Pierre Roux
email: pierre.roux@cea.fr

Valerian Mannoni
email: valerian.mannoni@cea.fr

Performance evaluation for co-channel coexistence between ITS-G5 and LTE-V2X

Keywords: vehicle communications, coexistence, mobility, performance evaluation

Two technologies are currently competing for providing Vehicle-to-Everything (V2X) communication services in Europe and in the 5.9 GHz ITS band. The oldest one is known as ITS-G5 and specified by ETSI, and the new comer is standardized in 3GPP and derived from LTE technology. It is now conjectured that both technologies will coexist in the deployment phase, at least during a transition period. Several coexistence scenarios are currently under study. Some of them are even considering the option to share a 10 MHz radio channel in the 5.9 GHz band among both technologies under the same geographical area. This approach is called co-channel coexistence. New mechanisms are currently explored and assume some modifications of the standards aimed at minimizing the performance losses. This co-channel coexistence scenario requires a careful assessment, and this article proposes the first performance evaluation in this particular context of coexistence between ITS-G5 and LTE-V2X standards. It presents a simulation work that has been carried out with the purpose of assessing performance of mixed deployments, and also assessing the efficiency of one possible coexistence mechanism. It is based on system level simulation which is itself relying on physical layer performance assessment pertaining to both technologies. The simulation methodology is presented and evaluation results are provided and commented.

I. INTRODUCTION

Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I), commonly referred to as Vehicle to Everything (V2X), are wireless systems currently under development to address several Intelligent Transport Systems (ITS) standardization and regulatory initiatives worldwide. Two standards are emerging in Europe as candidates for V2X operation, namely ITS-G5 and LTE-V2X.

ITS-G5 has been developed by the European Telecommunications Standards Institute (ETSI) and relies on the IEEE 802.11p standard for vehicular networks [START_REF]IEEE Standard for Information technology-Telecommunications and information exchange between systems[END_REF].

On the other hand, LTE cellular systems are evolving towards vehicular communications applications. 3GPP Release 14 has brought two new modes at the so-called PC5 interface that describes V2X communications: modes 3 and 4. In this paper, we focus on PC5 mode 4, which is purely based on sidelink autonomous and distributed resource selection algorithm, and does not require support or control from a cellular eNodeB.

Both technologies are now promoted and competing for the same ITS band, located at 5.9 GHz [START_REF]the harmonised use of radio spectrum in the 5 875-5 905 MHz frequency band for safety-related applications of Intelligent Transport Systems[END_REF]. With strong supporting stakeholders for both standards, it is unlikely that a single wining technology will emerge soon. It is likely on the contrary that both technologies will coexist in the 5.9 GHz band in some geographical areas. It is even possible that both technologies will share the same 10 MHz radio channel in this band, which is referred to as co-channel coexistence. Of course, the simultaneous usage of the same RF channel by both technologies will result in mutual influence, which might affect the performance of both systems.

Due to the heterogeneous nature of both Physical (PHY) and MAC layers, it is important that the situation of having vehicles equipped with different technologies, operating in a common road infrastructure, is characterized and well understood.

In anticipation of loss of performances due to coexistence, several mechanisms are currently under study [START_REF]technical body: ERM TG37[END_REF] with the aim to reduce such performance losses. We have selected the most relevant of these mechanisms to be addressed in terms of efficiency. This mechanism assumes no modification regarding ITS-G5 vehicles. However, PC5 mode 4 radio systems are modified to send an ITS-G5 preamble and its associated header before transmitting its PC5 mode 4 subframes. With this modification, since ITS-G5 devices perform Clear Channel Assessment (CCA) before transmission, this evolution makes them more sensitive to detect activity from surrounding modified PC5 mode 4 devices.

In this paper, we give the first co-channel coexistence evaluation addressing the issue of simulating coexistence scenarios in a given geographical area, assuming a single 10 MHz channel in the 5.9 GHz ITS band shared between ITS-G5 and PC5 mode 4 vehicles. For simplicity, we only consider a mix of vehicles with half of them using ITS-G5 and the other half using PC5 mode 4. Every vehicle is broadcasting regular Context Awareness Messages (CAM) to be received by neighbor vehicles of the same technology. As an outcome of the simulation, we observe average CAM decoding success ratio among neighbors as a function of the maximum neighboring distance.

This simulating approach is referred to as "system level simulation" [START_REF] Roux | System Level Analysis for ITS-G5 and LTE-V2X Performance Comparison[END_REF] and it is detailed in Section III. However this approach also requires physical layer simulations to be carried out, as described in Section II. The output of physical layer simulations serve as an input to the system level simulation. It is made of a set of physical layer performance curves providing decoding success ratio for different configurations. Section III evaluates the performance of the system level simulation and finally a conclusion is made in Section IV.

II. V2X PHYSICAL LAYER PERFORMANCE ASSESSMENT

To evaluate the performance of the considered co-channel coexistence approach using system level simulations and to compare the resulting performance with the legacy approach, it is first necessary to make valid assumptions on the performance of the V2X physical layer. These are divided into two groups:

• The performances of the physical channels for both standards with the evolution of the Packet Error Rate (PER) as a function of the signal to noise ratio (SNR) for different radio configurations [START_REF] Mannoni | A Comparison of the V2X Communication Systems: ITS-G5 and C-V2X[END_REF].

