also plausible that the unstable steering in [START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF] could be due to a combination of gaze and steering coordination and calibration mechanisms: the reorganisation of gaze may remove perceptual inputs that would be available in manual driving, leading to a decay of perceptual-motor mappings that in turn cause unstable steering. At present, the relative impact of gaze and steering coordination and calibration on transitions are unknown, but this understanding will be invaluable for the design safe AV systems.

In this section we have identified three more key open research questions pertinent to the design of safe AV systems: RQ4) How does gaze behaviour change during automation? RQ5) Do changes in gaze during automation affect steering control upon take-over? And if so, by what mechanism? RQ6)

How can we help drivers to maintain gaze patterns during automated driving that facilitate timely and well-calibrated re-engagement?

Current evidence: human responses during Transitions

In this section we assess the extent to which the AV literature maps onto the perceptual-motor control loop as outlined in the previous sections and the operational level of Figure 1. In particular we review empirical studies that examine transitions out of automated steering (for complementary reviews, see De Winter et al., 2014;[START_REF] Lu | Human factors of transitions in automated driving: A general framework and literature survey[END_REF], and focus on whether the research addresses concepts related to gaze and steering coordination and perceptual-motor calibration (see Table A2 for the references supporting this section, along with how they relate to the perceptual-motor behaviours discussed in this manuscript). Amongst the papers examining transitions, the perceptualmotor loop has been examined using a variety of measures, ranging from coarse yet critical realworld outcomes such as crashes (Dixit et al., 2016;[START_REF] Louw | Were they in the loop during automated driving ? Links between visual attention[END_REF][START_REF] Strand | Semi-automated versus highly automated driving in critical situations caused by automation failures[END_REF]van den Beukel & van der Voort, 2017;Van Den Beukel & Van Der Voort, 2013;[START_REF] Louw | Vehicle Variability[END_REF] through to detailed measures of changes in gaze and/or steering behaviours [START_REF] Dinparastdjadid | Smooth Driver action: Steering Wheel Variability (SD of velocity)[END_REF][START_REF] Naujoks | Driving performance at lateral system limits during partially automated driving[END_REF][START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF][START_REF] Petermeijer | Comparing spatially static and dynamic vibrotactile take-over requests in the driver seat[END_REF]Vogelpohl et al., 2018). By far the most commonly discussed measure of transition performance, however, attempts to balance sensitivity with applicability to real-world scenarios. The reaction time (RT; how quickly an individual takes control of their vehicle) has been used for assigning AV safety boundaries, which has led to RTs becoming the predominant

L G E " )
2018 for a meta-T 'T this section, followed by an evaluation of the additional measures used in this literature.

Reaction times when responding during transitions

RTs are typically recorded from the start of a take-over request [START_REF] Dogan | Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement[END_REF][START_REF] Eriksson | Transition to Manual: comparing simulator with on-road control transitions[END_REF]Eriksson & Stanton, 2017a;[START_REF] Feldhütter | How the Duration of Automated Driving Influences Take-Over Performance and Gaze Behavior[END_REF]Gold et al., 2013;Körber, Gold, Lechner, & Bengler, 2016;Korber et al., 2015;[START_REF] Lorenz | Designing take over scenarios for automated driving : How does augmented reality support the driver to get back into the loop[END_REF][START_REF] Naujoks | Driving performance at lateral system limits during partially automated driving[END_REF][START_REF] Naujoks | Controllability of Partially Automated Driving functions Does it matter whether drivers are allowed to take their hands off the steering wheel?[END_REF][START_REF] Naujoks | The effect of urgency of take-over requests during highly automated driving under distraction conditions[END_REF][START_REF] Payre | Impact of training and in-vehicle task performance on manual control recovery in an automated car[END_REF][START_REF] Politis | Using Multimodal Displays to Signify Critical Handovers of Control to Distracted Autonomous Car Drivers[END_REF][START_REF] Radlmayr | How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in Highly Automated Driving[END_REF][START_REF] Van Der Meulen | Switching Back to Manual Driving: How Does it Compare to Simply Driving Away After Parking[END_REF][START_REF] Zeeb | What determines the take-over time? An integrated model approach of driver take-over after automated driving[END_REF]Petermeijer et al., 2017a;Vogelpohl et al., 2018;[START_REF] Melcher | Take-over requests for automated driving[END_REF][START_REF] Blommer | Driver brake vs. steer response to sudden forward collision scenario in manual and automated driving modes[END_REF][START_REF] Telpaz | Haptic seat for automated driving: preparing the driver to take control effectively[END_REF][START_REF] Louw | Vehicle Variability[END_REF] up to the point when the AV system becomes deactivated by the user (most commonly by the execution of a driving action: movement of the wheel or pedals, or a button press). Of course sometimes a take-over request is not present and so some studies identify RTs as starting from the moment that the event which precipitates the hand-over is initiated (e.g. a parked car becomes visible, or a crosswind begins; [START_REF] Larsson | Learning from experience: Familiarity with ACC and responding to a cutin situation in automated driving[END_REF]Louw et al., 2017;[START_REF] Shen | [END_REF][START_REF] Strand | Semi-automated versus highly automated driving in critical situations caused by automation failures[END_REF][START_REF] Johns | Looking ahead: Anticipatory interfaces for driver-automation collaboration[END_REF]. The assumption underlying the use of RTs is that an early response (shorter RT) will result in safer steering control, and therefore RTs act as a useful proxy measure of steering coordination. This logic aligns with sequentially stepping through the perceptual-motor control loop D box of Figure 3): if a driver is quicker to sample and process perceptual inputs, then they should be faster at selecting an appropriate action, resulting in a motor output that is safe because there is sufficient time to execute it smoothly and accurately [START_REF] Benderius | Evidence for a fundamental property of steering[END_REF]. At the limits of the action time-window this assumption seems uncontroversial (slow RTs could cause braking that occurs so late that a collision is unavoidable) and there are suggestions that later steering responses will be more aggressive and jerky [START_REF] Hoc | Human-machine cooperation in car driving for lateral safety : delegation and mutual control[END_REF][START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF]. Indeed, transition scenarios that elicit earlier responses are associated with less aggressive steering movements, causing smoother and less variable steering trajectories compared to scenarios that elicit delayed responses [START_REF] Louw | Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation[END_REF][START_REF] Petermeijer | Comparing spatially static and dynamic vibrotactile take-over requests in the driver seat[END_REF][START_REF] Politis | Using Multimodal Displays to Signify Critical Handovers of Control to Distracted Autonomous Car Drivers[END_REF][START_REF] Zeeb | Is take-over time all that matters ? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving[END_REF][START_REF] Mok | Emergency, Automation Off: Unstructured Transition Timing for Distracted Drivers of Automated Vehicles[END_REF][START_REF] Madigan | The effect of varying leve[END_REF][START_REF] Kircher | Tactical driving behavior with different levels of automation[END_REF][START_REF] Shen | [END_REF]Louw et al., 2015a).

