Thuong Van Vu
email: thuong-van.vu@lip6.fr

Thi Mai

Trang Nguyen
email: thi-mai-trang.nguyen@lip6.fr

Guy Pujolle
email: guy.pujolle@lip6.fr

Distributed Opportunistic and Diffused Coding with Multiple Decoders in Wireless Mesh Networks

Keywords: C.2.1 [Network Architecture and Design]: Wireless communication network coding, opportunistic coding, forwarding

In wireless mesh networks for unicast traffic, opportunistic network coding are specifically introduced for improved network utilization. Some recent practical implementations like COPE, BEND, DCAR and DODE have shown the promising results over the original 802.11 conventional forwarding mechanism. For better performance, the aim of all mentioned network coding implementations is trying to find more and more coding chances in network topologies. However, they restricted the finding within a simple rule "a pair of coder and decoder". For every coded packet (the combination of natives packets of traffic flows) sent on the traffic flow, we always have one coder (which creates the coded packet) and one decoder (which retrieves the desired packet for the destination). The trivial rule limited coding chances much. In this paper, we are loosing this noose: we decouple the coding and decoding functions from strictly a pair of coder and decoder. For one coder, we can have multiple decoders on the path (up to the number of other traffic flows involved in the coding). With this, more coding chances are found, thus, improving the network performance. We extend our proposed DODE with this new idea, called Distributed Opportunistic and Diffused Coding with Multiple Decoders -DODEX. We implement DODEX system in NS-2. The simulation results show that DODEX can outperform its previous introducing systems.

INTRODUCTION

Since its first introduction in [START_REF] Ahlswede | Network Information Flow[END_REF], network coding has gained a significant attention from the research communities in the need of improving the way of communication in computer networks. Network coding is a technique which allows the nodes to combine several native packets into one coded packet for transmission (i.e., coding packets) instead of simply forwarding packets one by one. The network then can save the number of transmissions to reduce the data transfer time and increase the throughput. Based on that, opportunistic network coding (ONC) is firstly introduced in COPE [START_REF] Katti | XORs in The Air: Practical Wireless Network Coding[END_REF], the practical network coding system for the unicast traffic. Afterwards, more extended works are presented like BEND [START_REF] Zhang | MAC-layer proactive mixing for network coding in multi-hop wireless networks[END_REF], DCAR [START_REF] Le | DCAR: Distributed Coding-Aware Routing in Wireless Networks[END_REF] and DODE [START_REF] Vu | Distributed Opportunistic and Diffused Coding in Multi-hop Wireless Networks[END_REF]. Providing a particular coding condition, all these network coding systems can ensure after the coded packets are opportunistically created and broadcasted in the air by a coder (i.e., node intersecting many flows), there will be one decoder (i.e., node retrieving the desired packet for the destination) on the traffic flow decoding the coded packet successfully. The trivial rule limited coding chances much. Our previous work DODE has combined the features from COPE (the opportunistic listening), BEND (the diffused gain), and DCAR (the generalized coding condition) to create a better network coding architecture. DODE also exploited the routing metric SPENM (Shortest Paths of Enriched Neighbors Metric) which helps to choose the path with the most coding chances. DODE can indeed grasp more coding chance and the obtained throughput is significantly improved.

In this paper, we extend DODE to resolve the restricting rule: we decouple the coding and decoding functions from strictly a pair of a coder and a decoder. After one coder creating the coded packets, multiple decoders can be the next on the transmission path to retrieve the desired packet for the destination. More coding chances can be found, leading to an improved network performance. Our contribution are two-fold:

• We redesign the coding condition in DODEX for each coded packet at the coder, which allows multiple decoders to be on the traffic flow instead of only one in order to find more coding chance and improve network utilization.

• We implement the DODEX system in NS-2 and compare the result with other implementation 802.11, COPE, BEND, DCAR and DODE. The simulation results show that DODEX can outperform these systems.

The remainder of this paper is organized as follows. In sec-tion 2, we describe the related work which inspires our proposal. Section 3 details the design of DODEX. Simulation results are presented and analysed in section 4. Finally, we give the conclusion in section 5.