• The evaluation of the functionalities allowing the implementation of the coexistence method and/or impacting the state machine of the ITS-G5 transceiver. This state machine defines the transmission (Tx) and reception (Rx) periods. Because V2X communication technologies are half duplex, time in Tx or Rx modes has a strong impact on system-level performance, in particular in a coexistence context. The IEEE 802.11p physical layer uses Orthogonal Frequency Division Multiplexing (OFDM) combined with convolutional code [START_REF]IEEE Standard for Information technology-Telecommunications and information exchange between systems[END_REF]. To provide performance under rapidly varying channels, time domain parameters have been doubled, while the frequency domain parameters have been halved. A description of the IEEE 802.11p physical layer can be found in [START_REF]IEEE Standard for Information technology-Telecommunications and information exchange between systems[END_REF]. The LTE-V2X physical layer defined in [START_REF]3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and modulation[END_REF] is based on Single Carrier Frequency Division Multiplexing Access (SC-FDMA) and supports 10 or 20 MHz channels. Each channel is divided into sub-frames, Resource Blocks (RBs) and sub-channels. The typical parameters are given in Table I for both technologies.

The results in this section are provided in terms of PER versus the SNR. Throughout the paper, we consider the 3GPP Extended Vehicular A Model EVA channel model as defined in [START_REF]User Equipment (UE) Radio Transmission and Reception." 3rd Generation Partnership Project; Technical Specification Group Radio Access Network[END_REF]. Several configurations are considered for LTE-V2X as given in Table II. It should be noted that in this paper we consider perfect channel estimation for both technologies. Fig. 1 gives the physical layer performance for LTE-V2X and ITS-G5 for the considered configurations (Table II). The throughput ranges from 1.6 Mb/S to 4.8 Mb/S for LTE-V2X depending of the Transport block size and the Modulation Coding Scheme (MCS), and around 5.5 Mb/s for ITS-G5. We can then observe that the SNR for a P ER = 10 -2 varies from 2.8 to 8.1 dB for LTE-V2X and is equal to 10.6 for ITS-G5. That is mainly explained by the difference in data rate but also because channel coding for LTE-V2X is more complex and robust than for ITS-G5.

In a co-channel coexistence scenario, ITS-G5 stations may perceive the last LTE OFDM symbol as free and so transmit a message (only with a high level of priority like Decentralized Environmental Notification Message DENM or high priority DENM) with a possible collision if the next LTE subframe is occupied by at least one LTE user. This phenomenon can be mitigated by inserting of an ITS-G5 header [START_REF]technical body: ERM TG37[END_REF], which indicates to the ITS-G5 radios that the LTE-V2X waveform is lasting 1ms. Therefore, if the IEEE 802.11p signaling field (included in the header) is correctly decoded, ITS-G5 stations would anticipate that the duration of the LTE signal is 1ms, and would therefore refrain from using the channel during that period, even if the last LTE symbol appears empty.

If the ITS-G5 device wants to transmit a message, the state machine of the ITS-G5 transceiver switches to "Tx mode", otherwise it remains in "Rx mode". The device can be locked in the "Rx mode" for a period defined in the PHY header of the ITS-G5 when an ITS-G5 PHY Preamble is detected. This period should be set to 1 ms to avoid a message transmission involving a possible collision with LTE-V2X. The performance of preamble detection and header decoding are then required to complete the system level simulations.

ITS-G5 Header can be inserted in the last symbol of the previous LTE-V2X subframe by the device that sends a new packet in a given subframe. For the reservation of a fixed length time slot (for LTE-V2X e.g. 1 ms subframe) this ITS-G5 header can be "prerecorded" so that no additional computational effort is required. The ITS-G5 header signal can be delivered to the upconverter by the same digital to analog converter as the LTE signal. Figure 2 gives the performance with first the evolution of the detection probability of the preamble as a function of the SNR, together with the evolution of the false alarm (i.e. a detection when no preamble is transmitted). Figure 2 also gives the PER of the ITS-G5 header signaling for the 1 ms locking. Due to the header structure and its integrity check (1 parity bit and 6 bits for the tail biting [START_REF]IEEE Standard for Information technology-Telecommunications and information exchange between systems[END_REF]), a non-negligible header false alarm can also be observed. This false alarm is observed when, after decoding the header, we obtain a header signal that matches with the header structure (i.e. with the parity bit and the 6 bits set to 0 for the tail bits).

These results have a significant impact on the efficiency of the considered coexistence method and are fed into the systemlevel simulation presented in the next section.

III. SYSTEM LEVEL SIMULATION

A. Simulation specifications TR 36.885 proposes a methodology for evaluating performances regarding vehicular communications [8]. It involves simulating vehicles which are moving on urban area lanes (urban scenario) or freeway lanes (freeway scenario). In this article, only the urban scenario is considered.

A typical Manhattan-like urban area is considered for the urban scenario [8] (Road configuration for urban case). Nine buildings are arranged into a 3 by 3 matrix, with 4 lanes going through the roads between the buildings (a pair of lanes in one direction and another pair of lanes in the opposite direction). Though several options regarding vehicle speed and density are considered according to [8], we only consider here a vehicle speed equal to 60 km/h and a number of vehicles present in the area which is equal either to 800 vehicles or 1600 vehicles. As proposed in [8] we also assume that when a vehicle is crossing a road, it has a probability equal to 0.5 of going straightforward and an equal probability of 0.25 of turning left or right.

We assume that each vehicle sends periodical Context Awareness Messages (CAM) every 0.47 seconds. Each vehicle assumes its own initial time offset for sending CAM which is randomly set at simulation initialization. However the CAM size (in bytes) is not constant. The CAM size is drawn randomly for each new message, according the probabilities defined in table II. The mentioned duration also includes packet preamble and header.