However, as we shall discuss later there are limits to applying these assumptions to all transition scenarios, particularly when the response time-window is less critical.

As well as using RTs as a measure of steering response, they can also be used to measure gaze response. Rather than using the first steering response as the RT endpoint, the first glance can be used (either looking away from the secondary task or toward a predefined region of interest such as the car windscreen; [START_REF] Feldhütter | How the Duration of Automated Driving Influences Take-Over Performance and Gaze Behavior[END_REF]Gold et al., 2013;[START_REF] Kerschbaum | Highly automated driving with a decoupled steering wheel[END_REF][START_REF] Lorenz | Designing take over scenarios for automated driving : How does augmented reality support the driver to get back into the loop[END_REF][START_REF] Zeeb | What determines the take-over time? An integrated model approach of driver take-over after automated driving[END_REF]2016;2017;Vogelpohl et al., 2018). RTs for eye-movements are in some respects a purer measure of perceptual-motor performance than steering RTs: eye-movements have a very low latency (Leigh & Zee, 2006) and will be near the lower-bound of physiological responses to visual inputs relevant to steering, whereas steering responses may only be executed after the driver has decided which type of action to take (and therefore these measures may sometimes incorporate fairly high-level decision making). However, the relevance of gaze RTs for driving safety depends on whether steering behaviour can be inferred from early or late responses. In the literature (covered in Section 2.2) eye movements lead steering responses by ~1s in highly predictable conditions, such as curve negotiation [START_REF] Land | Steering with the head: The visual strategy of a racing driver[END_REF][START_REF] Land | Where we look when we steer[END_REF][START_REF] Lappi | Beyond the tangent point: gaze targets in naturalistic driving[END_REF][START_REF] Lehtonen | Effect of driving experience on anticipatory look-ahead fixations in real curve driving[END_REF]. In transition experiments the lag between gaze and steering RTs are of a similar order of magnitude, ranging from approximately 1-2.5 s [START_REF] Feldhütter | How the Duration of Automated Driving Influences Take-Over Performance and Gaze Behavior[END_REF]Gold et al., 2013;[START_REF] Kerschbaum | Highly automated driving with a decoupled steering wheel[END_REF][START_REF] Lorenz | Designing take over scenarios for automated driving : How does augmented reality support the driver to get back into the loop[END_REF][START_REF] Zeeb | What determines the take-over time? An integrated model approach of driver take-over after automated driving[END_REF]2016;2017;Vogelpohl et al., 2018). This similarity may suggest that gazesteering RT lag times in transitions capture at least some aspects of the nature of gaze and steering