RELATED WORK

Previous Opportunistic Coding systems

By opportunistically listening to other transmission, wireless nodes in COPE [START_REF] Katti | XORs in The Air: Practical Wireless Network Coding[END_REF] can use overheard packets to perform ONC. In Fig 1(b), node 0, 2, 3, 4 wish to send a packet p0, p2, p3, p4 to its opposite node 2, 0, 4, 3 respectively, via the forwarder 1. The dashed lines imply related nodes (e.g., 0 and 1) are 1-hop neighbors to each other and the arrows indicate the traffic flows. Node 1 codes four native packets to broadcast (p0 ⊕p2 ⊕p3 ⊕p4) and receivers can extract the desired packets without failure. For examples, node 0 can get p2 successfully because p3, p4 are overheard by transmissions of (3 → 1), (4 → 1)), and p0 is its own packet. To perform opportunistic coding, COPE designs its own particular coding condition. Each pair of n native packets will be checked with the coding condition if they can be coded together before nodes can process n-packet coding (the combination of n native packets). By this way, the coders ensure their coded packets are received and decoded successfully. BEND argued a problem presented by the nature of ONC: concentrating traffic via a node for coding can lead to packet collisions and drops. BEND suggested the diffused gain, the neighbors of the node intersecting flows (like 3, 4 of coder I for flows (0 → 1 → 2) and (2 → 1 → 0) in Fig 1(b)) can actively overhear the transmission and broadcast the coded packets. Instead of only one coder, we can have advantages of multiple coders sharing the coding process. From that point of view, BEND keeps the two next-hop coding pattern from COPE, but it increases the number of coders on the flows, which alleviates the packet collisions and drops at the coders, and improves the network performance. DCAR takes another approach for network enhancement: DCAR breaks the two next hop coding pattern provided by COPE. The authors of DCAR creates the general coding condition which allows the coder and decoders not to necessarily be neighbors but any nodes which can intersect flows (coders) and overhear the transmission properly (decoders), respectively. For examples, in Fig 1(c), we can see 3 as coder and 4, 7 as decoders while COPE, and BEND cannot detect that.

DISTRIBUTED OPPORTUNISTIC AND DIF-FUSED CODING WITH MULTIPLE DE-CODERS -DODEX

DODE

In this part, we introduce DODE, which our proposition in this paper is heavily based on. DODE is a network coding architecture that works at MAC layer but also uses information from upper layers (the packet queues and the routing protocol) to do the coding, decoding processes. In summary, DODE contains several features: the opportunistic listening and encoding from COPE, the diffused gain by neighborhood from BEND and the generalized coding condition from DCAR. The coding condition of DODE can be seen as a combination of two features: the diffused gain from BEND and the general coding condition from DCAR. At first, DODE can increase the coding chances by adding more coders (via the diffused gain). Next, the general coding condition is applied, helping DODE to discover the coding chances over the whole traffic paths, better than the two next hop coding pattern from BEND or COPE. Moreover, as already explained in Related work, the diffused gain can alleviate the concentrating traffic problem as DCAR inherits it from COPE. Particularly, DODE gains a better performance by taking the advantages form the previous and resolving their problems. Besides, DODE also provide a new routing metric called SPENM (Shortest Path with Enriched Neighbors Metric) over the routing protocol DSDV [START_REF] Perkins | Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers[END_REF] to cope with the new coding condition, helping to find routing paths with enhanced coding chances. Due to the lack of space, more technical explanation can be found in our previous work [START_REF] Vu | Distributed Opportunistic and Diffused Coding in Multi-hop Wireless Networks[END_REF].

Coding chance improvement

As mentioned in Related work, each opportunistic coding architecture designs its own coding condition to gain more chances, thus, improving the network performance. In DO-DEX, coders can combine many native packets for transmission and multiple decoders next on the flow will recover the desired packet. Consider Fig 3(a), we can see three flows

F1(0 → 3 → 4 → 5), F2(2 → 3 → 6) and F3(8 → 7 → 3 → 1)
can be coded together but on the flow F1 there are two decoders 4 (for leaving out packet p2) and 5 (removing p3 to get the desired p1). With previous implementation, only 2packet coding can be found instead of 3-packet coding like our proposition DODEX. The coded packets are not maintained "untouched" from the coder to the destination but eventually "peeled out" the unnecessary packets until only the intended packet can reach to destination. Therefore, the coding condition can be rewritten as followings:

Detailed design

To perform the coding condition above, DODEX has to collect the information "who sends what" on the transmission path of each flow and detect the coding chances based on that. Moreover, DODEX needs to store, forward native packets, create and forward coded packets when coding chances exist. Particularly, Each nodes also change their behaviors to also intercept the coded traffic. and have to collect the neighbor list and the source routing list to check Figure 1: Opportunistic network coding scenarios the coding condition above, the queuing system to manipulate packets.