Regarding PC5 mode 4, several strategies can be proposed for accommodating CAMs of different sizes into transport blocks. It is not recommendable to adapt the MCS as a function of the CAM size because we may obtain different performances for each CAM size. Instead, we propose to fix a single MCS and to derive the required number of Transmission Time Intervals (TTI) as a function of the CAM size. It is also assumed that we use 48 Resource Blocks (RBs) for each Transport Block (TB) (with 2RBs for signaling), so that we accommodate only one TB per TTI, given that we use a 10 MHz radio channel. Table II defines how many TTIs are required for each possible CAM size. In this paper, we have considered the use of MCS3 only. We also need to clarify how to manage the Sensing-Based Semi-Persistent Scheduling (SB-SPS). Because we have regular CAM transmission every 0.47 seconds, we propose to maintain a single SPS with an interval equal to 100 ms for each vehicle. A new SPS is re-established as soon as the former one is over (through the resource re-selection process) in order to maintain one transmission opportunity every 100 milliseconds on average. The re-selection process follows the so called Sensing Based Semi-Persistent Scheduling as standardized in 3GPP release 14 [START_REF] Mannoni | A Comparison of the V2X Communication Systems: ITS-G5 and C-V2X[END_REF]. Of course, this scheme introduces some waste of reserved resources that can however not be avoided because of the variable size of CAM messages. If we have mostly large CAM (e.g. CAM of length 700 bytes), then most of the transmission opportunities will be exploited. However, short messages of length 200 or 300 bytes are typically dominant [START_REF] Mannoni | A Comparison of the V2X Communication Systems: ITS-G5 and C-V2X[END_REF], then the rate of unexploited transmission opportunities may be significant.

This mechanism introduces latency as well, in particular for large CAMs. When a large CAM is received from the application layer for transmission over the air, we may have to wait for 3 transmission opportunities before completing its transmission, leading to 300 milliseconds of additional latency.

As recommended in [8] we use the Winner B1 path loss model (see [START_REF]IST-4-027756 WINNER II[END_REF]) in order to evaluate path losses between vehicles. This model differentiates between Line Of Sight (LOS) conditions and Non Line Of Sight (NLOS). When a building is located between two vehicles, the propagation conditions turn to NLOS.

The simulation process mainly consists in tracking vehicles transmitting packets, and deciding about which receiving vehicles are able to receive such packets, by computing a Signal to Noise plus Interference ratio (SINR) at the receiver. The performances of Section II are used to decide about the decoding success, given the SINR.

For ITS-G5, the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithm is added to decide if the packet can actually be sent on the channel. Only one packet for a CAM message is involved here. However the packet decoding requires 3 successive decision: Preamble detection, Header decoding, and finally CAM message decoding.

For PC5 mode 4, the CAM message decoding may involve several TTI transmissions, each of them carrying a Sidelink Control Information combined with a Transport Block (TB). For each SCI+TB, the simulator first checks that the SCI decoding is successful by computing the SINR and using the SCI performance curve of Section II. Then if it is successful, a second performance curve is used to decide whether the TB is successfully decoded. Finally, a CAM is successfully decoded if all the TBs used for this CAM have been successfully decoded.

The outcome of the simulation is typically made of a set of curves showing CAM Packet Delivery Ratio (PDR) versus maximum distance between CAM sending vehicles and CAM receiving vehicles. For a given distance used in the x-axis the value provided on the y-axis represents the PDR averaged over all potential receivers located at a distance not greater than the x-axis distance.

If a mix of technologies is simulated then separate curves are given for each technology, however each curve reflects the possible loss of performances due to the presence of foreign vehicles.

B. ns-3 based implementation

ns-3 has been used for all simulations and new ns-3 modules have been developed to perform co-channel coexistence simulations

• for managing the lane based mobility of urban scenario • and for instantiating vehicles that follows either ITS-G5 or PC5 mode 4 technology in the context of an hybrid deployment This last module exploits the C++ inheritance concept. A generic vehicle class contains all properties and functions of vehicles common to both technology. As an example, the computation of SINR with respect to a sending vehicle is part of the common functions. Then two classes inherit from this abstract class:

• A first class is used to instantiate ITS-G5 vehicles and it adds specific variables and functions for ITS-G5 • A second class is used to instantiate PC5 mode 4 vehicles and adds specific variables and function as well, for PC5 mode 4 (e.g. the "reselection()" function).

C. Performance comparison in the absence of coexistence Figure 3 gives the performance derived out of 4 independent simulation configurations:

• 800 ITS-G5 vehicles (yellow curve).

• 1600 ITS-G5 vehicles (green curve),

• 800 PC5 mode 4 vehicles (blue curve),

• 1600 PC5 mode 4 vehicles (red curve). If we compare the curve in yellow for 800 ITS-G5 vehicles, to the curve in blue for 800 PC5 mode 4 vehicles, we observe that ITS-G5 outperforms PC5 mode 4 up to a given distance of around 50 meters where the PDR is above 0.9. For higher distances, PC5 mode 4 outperforms ITS-G5. Now if we do the same comparison with 1600 vehicles instead of 800 we observe that ITS-G5 outperforms PC5 mode 4 up to an increased range of about 60 meters. We also observe higher resistance to traffic density on the ITS-G5 side.

These observations can be understood if we remind that we have selected a relatively robust MCS for PC5 mode 4 (MCS3). It provides good performances for long distances however it also reduces the number of resources for SB-SPS (only one resource per TTI) so that the collisions become an issue whatever the distance we have between sending vehicles and receiving vehicles.