H 'T

to repeat the same task multiple times in order to derive an estimate of central tendency. This limitation in experimental design can make take-over events much more predictable than would be the case in real driving, possibly leading to artificially low RTs. It has also been well-documented that subtly different gaze locations can lead to very different mechanistic explanations when considering gaze and steering coordination [START_REF] Lappi | Future path and tangent point models in the visual control of locomotion in curve driving[END_REF]. The risk of wrongfully inferring psychological mechanisms (e.g. the driver sampling road information when they may be looking at an object close to the road) increases as the area of interest increases [START_REF] Orquin | Threats to the validity of eye-movement research in psychology[END_REF], especially since the catchment area used for measuring gaze RTs tends to be extremely wide (e.g. the first fixation onto any area of the windscreen; Eriksson et al., 2017;Gold et al., 2013;[START_REF] Kerschbaum | Highly automated driving with a decoupled steering wheel[END_REF][START_REF] Lorenz | Designing take over scenarios for automated driving : How does augmented reality support the driver to get back into the loop[END_REF][START_REF] Zeeb | What determines the take-over time? An integrated model approach of driver take-over after automated driving[END_REF]2016;2017). [START_REF] Louw | Were they in the loop during automated driving ? Links between visual attention[END_REF] used an area of interest approach (by dividing the windscreen into a central 6 degree circle and four surrounded segments)

to examine the fixation placement at 200ms after take-over, and found that only 35-55% (depending on condition) of fixations were directed to the road centre, suggesting that gaze and steering coordination is only partially captured by typical RT measures.

It seems then that whilst RTs are useful for identifying safety boundaries, these sorts of temporal indicators (of gaze or steering) are restricted to time-stamping processes within the perceptualmotor control loop (Figure 2). Gaze-on-Road RT measures provide an estimate of when the process of sampling task-relevant perceptual inputs may have started, and Turn-Initiation RTs give an estimate of when the decision-making process is sufficiently advanced to trigger an initial motor output. However, it seems that there are some conditions (e.g. visual distractions during non-urgent control transitions) that have little influence over the time it takes to return hands to the wheel or eyes to the road, yet they will affect the nature of the steering response [START_REF] Zeeb | Is take-over time all that matters ? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving[END_REF].

Consequently, some authors have taken pains to draw a distinction between the time taken to (smoothness and variability) of steering control [START_REF] Zeeb | Is take-over time all that matters ? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving[END_REF]2017;[START_REF] Louw | Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation[END_REF]Vogelpohl et al., 2018). The next section briefly reviews the current approaches in the transition literature to reporting steering quality.

Beyond reaction times: steering quality during transitions

Transition researchers have adopted a variety of approaches when reporting steering quality (see Table A3). The most frequently used metrics capture behavioural extremes (prevalent examples are maximum lateral error or maximum lateral acceleration), but these necessarily capture only a snapshot or may not have ramifications for driver safety. For example, high lateral acceleration may occur during short and sharp obstacle avoidance manoeuvres that are effective and safe. Some researchers have attempted to assess the quality of steering across whole trajectories by taking (most commonly) the standard deviation of lane position (SDLP).

Variability of vehicle position is often used to infer how smoothly a driver is controlling the vehicle, since greater variability is associated with producing many steering corrections. Supporting the use of this metric, some papers have reported a spike in SDLP in the first few seconds after take-over (presumably when drivers are making the most corrections), corrections which take a further few seconds to dissipate [START_REF] Dogan | Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement[END_REF][START_REF] Naujoks | Driving performance at lateral system limits during partially automated driving[END_REF].

If these sorts of variability measures of vehicle behaviour are considered in isolation it can sometimes be difficult to classify underpins the trajectories taken. There is an implicit assumption that smoother trajectories are synonymous with better performance, however, fewer large steering corrections would usually be classified as smoother steering, but could lead to large errors in lane positioning. It is often more actions on the wheel. For example, [START_REF] Eriksson | Driving performance after self-regulated control transitions in highly automated vehicles[END_REF] found little difference in lane position between manual and automation modes, but found large differences in steering wheel angle variability.

With metrics from both steering actions and vehicle position, one can build a better picture of the tailed plots of both steering wheel angle and vehicle position, see Table A3). For example, [START_REF] Louw | Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation[END_REF] demonstrated that the steering wheel amplitude of an avoidance manoeuvre was related to the criticality of the near-collision situation. Considering both steering actions and vehicle position allows the evaluation of whether unstable steering is related to poor calibration (as in the case of [START_REF] Russell | Motor learning affects car-to-driver handover in automated vehicles[END_REF] or whether it is due to impaired coordination of gaze and steering (which may be the case in many of the papers cited in this review, especially for drivers looking away from the future path during take-over, for example when looking toward a displayed secondary task).

Similarly, jerky steering during the few seconds after take-over can be examined to see whether this results from successful compensatory steering (jerky steering that keeps vehicle position within acceptable limits, Donges, 1978) a behaviour which might be encouraged (e.g. by training the driver to look in the near region ahead of the vehicle). However, if jerky steering is not leading to good road positioning then perhaps different gaze behaviours patterns that enable smoother trajectories should be encouraged. Addressing these issues around perceptual-motor control requires a common framework amongst researchers, so the next section works to situate the existing evidence in the perceptual-motor control framework (Figure 3).

Comparing existing transition evidence to the perceptual-motor control loop

This final section of the review of the transition literature highlights the usefulness of adopting the conceptual framework presented in Figure 3, both with helping to interpret existing transition phenomena, and for the future of autonomous vehicle design.

One situation where the processes underpinning perceptual-motor calibration could offer insight is when RTs apparently fail to capture steering quality (e.g. where RTs are similar across conditions but steering quality differs; Section 4.1). Miscalibration does not necessarily lead to longer RTs [START_REF] Benson | A spatial explicit strategy reduces error but interferes with sensorimotor adaptation[END_REF]. Furthermore, longer RTs might result from explicit corrective strategies that can improve performance by compensating for inaccurate calibration [START_REF] Benson | A spatial explicit strategy reduces error but interferes with sensorimotor adaptation[END_REF][START_REF] Taylor | Explicit and implicit contributions to learning in a sensorimotor adaptation task[END_REF]. Therefore, while similar RTs for different transition conditions indicate that steering movements were initiated equally quickly, it would be wrong to assume that the driver is equally well-calibrated in these cases. Transitions when the driver is poorly calibrated may be identified by examining steering quality in the first few seconds of take-over (e.g. [START_REF] Russell | Motor learning affects car-to-driver handover in automated vehicles[END_REF].

Perceptual-motor calibration may explain differences in steering quality across conditions where changes in perceptual stimuli and/or time spent without active control may have altered/decayed perceptual-motor mappings. However, steering quality can vary even when the perceptual stimuli and time spent without active control have been kept constant (so the mappings have not changed), and only the amount of time between the take-over request and manual control recovery (take-over lead time) has varied. Shorter take-over lead times are more likely to result in variable and unsafe steering behaviours than longer take-over request lead times (e.g. Gold et al., 2013;[START_REF] Louw | Vehicle Variability[END_REF][START_REF] Mok | Emergency, Automation Off: Unstructured Transition Timing for Distracted Drivers of Automated Vehicles[END_REF]. The difference could be partly due to changes in gaze and steering coordination: longer lead times allow the driver to establish useful gaze behaviours (such as tracking a point where the driver wishes to go) that E across take-over request lead times may shed light on the requisite perceptual-motor behaviours for smooth steering after take-over.

Despite the potential issues with interpreting RTs there are some fairly concrete findings emerging from comparing RTs across different scenarios and pooling the results [START_REF] Zhang | Determinants of take-over time from automated driving : A metaanalysis of 93 studies[END_REF]: Transition

RTs seem to increase with traffic density [START_REF] Gold | Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations[END_REF][START_REF] Korber | [END_REF][START_REF] Radlmayr | How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in Highly Automated Driving[END_REF][START_REF] Happee | Take-over performance in evasive manoeuvres[END_REF], or the addition of a secondary task [START_REF] Dogan | Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement[END_REF][START_REF] Payre | Impact of training and in-vehicle task performance on manual control recovery in an automated car[END_REF]Merat et al., 2012;[START_REF] Eriksson | [END_REF], whereas RTs are quicker when the take-over is cued auditorily rather than visually [START_REF] Politis | Using Multimodal Displays to Signify Critical Handovers of Control to Distracted Autonomous Car Drivers[END_REF][START_REF] Walch | Autonomous driving: investigating the feasibility of car-driver handover assistance[END_REF][START_REF] Naujoks | The effect of urgency of take-over requests during highly automated driving under distraction conditions[END_REF] or when drivers have prior experience taking control from automated systems [START_REF] Zeeb | Is take-over time all that matters ? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving[END_REF][START_REF] Payre | Fully automated driving: Impact of trust and practice on manual control recovery[END_REF][START_REF] Happee | Take-over performance in evasive manoeuvres[END_REF][START_REF] Larsson | Learning from experience: Familiarity with ACC and responding to a cutin situation in automated driving[END_REF]Hergeth, Lorenz, & Krems, 2017). When combined, these findings begin to illustrate key situations where take-over may be unsafe (e.g. a novice user of automation that is distracted in heavy traffic). This being the case one might wonder why it is necessary to look beyond RTs and consider the detailed issues highlighted in Sections 2 & 3? We would contend that a theoretical understanding of the mechanisms underlying driver behaviour during transitions will be invaluable. It is tempting to believe that enough experiments recording RTs from a multitude of different scenarios would be sufficient to capture human responses during transitions with a degree of precision sufficient to inform safe AV design.

However the danger is that ignoring the issues identified in this manuscript will only ever lead to a partial understanding, and the measured disruption will be qualitatively different from the actual changes in steering control. New types of AV-transition scenarios are bound to occur and for systems to be safe we need to be confident in predicting likely steering behaviours in conditions beyond those currently studied. It is simply impossible to test all possible scenarios, however a deeper understanding should lead to viable computational models (e.g. Markkula et al., 2017) that allow virtual testing across a much wider range of scenarios, extrapolating our understanding of these cases to find those where there is most risk. Ultimately of course we need to identify effective solutions that aid drivers re-engaging with their vehicles, with the aim being to identify what information the driver needs in order to re-engage the perceptual-motor loop most effectively. The concepts of calibration and gaze and steering coordination will help to provide an operational (and drive. As shall be discussed in the next section there will be benefits to having this fundamental understanding of the underpinning processes in order to push forward research applied to the field of transitions.