If we refer to Table II we can infer that the average channel occupancy duration per CAM message is equal to 529 µs for ITS-G5 while it is equal to 1500 µs for PC5 mode 4 with MCS3. The use of MCS3 provides good transmission range, however it generates many packet collisions.

D. Performance evaluation with co-channel coexistence

Figure 4 shows the performances of ITS-G5 vehicles in a co-channel coexistence scenario. The yellow curve shows the performances for 800 ITS-G5 vehicles in the presence of 800 PC5 mode 4 vehicles. For comparison, we also give the performances for 1600 ITS-G5 vehicles and no PC5 mode 4 vehicle (in red) and the results with 800 ITS-G5 vehicles and no PC5 mode 4 vehicle (in blue). We observe a noticeable degradation of ITS-G5 performances due to the presence of PC5 mode 4 vehicles. This is the case, of course, if we consider as a reference performances of ITS-G5 only with 800 vehicles. But it is still the case if we consider performances of ITS-G5 only vehicles with 1600 vehicles. To illustrate the loss, we may consider a target PDR equal to 0.9. Then the reference distance for this target is equal to around 56 meters if we have 800 ITS-G5 vehicles, around 54 meters if we have 1600 ITS-G5 vehicles, and around 48 meters if we have 800 ITS-G5 vehicles and 800 PC5 mode 4 vehicles. The coexistence performances are shown in the yellow curve with 800 PC5 mode 4 vehicles and 800 ITS-G5 vehicles. We also give for reference the PC5 mode 4 only scenario performances with respectively 800 PC5 mode 4 vehicles (blue curve) and 1600 PC5 mode 4 vehicles (red curve).

Not surprisingly the best performances are obtained with 800 PC5 mode 4 vehicle alone. However if we compare the performances of 800 PC5 mode 4 vehicles in the presence of 800 ITS-G5 vehicles to the performances of 1600 PC5 mode 4 vehicles alone, we observe that the coexistence is beneficial in terms of performances. In other words it is better for a PC5 mode 4 vehicle to face interference issued from an ITS-G5 vehicle rather than interference issued from another PC5 mode 4 vehicle. This should not be a surprise if we remind that the average channel occupancy per CAM is much larger in case of PC5 mode 4 (1500 µs) as compared to ITS-G5 (529 µs). In such conditions, PC5 mode 4 interfering vehicles cause more harm than ITS-G5 interfering vehicles.

However Sensing Based Semi-Persistent Scheduling (SB-SPS) should introduce compensation, because it is meant to avoid collisions even in high channel occupancy conditions. It has been observed that the SB-SPS algorithm has a relatively poor efficiency, mainly for the 2 following reasons:

• The variable size of CAM messages introduces overreservation in SB-SPS, • And the selection of a robust but not spectral-efficient MCS (MCS3) reduces the number of resource sets for SB-SPS re-selection process. E. Performance evaluation in case of coexistence method Figure 6 shows system performances in the co-channel coexistence scenario when a coexisting method is applied, as described in sections I and II. All the curves assume a mix of vehicles with 800 PC5 mode 4 vehicles and 800 ITS-G5 vehicles:

• The blue curve shows PC5 mode 4 performances with coexistence and without coexisting method applied, • The red curve shows ITS-G5 performances with coexistence and without coexisting method applied, • The green curve shows PC5 mode 4 performances with coexistence and with coexisting method applied, • And the yellow curve shows ITS-G5 performances with coexistence and with coexisting method applied. On the ITS-G5 side, we observe that applying the coexistence method is beneficial, even though the improvement is not drastic.

On the PC5 mode 4 side, we observe a performance degradation due to applying the coexistence method. This can be explained due to the constraints in terms of resources selection. By constraining PC5 mode 4 to using only half of the TTIs (with the other half being reserved for ITS-G5) we divide by 2 the number of resources available for selection in the SB-SPS process. This turns into more packet collisions.

IV. CONCLUSION

We have investigated here the performance losses due to co-channel coexistence between LTE-V2X and ITS-G5 technologies. This evaluation was never done before.

We have assessed the loss of ITS-G5 performances when ITS-G5 vehicles coexist with PC5 mode 4 vehicles, and we have confirmed and evaluated the possible benefit of coexistence mechanisms, in order to reduce this loss.

On the other hand we have observed PC5 mode 4 performance improvements, when PC5 mode 4 vehicles face ITS-G5 Fig. 6. PC5 mode 4 performance with a coexistence method applied interference, rather than interference from other PC5 mode 4 vehicles.

Both observations are influenced by the choice of an MCS for PC5 mode 4. We have selected MCS3, which is a relatively robust modulation scheme that brings good performances in terms of achievable communication ranges. On the other hand, this modulation is not spectrally efficient such that it does not exploit the full benefits of Sensing Based Semi Persistent Scheduling.

Future work should consider more spectrally efficient MCS modes for PC5 mode 4, in order to update these conclusions.

Fig. 1 .

 1 Fig. 1. ITS-G5 and LTE-V2X performance with the evolution of the PER as a function of the SNR through EVA channel.

Fig. 2 .

 2 Fig. 2. ITS-G5 header and preamble detection performance on EVA channel.

Fig. 3 .

 3 Fig. 3. Performance comparison with no coexistence

Fig. 4 .

 4 Fig. 4. ITS-G5 performance with coexistence Figure 5 shows the performances of PC5 mode 4 vehicles for the same co-channel coexistence scenario.The coexistence performances are shown in the yellow curve with 800 PC5 mode 4 vehicles and 800 ITS-G5 vehicles. We also give for reference the PC5 mode 4 only scenario performances with respectively 800 PC5 mode 4 vehicles (blue curve) and 1600 PC5 mode 4 vehicles (red curve).Not surprisingly the best performances are obtained with 800 PC5 mode 4 vehicle alone. However if we compare the performances of 800 PC5 mode 4 vehicles in the presence of 800 ITS-G5 vehicles to the performances of 1600 PC5 mode 4 vehicles alone, we observe that the coexistence is beneficial in terms of performances. In other words it is better for a PC5 mode 4 vehicle to face interference issued from an ITS-G5 vehicle rather than interference issued from another PC5 mode 4 vehicle. This should not be a surprise if we remind that the average channel occupancy per CAM is much larger in case of PC5 mode 4 (1500 µs) as compared to ITS-G5 (529 µs). In such conditions, PC5 mode 4 interfering vehicles cause more harm than ITS-G5 interfering vehicles.However Sensing Based Semi-Persistent Scheduling (SB-SPS) should introduce compensation, because it is meant to avoid collisions even in high channel occupancy conditions. It

Fig. 5 .

 5 Fig. 5. PC5 mode 4 performance with coexistence


Keywords: Structure from motion, 3D motion, 3D shape, dorsal pathway, ventral pathway, fMRI, passive viewing, stimulus-driven. ABBREVIATIONS 3D = three-dimensional, SFM = Structure from motion, BOLD = Blood Oxygenation Level Dependent, (f)MRI = (functional) Magnetic Resonance Imaging, EPI = Echo Planar Imaging

We previously identified a number of regions that were selectively activated by transitions of 3D form and 3D motion attributes, either relevant or irrelevant to the task performed by the observers. Here, we tested the same areas with purely-passive viewing conditions. We hypothesized that the areas previously activated by unattended transitions should be activated by the same transitions viewed passively. Unexpectedly, although the occipito-temporal areas were consistently activated by the passively-viewed stimulus transitions, none of the dorsal areas presented any significant activity. Further, the selectivity for the 3D motion seems to disappear in passive viewing. Our results suggest that the dorsal areas considered here are activated by 3D motion in a task context only. The role of the dorsal pathway relative to 3D motion processing is revised in the light of the present results.

INTRODUCTION

Perceiving 3D moving objects implies identifying motion directions as well as recognizing shapes. With real-life objects, static cues are usually available to extract the global shape (e.g. local contours) so that brain activity related to the final shape perception can hardly be disentangled from brain activity related to the early processing of these visual cues. Using structure-from-motion (SFM) stimuli, for which the global form as well as the global motion are defined through motion cues only, it is possible to target the activity related to the endpercept 3D attribute while ignoring cue-dependent intermediate stages of processing. In a previous work using a factorial design that independently modulated the feature attended by the observer and the stimulus attributes, we were able to unveil a number of cortical areas selectively involved in processing 3D motion or 3D shape, for attention-related and/or stimulus-driven conditions [START_REF] Paradis | Processing 3D form and 3D motion: Respective contributions of attention-based and stimulus-driven activity[END_REF]. The stimulus-driven conditions consisted of 3D-motion or 3D-form transitions occurring while an incident task focused the observers' attention on the colour of the stimulus. In the present work, we wondered whether the same areas would be activated if similar modulations of the stimulus attributes were presented in purely passive viewing conditions. Our hypothesis was that the areas activated in our previous stimulus-driven condition would also be activated by passively-viewed changes, but that areas activated when attention was directed to the feature may not.

METHODS

Stimulation

Visual stimuli consisted of 200 coloured dots and a central fixation, displayed on a black background and viewed through a 16° diameter virtual window. There were 5 blocked conditions where the dots could be either static (ST: 10 blocks), moving incoherently (IM: 10 blocks) or moving coherently as if they belonged to a three-dimensional surface oscillating in depth (3D: 15 blocks). The 3D blocks were divided into 3 conditions of 5 blocks each, where the form of the stimulus, its 3D motion or its colour arrangement changed every 2 s., each time the oscillating surface passed through the central and initial position. As in [START_REF] Paradis | Processing 3D form and 3D motion: Respective contributions of attention-based and stimulus-driven activity[END_REF], the 3D form alternated between a paraboloid and a horse-saddle; the oscillation axis tilted in the screen plane by an angle of 45°, 60° or 90°; and 85 to 95 % of the dots reversed colour (from red to green and vice versa). In the IM blocks, the direction of motion of all dots changed every 2 s by an angle comprised between 30° and 100°. Block durations varied from 4 to 12s, with the whole experiment lasting 5m 12s. In the scanner, stimuli were backprojected on a translucent screen that participants could see through mirror glasses.

Participants were instructed to fixate a cross at the centre of the display. T2*-weighted gradient echo EPI sequence (flip angle 90°, TE = 40ms, TR = 2 s) sensitive to BOLD contrast, with 18 contiguous slices (in-plane resolution 3.75 x 3.75 mm², 6 mm thickness) covering the cerebral cortex, but excluding the cerebellum and the most inferior part of the temporal poles.

These data were acquired in the context of a more general study, which was approved by an Institutional Ethic Committee and was conducted with the understanding and written consent of each participant. In this context, participants underwent four runs of functional acquisition during the scanning session. In the last two runs, participants had to pay attention to the motion, form or colour of a 3D stimulus. The analysis of these runs was described in a previous work [START_REF] Paradis | Processing 3D form and 3D motion: Respective contributions of attention-based and stimulus-driven activity[END_REF] and used to delineate the regions of interest for the present work. The passive-viewing data presented here were acquired during the first run in order to avoid a possible attentional bias toward one specific attribute of the stimulus.