Conclusions & Future Directions

This manuscript has outlined two key perceptual-motor processes: perceptual-motor calibration and gaze and steering coordination. These processes may be disrupted during automation with potentially detrimental effects on steering capability upon take-over, but they have hitherto received only a small amount of attention in current transition research (see [START_REF] Russell | Motor learning affects car-to-driver handover in automated vehicles[END_REF] for some promising advances in this area). For each process (see sections 3.1 & 3.2) three concrete research questions have been highlighted that are important for the design of safe AVs. These research questions map onto three categories: 1) Breaking, 2) Re-engaging, or 3) Maintaining the operational control loop (see Table 1). The answers to the research questions arranged in Table 1 will ultimately determine the practical significance of the perceptual-motor control issues raised in this manuscript. The next step will be to understand how the uncovered perceptual-motor issues connect with existing theoretical frameworks as currently used by those investigating automated driving. Recently, [START_REF] Merat | Steering Wheel signal plots over time (e.g. SWA measures) DinparastDjadid et al[END_REF] have usefully defined driver engagement using three distinct stages: In-the-Loop, where a driver is in physical control of the vehicle and monitoring their environment; On-the-Loop, where a driver is not in physical control but is monitoring their environment; Out-of-the-Loop, where the driver is not monitoring the environment (regardless of whether they are in physical control). These definitions are complementary yet distinct from the perceptual-motor control issues raised in this manuscript.

M

However, judicious applications of the definitions proposed by [START_REF] Merat | Steering Wheel signal plots over time (e.g. SWA measures) DinparastDjadid et al[END_REF] requires a precise description of what processes and actually consist of. We contend that such a description needs to start at the perceptual-motor level (Figure 2 & 3): perceptual-motor control is central to any transition scenario, and the framework presented here (Figures 2 &3) provides researchers with a common starting point for interpreting steering behaviour after takeover. Experiments addressing RQs 1-6 (Table 1) could lead to operational definitions for concepts physical control monitoring being safely In-the-Loop (Figure 2).

One of the benefits of improving our understanding of the perceptual-motor processes supporting steering control is the facilitation of technological solutions to support automated driving. It is currently difficult to propose concrete practical solutions to the issues raised in this manuscript.

However, we see clear opportunities for the use of technological advances to address RQ3 and RQ6 once answers to RQs 1, 2, 4 & 5 (Table 1) have been resolved. Two main obstacles for maintaining good calibration during transitions of control (Section 3.1) are the potentially rapid decay of perceptual-motor mappings (RQ1), and the need for active control in order to recalibrate (RQ2).

These obstacles arise from transitions between 100% AV control to 100% human control, but there are intermediate AV sys comprehensive review, see [START_REF] Abbink | A Topology of Shared Control Systems Finding Common Ground in Diversity[END_REF]. With these systems the AV and human jointly control the vehicle, with the steering commands of the human being mediated by the AV system [START_REF] Abbink | The importance of including knowledge of neuromuscular behaviour in haptic shared control[END_REF][START_REF] Abbink | The importance of including knowledge of neuromuscular behaviour in haptic shared control[END_REF]. The benefit of such systems is that they are able to provide " prevent decay of perceptual-motor mappings during automated driving [START_REF] Mars | Analysis of human-machine cooperation when driving with different degrees of haptic shared control[END_REF][START_REF] Mars | Driver adaptation to haptic shared control of the steering wheel[END_REF] and/or reduce the time needed for recalibration during progressive transitions of control (Guo et al., in press).

In Section 3.2 we examined the potential issues with altered gaze behaviour during automation (RQ4) and considered the effects of disrupting coordination on manual control (RQ5). Head-up displays (HUD) offer the possibility of superimposing visual information over the visual scene (using useful and appropriate gaze behaviours to assist coordination before and during a transition. In the literature HUDs have typically been employed to increase the salience of symbolic information (such as roadside hazards, signs, or other cars; [START_REF] Rusch | Directing driver attention with augmented reality cues[END_REF][START_REF] Eyraud | Allocation of visual attention while driving with simulated augmented reality[END_REF][START_REF] Langlois | Augmented reality versus classical HUD to take over from automated driving: An aid to smooth reactions and to anticipate maneuvers[END_REF] in order to aid decision making, but these systems could be adapted to aid the re-establishment of coupled gaze and steering. For instance, Mars (2008a[START_REF] Mars | Steering and gaze control modifications from directed or spontaneous use of a visual augmentation cue[END_REF]) demonstrated that guiding gaze by means of a virtual target moving ahead (as a function of the changes in road curvature) improved steering stability during manual driving. If gaze patterns during AV control are found to have a major impact on resumption of control (Section 3.2), then the use of a HUD that informs drivers where they need to look based on their current direction of travel, and other environmental conditions, could be a promising avenue for investigation.The benefits these types of technology (shared control systems or HUDs) can only be realised once we have accurate and detailed models of human perceptual-motor control behaviours during (transitions out of) automated driving. Shared control systems will not only need models of human sensorimotor coordination [START_REF] Abbink | Measuring neuromuscular control dynamics during car following with continuous haptic feedback[END_REF][START_REF] Mulder | Design of a haptic gas pedal for active car-following support[END_REF][START_REF] Abbink | The importance of including knowledge of neuromuscular behaviour in haptic shared control[END_REF][START_REF] Mars | Modeling the visual and motor control of steering with an eye to shared-control automation[END_REF][START_REF] Saleh | Human-like cybernetic driver model for lane keeping[END_REF], but also an appreciation of the calibration mechanisms to ensure that appropriate feedback is provided. Similarly, HUDs require a sophisticated mechanistic model of how steering and gaze are coordinated in order to appropriately train gaze.