Data analysis

Functional data were corrected for their geometric distortions and the first three scans acquired during the transition to the steady state of the magnetic resonance signal were discarded. The following pre-processing was performed using SPM functions (http://www.fil.ion.ucl.ac.uk/spm). As the evaluated amount of head movement within runs was less than 3 mm in translation and 2° in rotation, realignment was performed between but not within runs. Anatomical and functional images were normalised into the MNI stereotactic system of coordinates using a local template matching the contrast specificity of our images, and functional data were smoothed using an 8-mm (FWHM) isotropic Gaussian kernel. The final size of the functional voxels was set to 3 x 3 x 3 mm 3 . Statistical analyses were carried out using SPM2 (http://www.fil.ion.ucl.ac.uk/spm). Five regressors were built up using the "HRF" basis function triggered by stimulus transitions.

Each regressor modelled the event-related BOLD responses to one type of transition: Onset of an incoherent motion and its changes of direction (2D motion); apparition of a 3D object (3D onset); changes of 3D motion, 3D form or colour when a 3D object was present. Individual results were entered in a second level random effect analysis, and group results were tested in selected regions of interest using the SPM toolbox MarsBaR (Brett et al., 2002; http://marsbar.sourceforge.net/). Activity with p<0.05, corrected for multiple comparisons over the number of ROI, is considered significant.

Regions of interest (ROI)

We limited the analysis of the present passive-viewing data to areas previously found to be differentially activated for transitions of 3D form and 3D motion. Our regions of interest thus include dorsal areas that were found more activated by transitions of 3D motion and occipital and ventral areas that were more activated by form transitions. The data used to delineate these areas were acquired with the same subjects but from independent scanning runs. In these, participants underwent an active task so that form and motion transitions were either relevant or irrelevant to the on-going task (details in [START_REF] Paradis | Processing 3D form and 3D motion: Respective contributions of attention-based and stimulus-driven activity[END_REF]. The regions of interest are represented on Figure 2 along with their activity pattern in the passive viewing conditions. Their coordinates and spatial extent are detailed in Table 1.

Figure 2: Activity profiles of the regions of interest in the passive viewing conditions (ROI). Green ROI

were found more activated by 3D motion than form transitions, Red ROI were more activated by 3D form transitions. For dark ROI, the contrast between 3D motion and form was significant for attended transitions. For light ROI, the contrast between 3D attributes was significant for the transitions occurring during an incidental colour task. For each ROI, the activity profile plots the mean amplitude of response (SPM beta ± one standard error) to each type of passively-viewed transition. From left to right: changes of motion direction in random motion (2D, green); onset of a 3D stimulus (3D, red); colour transition in the 3D stimulus (C, grey); transition of the 3D stimulus form (F, red) and transition of the 3D stimulus motion (M, green). All activity profiles are plotted using the same scale, with beta from -1 to +2 for dorsal 8

RESULTS

Although of possibly small amplitude, the activity observed hereafter is always positive, that is greater than the activity during the static-dot baseline. By contrast with what was observed in the context of a task, we do not find any negative activity relative to the low level baseline.

Shape-selective ROI

All the regions of interest previously defined as selective for form transitions appear significantly activated by passively-viewed colour transitions. LO and the superior occipital areas (V3A) are significantly activated by passively-viewed changes of the 3D form (F), with maximal activation in LO, while fusiform areas (pFs and Fus) only show subthreshold activity for these (uncorrected p = 0.01 for pFs, uncorrected p = 0.04 and 0.02 for the left and right Fus). The activity related to the onset of the 3D stimulus never reaches significance (uncorrected p = 0.01 for both V3A; 0.02 and 0.03 for left and right LO respectively, and more than 0.03 for the fusiform areas), although it is similar in amplitude with the activity related to shape transitions in V3A.

None of the ventral ROI is significantly activated by the transitions of 3D motion (M) (uncorrected p ≤ 0.01 for LO; ≤ for V3A; 0.04 for left pFs and ≥ 0.05 for both fusiform and right pFs). The amplitude of activity however tightly follows that related to the random motion changes (2D), which is significant in bilateral LO, bilateral V3A and left pFs (corrected p < 0.05). Note that the number of 2D-motion events was about twice the number of the other events, inducing a greater statistical power.

3D-Motion-selective ROI

In contrast with the shape-selective areas, the dorsal areas do not show any significant activity related to the passively-viewed transitions with respect to the baseline (corrected p > 0.05).

The closest-to-significance activity was found bilaterally in the dorsomedial FEF (med FEF)

for the form transitions (uncorrected p = 0.02 and 0.01 for the left and right FEF respectively).

The left supramarginal region also shows subthreshold activity for the colour and form transitions at comparable levels of significance (respectively p = 0.02 and p = 0.03, uncorrected). With similar beta values, the form-related (F) activity in PO and the 3D-motion (M) activity in both med FEF only reach p = 0.04, uncorrected. All other activities have uncorrected p ≥ 0.05.

Summary

As expected, in passive viewing conditions, the ventral regions of interest are preferentially activated by colour and form transitions, and smaller activity is found in the most anterior fusiform areas which were previously delineated using attended transitions. More surprisingly, none of the ROI is significantly activated by the 3D-motion transitions viewed passively, not even the dorsal ones which were previously selective for the 3D-motion transitions either relevant to the task or not. In addition, the dorsomedial FEF previously found selective for the attended motion transitions now reveals a tendency to respond to form transitions as well as motion transitions.

DISCUSSION

That no dorsal area be significantly activated by passively viewed transitions of the 3D stimulus seems at odds with the observation that these areas were previously activated by unattended transitions of 3D motion. To exclude artefactual causes to this discrepancy, we first examine the experimental choices and data analysis. We then discuss the possible outcome of our results.

Positive activity relative to the static baseline

Overall, we note that none of the passively viewed transitions induce decreased activity with respect to the static baseline. By contrast, decreased activity was observed in the task context.

Although, we cannot exclude that this difference is due to the slightly different baselines used in the active and passive experiments, this observation is consistent with our previous suggestion that not only signal enhancement but also inhibitory mechanisms are associated with attentional selection.

Background stimulus vs. transitions

The 3D stimulus we used conveys colour, 3D-motion and form information at once. The three attributes are present and visible to the observer all the time the stimulus is displayed on the screen, which means at transitions as well as between transitions. In this study, we modelled the transitions only, assuming that the steady-state activity related to the background presence of the stimulus would be negligible related to the activity evoked by the transitions. As the activity related to the transitions is measured with respect to a static baseline, we cannot exclude that part of the activity captured at transitions be due to the background presence of the coloured 3D moving stimulus. In line with this, IOS, LO and pFs, which are the most activated areas, are part of the network usually reported to be activated by the presence of a structure-from-motion stimulus [START_REF] Orban | Human cortical regions involved in extracting depth from motion[END_REF][START_REF] Paradis | Visual perception of motion and 3-D structure from motion: an fMRI study[END_REF][START_REF] Kriegeskorte | Human cortical object recognition from a visual motion flowfield[END_REF]. FEF however are also part of this network but do not appear significantly activated here. In addition, this interpretation cannot account for all the observed effects, because activity due to the background presence of the stimulus would not depend on the transition types, whereas here the amplitude of activity varies with transitions, especially between colour and motion transitions -and particularly in right V3A, and between form and 3Dmotion transitions in bilateral LO.

3D onsets: form or motion processing?

The 3D onsets were modelled separately from the 3D-motion and form transitions because the first appearance of the 3D stimulus was not supposed to enhance preferentially the process of one attribute compared to the other. May the activity profiles give us any hint that the 3D onsets may be assimilated to form or motion events?

If we except LO and the left supramarginal, it is noteworthy that the amplitude of activity related to the 3D onsets tightly follows that of the form transitions in most areas, which suggests that the onset of the 3D stimulus might rather be treated as a form event by most areas. By contrast with the form transitions however, the responses to the 3D onset never reach significance, even though there was the same number of 3D onsets compared to form transitions (15 each). As the onsets are isolated events whereas form transitions are blocked, the statistical difference might be due to the respective efficiency of these events' arrangement [START_REF] Friston | Stochastic designs in eventrelated fMRI[END_REF].

Ventral areas

The activation of the superior occipital regions (V3A) to 2D motion and 3D form transitions viewed passively is consistent with other results [START_REF] Tootell | Functional Analysis of V3A and Related Areas in Human Visual Cortex[END_REF][START_REF] Paradis | Visual perception of motion and 3-D structure from motion: an fMRI study[END_REF][START_REF] Denys | The Processing of Visual Shape in the Cerebral Cortex of Human and Nonhuman Primates: A Functional Magnetic Resonance Imaging Study[END_REF]) and a possible role of this area in a coarse analysis of the 3D shape, as previously suggested [START_REF] Valyear | A double dissociation between sensitivity to changes in object identity and object orientation in the ventral and dorsal visual streams: A human fMRI study[END_REF][START_REF] Paradis | Processing 3D form and 3D motion: Respective contributions of attention-based and stimulus-driven activity[END_REF].

The activity in bilateral LO is maximal for the form transitions and significantly higher for this condition than for the 3D-motion transitions (Paired t-test t > 2.01, p < 0.04 in both hemispheres). These results are in line with what has been shown by Grill-Spector and colleagues [START_REF] Grill-Spector | Cue-invariant activation in object-related areas of the human occipital lobe[END_REF], 1999) and replicate in passive-viewing condition what was found when subject were paying attention to colour [START_REF] Paradis | Processing 3D form and 3D motion: Respective contributions of attention-based and stimulus-driven activity[END_REF]. They thus confirm the selectivity of LO for the form transitions independently of the task context.

Last, and consistent with our expectations, the fusiform (fus) areas that were delineated using attended transitions show the smallest amplitudes of response to the passively-viewed transitions.

Overall in the ventral pathway, the activity evoked by the unattended form and motion transitions occurring during a colour task appear as a good predictor for the activity induced by the same transitions in passive viewing. By contrast, whereas colour transitions induce greater activity than 3D motion transitions in passive viewing, they always induced decreased activity compared to the unattended motion transitions or the static baseline during the motion task. This likely confirms that, as assumed previously, the activity observed at colour transitions in the active conditions was actually related to the task rather than the colour transition itself.

Dorsal areas

With a task context, lateral FEF showed negative or null response to unattended form and positive response to unattended motion. Although the results are below significance, the exact same tendency appears in passive viewing.

For med FEF, we had a positive or null response to unattended form transition and slightly greater response to unattended motion transitions. Here, passively-viewed form and motion transitions both induce similar subthreshold positive activity. Thus, although selective for 3D motion when this attribute is attended and competing with form in the task context, dorsomedial FEF seems sensitive to any 3D attribute transitions when there is no task. This could be consistent with medial FEF contributing to orient attention whether this is exogenously or endogenously driven: attention may be attracted by any 3D object-related event in passive-viewing, but motion transitions could be favoured compared to form in the task context because they can help update orienting information.