Our hope is that by addressing the RQs raised in

Gaze Patterns

Dambock et al., 2013;[START_REF] Dogan | Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement[END_REF]Eriksson et al., 2017;[START_REF] Eriksson | [END_REF][START_REF] Gold | Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations[END_REF][START_REF] Kircher | Tactical driving behavior with different levels of automation[END_REF][START_REF] Louw | Driver inattention during vehicle automation: how does driver engagement affect resumption of control?[END_REF][START_REF] Louw | Were they in the loop during automated driving ? Links between visual attention[END_REF][START_REF] Louw | Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation[END_REF][START_REF] Merat | Transition to manual: Driver behaviour when resuming control from a highly automated vehicle[END_REF][START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF][START_REF] Shen | [END_REF][START_REF] Telpaz | Haptic seat for automated driving: preparing the driver to take control effectively[END_REF][START_REF] Payre | Impact of training and in-vehicle task performance on manual control recovery in an automated car[END_REF][START_REF] Petermeijer | Comparing spatially static and dynamic vibrotactile take-over requests in the driver seat[END_REF][START_REF] Van Der Meulen | Switching Back to Manual Driving: How Does it Compare to Simply Driving Away After Parking[END_REF]Zeeb et al., 2015 (n=17) Papers that examine gaze patterns report gaze metrics that confers information beyond timestamping gaze behaviour (such as proportion of fixations within a catchment area, or variability of gaze angles).

Figure 1 .

 1 Figure 1. The principles underlying multi-level driver models. Each level has its own control loop, with specific environment inputs and action outputs. Whilst the terms used in the Figure are taken from Michon, 1985, the principles are similar for all multi-level driving models. The higher level, Strategic Control, is concerned with general plans that require infrequent updating, for example large scale route setting through the environment. The middle level(s), Tactical control, is concerned with the organisation and sequencing of actions that determine the course through the local environment. The lowest level,

  Figure 2 is put forward as a schematic representation, which does not attempt to describe an implemented steering model, nor does it make concrete proposals about the physiological or perceptual-cognitive nature of the key mechanisms underlying calibration and gaze & steering coordination.

Figure 2 .

 2 Figure 2. A schematic of the perceptual-motor control loop supporting efficient and safe driving. Perceptual Variables (examples given: Retinal Flow refers to the continuous transformation of elements on the retina; Gaze Angle refers to proprioceptive information about direction of gaze relative to the locomotor heading; and Force Feedback refers to contact forces on the wheel) are mapped via Calibration (Section 2.1) onto Gaze & Steering coordination (Section 2.2): Steering

Figure 3 .

 3 Figure 3. A schematic of how automated driving may disrupt the perceptual-motor loops underpinning efficient and safe driving. AV control replaces driver actions. The operation control loop is broken: information only flows from perception to action, not from action to perception. Specifically, information flows to and from steering control are removed. The removal of steering control is likely to change gaze behaviour, putting the utility of gaze-mediated perceptual variables in doubt.

Table 1 .

 1 Six key Research Questions emerging from applying perceptual-motor control mechanisms to transitions out of

	Automation driving.
		1. Breaking the	2. Re-engaging the	3. Maintaining the
		operational control	operational control	operational control
		loop	loop	loop
		(effect of automation)	(effect of automation	(assistance)
			on take-over)
	Perceptual-Motor	RQ1. How long does a	RQ2. What factors	RQ3. How can we help
	Calibration	well-calibrated human	determine how quickly	drivers remain well-
			a driver can	calibrated during
		persist without active	recalibrate to new	automated driving?
		control?	conditions after a
			control transition?
	Steering & Gaze	RQ4. How does gaze	RQ5. Do changes in	RQ6. How can we help
	Coordination	behaviour change	gaze during	drivers to maintain
		during automation?	automation affect	gaze patterns during
			steering control upon	automated driving
			take-over? And if so,	that facilitate timely
			by what mechanism?	and well-calibrated re-
				engagement?