Similarly to Med FEF, cuneus and bilateral supramarginal areas showed subthreshold positive activity for form transitions in passive viewing. By contrast, the activity at the unattended form transitions occurring during the colour task was always negative compared to the static baseline in the three regions. The reduced activity of the cuneus in a demanding-task context is consistent with this area being part of the default mode network [START_REF] Raichle | A default mode of brain function[END_REF]. Note that the cuneus is the only dorsal ROI revealing a possible difference between passivelyviewed form transitions and 3Ds-motion transitions (uncorrected p = 0.02), with a greater positive response to the form transitions compared to the 3D-motion transitions.

Finally, the paracentral area, which showed maximal activity for the unattended compared to attended transitions, is among the ones showing the lowest activity whatever the transition in passive viewing.

Overall in passive viewing, the dorsal regions not only show reduced activity compared to the previous active conditions, most of them also appear to lose their selectivity for the 3D motion feature. With these results, we may wonder whether there is an automatic processing of objects' 3D motion or whether accessing this feature systematically requires attention.

Figure 1 :

 1 Figure 1: Experimental conditions and associated model of the BOLD response. (a) Stimulation sequence in passive viewing. The baseline consists of static dots (ST). Stimuli are either incoherent motion (IM) or threedimensional surfaces (3D) changing colour (C), form (F) or motion direction (M) every 2s. (b) Onsets for the 5 event-related regressors modelling the fMRI signal. For the 2-D motion regressor, intervening onsets correspond to changes in local motion directions.

  (a) and parieto-occipital (b) areas, and beta from 0 to +3 for the ventral areas (c). Asterisks indicate responses significantly different from zero with a threshold of p < 0.05 corrected for the number of tested ROI. Double dots indicate responses under this threshold but with uncorrected p < 0.05. Areas for which no response passed the corrected threshold are bordered with dotted lines. Note that only form-related areas (red) show significant responses in passive-viewing conditions. All the responses passing the threshold in those areas had a corrected p < 0.04, whereas all dorsal responses had a corrected p > 0.4 and uncorrected p ≥ 0.02, except in the right superior FEF where the response to the form transitions reached p = 0.01, uncorrected. Abbreviations: ant= anterior; post= posterior; lat= lateral; med= medial; cing= cingular; paracentral= paracentral lobule; FEF= frontal eye field, at the intersection of the precentral and superior frontal sulci; supra= supramarginal gyrus; angul= angular gyrus; V3A= superioroccipital sulcus; LO= lateral occipital area; pFs= posterior fusiform; fus= fusiform gyrus.

(

  x y z) are the coordinates of the centre of mass of the ROI in mm. Cing. = cingulate; ant. = anterior; post. = posterior; FEF = frontal eye field; lat = lateral; med = medial; SMA = supplementary motor area; IPL = inferior parietal lobule; supra.= supramarginal gyrus; angul. = angular gyrus; POs = parieto-occipital sulcus; SOg = superior occipital gyrus; IOS = intra-occipital sulcus; LO = lateral occipital; pFs = posterior fusiform; Fus = fusiform. In black, the regions that were activated by unattended transitions; in italic grey, ROI delineated by attended transitions.

Table 1 . Regions of interest (ROI)

 1

	Label	Size	Coordinates		Label	Size	Coordinates
		(mm 3 )	x	y	z		(mm 3 )	x	y	z
	Motion-selective regions			post. cing.	1728	4	-46	66
	ant. cing.	2619	5	46	47	POs	1161	4	-90	24
	lat. FEF	1215	-37	12	54		Form-selective regions
		2403	36	14	51	SOg/IOS	2403	-31 -86	11
	med. FEF	891	21	-4	57	(V3)	3456	30 -76	17
		4344	-26	0	58	LO	512	-44 -77	-8
	SMA	1576	-6 -19	61		1184	49 -70	-11
	IPL (supra.)	4736	-54	-46	46	pFs	976	-34 -66	-8
		3186	48	-51	49		968	36 -67	-15
	IPL (angul.)	1809	-48	-73	37	Fus	6291	-33 -55	-13
		736	48	-72	31		2451	34 -40	-16

ACKNOWLEDGMENT

This work was supported by the ACC-SV program (n° 951261/12 from the French Ministère de la recherche) and IFR49. Scanning was conducted at the Service Hospitalier Frédéric Joliot, CEA, with the help of Pierre-Gilles Henri. ALP was supported in part by a fellowship from CEA, IFSBM, and a grant of neuro-ophtalmology from la Fondation de France.

Data

We analysed the data from ten healthy volunteers (4 men, 6 women) aged 21-28 years. All had normal vision; all but one were right handed, and one had a left ocular dominance.

Functional data were acquired in a 3T whole body MRI system (Bruker, Germany), using a

Highlights

• LO is sensitive to form transitions whatever the task context.

• No "deactivation" is observed in passive viewing.

• Dorsal areas appear significantly activated by stimulus transitions in a task context only, whether the transitions are relevant to the task or not.

•

SUPPLEMENTARY MATERIAL

Supplementary Figure 1: Activity profiles of the regions of interest in the experiment with a task context (results from [START_REF] Paradis | Processing 3D form and 3D motion: Respective contributions of attention-based and stimulus-driven activity[END_REF]. Green ROI were found more activated by 3D motion transitions than form transitions, Red ROI were more activated form transitions. For dark ROI, the contrast between 3D motion and form was significant when the transitions were attended. For light-color ROI, the contrast between 3D attributes was significant for the unattended transitions. For each ROI, the activity profile plots the mean amplitude of response (SPM beta ± one standard error) to each type of transition.