  Table1, and considering the perceptual-motor control issues raised in this manuscript alongside existing practices, the field of transition research may come closer to realising the benefits of automated driving technologies and ensure the automotive future is as bright as has been promised. what aspects are automated?.Human factors, 54(5), 747-761. Castro, L. N. G., Hadjiosif, A. M., Hemphill, M. A., & Smith, M. A. (2014). Environmental consistency determines the rate of motor adaptation. CurrentBiology, 24(10), 1050-1061. Chan, C. Y. (2017). Advancements, prospects, and impacts of automated driving systems. International journal of transportation scienceand technology, 6(3), 208-216. Chattington, M., Wilson, M., Ashford, D., & Marple-Horvat, D. E. (2007). Eye-steering coordination in natural driving. Experimental Brain Research, 180(1), 1 14. https://doi.org/10.1007/s00221-006-0839-2 Coats, R., Bingham, G. P., & Mon-Williams, M. (2008). Calibrating grasp size and reach distance: interactions reveal integral organization of reaching-to-grasp movements. Experimental Brain Research, 189(2), 211-220. ITSC 1656 1661. https://doi.org/10.1109/ITSC.2013.6728467 Deborne, R., Gilles, R., & Kemeny, A. (2012). Modeling Driver Adaptation Capabilities in Critical Driving Situations (No. 2012-01-1179). SAE Technical Paper. de Winter, J. C. F., Happee, R., Martens, M. H., & Stanton, N. A. (2014). Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence. Transportation Research Part F: Traffic Psychology and Behaviour, 27(PB), 196 217. https://doi.org/10.1016/j.trf.2014.06.016 DinparastDjadid, A., Lee, J. D., Schwarz, C., Venkatraman, V., Brown, T. L., Gasper, J., & Gunaratne, P. (2017). After the fail: How far will drivers drift after a sudden transition of control. In Proceedings of the Fourth International Symposium on Future Active Safety Technology Towards zero traffic accidents. Injury Prevention, (August), 1 13. https://doi.org/10.1136/injuryprev-2016-042155 events after automated driving. Accident; Analysis and Prevention, 108(February), 9 18. Applied Ergonomics, 59, 302 312. https://doi.org/10.1016/j.apergo.2016.09.005 Van Den Beukel, A. P., & Van Der Voort, M. C. (2013). The influence of time-criticality on Situation Awareness when retrieving human control after automated driving. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, (Itsc), 2000 2005.

	D Intelligent Transportation Systems, Proceedings, D D T W M K K B V V C " N D J P W C J D and crash potential. L T M G B E M ' C O performance in critical V D B A P V M C H framework for early concept assessment. Papers that examine steering ' IEEE Conference on P Koppa, R. (2016). Autonomous Vehicles: Disengagements, M N C D l-motor tems A quality report quantitative measures that either directly or indirectly relate to steering actions are reported (such as standard deviation of lane position, or steering wheel acceleration).

https://doi.org/10.1109/ITSC.2013.6728523

ACKNOWLEDGEMENTS

CDM, OTG, GM and RMW were funded by EPSRC UK grant EP/P017517/1. CDM was supported by travel grants from the Experimental Psychology Society and the University of Leeds Research Mobility Programme to visit FM and OL. FM was funded by Agence Nationale de la Recherche (AUTOCONDUCT project, grant ANR-16-CE22-0007-05). OL was supported by a post doctoral grant from the Finnish Cultural Foundation (00150514), and a researcher mobility period in Leeds for OL was supported by the Academy of Finland (279905).

Key Points

During successful steering control driving is supported by a rapid perception-action loop that regularly updates perceptual-motor mappings to remain well-calibrated to changing conditions, and tightly couples gaze and steering behaviors. This perceptual-motor loop is likely to be disrupted during automated driving if perceptualmotor mappings are allowed to decay and gaze control is no longer coordinated with steering control.

Miscalibration and uncoordinated gaze and steering behaviours are expected to lead to unstable steering control during the initial period of steering after transitions out of automated driving.

Incorporating an understanding of perceptual-motor mechanisms into transition research will lead to an improved ability to address the issues that arise during transitions out of automated driving.

Appendix

Table A1. Search Terms for targeted literature searches on perceptual-motor calibration and steering and gaze coordination, and number of papers included.

Search 1 (Coordination)

(steering) AND (gaze) AND (coordination) AND ("autonomous driving" OR "automated driving" OR "automated vehicles" OR "autonomous vehicles") -robotic -185 Results.

Five papers included.

Search 2 (Calibration)

("motor calibration" OR "motor learning" OR "motor adaptation") AND ("autonomous driving" OR "automated driving" OR "automated vehicles" OR "autonomous vehicles") -robotic -74 Results.

Five papers included.

. Table A2. References supporting the review of the current literature presented in Section 4, classified by the level of detail with which they report on the aspects of perceptual-motor control covered in this manuscript.

Aspect of perceptualmotor behaviour

(53 Total)

Comments

Steering Timings [START_REF] Blommer | Driver brake vs. steer response to sudden forward collision scenario in manual and automated driving modes[END_REF][START_REF] Dogan | Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement[END_REF]Dixit et al., 2016;Eriksson & Stanton, 2017a;[START_REF] Eriksson | Transition to Manual: comparing simulator with on-road control transitions[END_REF]Eriksson et al., 2017;[START_REF] Eriksson | [END_REF]Gold et al., 2013;[START_REF] Gold | [END_REF][START_REF] Happee | Take-over performance in evasive manoeuvres[END_REF][START_REF] Happee | Take-over performance in evasive manoeuvres[END_REF][START_REF] Johns | Looking ahead: Anticipatory interfaces for driver-automation collaboration[END_REF][START_REF] Kerschbaum | Highly automated driving with a decoupled steering wheel[END_REF]Korber et al., 2015;[START_REF] Korber | [END_REF][START_REF] Liu | The Conclusion of a Driving Study About Warnings Depends upon How Response Time is Defined[END_REF][START_REF] Lorenz | Designing take over scenarios for automated driving : How does augmented reality support the driver to get back into the loop[END_REF]Louw et al., 2015a;[START_REF] Louw | Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation[END_REF][START_REF] Melcher | Take-over requests for automated driving[END_REF][START_REF] Naujoks | The effect of urgency of take-over requests during highly automated driving under distraction conditions[END_REF][START_REF] Naujoks | Controllability of Partially Automated Driving functions Does it matter whether drivers are allowed to take their hands off the steering wheel?[END_REF][START_REF] Naujoks | Driving performance at lateral system limits during partially automated driving[END_REF][START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF][START_REF] Payre | Fully automated driving: Impact of trust and practice on manual control recovery[END_REF][START_REF] Payre | Impact of training and in-vehicle task performance on manual control recovery in an automated car[END_REF]Petermeijer et al., 2017a;[START_REF] Politis | Using Multimodal Displays to Signify Critical Handovers of Control to Distracted Autonomous Car Drivers[END_REF][START_REF] Radlmayr | How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in Highly Automated Driving[END_REF][START_REF] Shen | [END_REF][START_REF] Strand | Semi-automated versus highly automated driving in critical situations caused by automation failures[END_REF][START_REF] Telpaz | Haptic seat for automated driving: preparing the driver to take control effectively[END_REF][START_REF] Van Der Meulen | Switching Back to Manual Driving: How Does it Compare to Simply Driving Away After Parking[END_REF]Vogelpohl et al., 2018;[START_REF] Walch | Autonomous driving: investigating the feasibility of car-driver handover assistance[END_REF][START_REF] Louw | Vehicle Variability[END_REF][START_REF] Zeeb | What determines the take-over time? An integrated model approach of driver take-over after automated driving[END_REF][START_REF] Zeeb | Is take-over time all that matters ? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving[END_REF]Zeeb et al., 2017 (n=39) Papers that include a measure that timestamps an aspect of steering behaviour (such has when the hands were returned to the wheel or when the wheel angle exceeded a certain threshold) Gaze Timings Dambock et al., 2013;Eriksson et al., 2017;[START_REF] Eriksson | [END_REF]Gold et al., 2013;[START_REF] Kerschbaum | Highly automated driving with a decoupled steering wheel[END_REF][START_REF] Lorenz | Designing take over scenarios for automated driving : How does augmented reality support the driver to get back into the loop[END_REF][START_REF] Louw | Were they in the loop during automated driving ? Links between visual attention[END_REF][START_REF] Navarro | Obstacle avoidance under automated steering: impact on driving and gaze behaviours[END_REF]Vogelpohl et al., 2018;[START_REF] Zeeb | Is take-over time all that matters ? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving[END_REF]Zeeb et al., 2017 (n=11) Papers that include a measure that timestamps aspect of gaze behaviour

Category Papers

Vehicle Position (e.g. max error, mean lane position) [START_REF] Dinparastdjadid | Smooth Driver action: Steering Wheel Variability (SD of velocity)[END_REF][START_REF] Eriksson | Driving performance after self-regulated control transitions in highly automated vehicles[END_REF], Happee et al., 2017, Johns et al., 2017, Kircher et al., 2014, Madigan et al., 2018, Naujoks et al., 2014, Naujoks et al., 2017, Navarro et al., 2016, Petermeijer et al., 2017a[START_REF] Petermeijer | Comparing spatially static and dynamic vibrotactile take-over requests in the driver seat[END_REF], Politis et al., 2017[START_REF] Shen | [END_REF], Zeeb et al., 2016, Zeeb et al., 2017 (n = 15) Vehicle Acceleration (e.g. max or min lateral acceleration) [START_REF] Eriksson | [END_REF], Gold et al., 2013, Gold et al., 2016, Happee et al., 2017[START_REF] Happee | Take-over performance in evasive manoeuvres[END_REF], Kerschbaum et al., 2014, Kircher et al., 2014[START_REF] Korber | [END_REF], Lorenz et al., 2014, Louw et al., 2015a,