Neighbor list and source routing list

The routing protocol DSDV is modified to cope with carrying more information to detect coding chances. Besides original information of DSDV, each route entry in the update message will also carry the list of nodes constituting the route and the neighbor list of each node along the route. The neighbor list is obtained by collecting the senders' addresses of DSDV messages. Before broadcasting out the DSDV messages, the sender adds its address to the current list of routing path in the entry. After receiving the routing updates from neighbors, each node will process the routing update messages as the original DSDV routing protocol and also cache the information about the neighbors and detailed routing path. With this, all nodes in the network gradually acquire enough information to perform the correct coding.

Queuing system

DODEX uses four different packet queues: Qnative for the native packets, Qovrhrd for storing the overheard packets, Q codable for the linked lists of packet that can be combined together to create coded packets, and Q coded for coded packets that need forwarding. Figure 2 shows the illustration of queues.

Q_native

Node behavior

Each node in DODEX has to perform native packet interception, coded packet interception and packet transmission which is described in Section A.

SIMULATION AND RESULTS

We use NS-2 as the simulator to compare the performances of DODEX with the previous architectures: IEEE 802.11, COPE, BEND, DCAR and the non-extended DODE. We use two topologies as illustrated in Rate) flows sent over UDP (User Datagram Protocol) using 1000-byte datagrams with an arrival interval of 0.01s and traffic generation duration at source of 150s. The performances is then evaluated by two performance metrics, the throughput and the number of coded packets. We vary the traffic flows in test scenarios as shown in table Flows in Test scenarios. Afterwards, the test scenarios will be executed with each traffic-flow variety for all implementations and the result is collected with a 95% confidence interval.

F1(0 → 3 → 4 → 5), F2(2 → 3 → 6), F3(8 → 7 → 3 → 1)
6 flows varied in test scenario 1

F1(0 → 3 → 4 → 5), F2(2 → 3 → 6), F3(8 → 7 → 3 → 1), F4(5 → 4 → 3 → 0), F5(6 → 3 → 2), F6(1 → 3 → 7 → 8) 7 flows var- ied in test sce- nario 2 F1(6 → 2 → 3 → 1), F2(1 → 3 → 2 → 6), F3(0 → 3 → 4 → 5), F4(5 → 4 → 3 → 0), F5(5 → 6), F6(1 → 4), F7(0 → 2) 10 flows var- ied in test sce- nario 2 F1(6 → 2 → 3 → 1), F2(1 → 3 → 2 → 6), F3(0 → 3 → 4 → 5), F4(5 → 4 → 3 → 0), F5(5 → 6), F6(1 → 4), F7(0 → 2), F8(5 → 6 → 2), F9(2 → 6 → 5), F10(0 → 2 → 6)
As shown in Fig 4(a) and 4(b), with the light traffic, DO-DEX outperforms previous implementations significantly. In the case of 3 flows, only DODEX discovered the 3-packet coding, and the throughtput gain over the previous is quite promising (32% over our old DODE and 30%-45% over the others). First, COPE and BEND only detects the 2-packet coding between F2 and F1. However, coder 3 also serves as a forwarder for F3, increasing packet collisions and drops at node 3 due to concentrating traffic via the coder. Second, DODE and DCAR are also able to detect the 2 packetcoding between F1 and F3 thanks to the general coding condition but the same problem happened too because the diffused gain does not help BEND, DODE or even DODEX much (only 3 as the coder). With 3-packet coding, DODEX allows more packets delivered to destination, result in higher throughput. For the next case (6 flows), because there are more coding chances (F4, F5 and F6 are in reverse directions with F1, F2 and F3, respectively), the previous works can compensate the throughput gain and the number of coded packets by creating the coded packets from these flows with opposite ways. Nevertheless, DODEX still maintains higher throughput because DODEX can detect not only all coding chances like the previous but also the 3-packet coding with multiple decoders.

In the second topology exposing the coding 3 or 4 packets in a single transmission, the performances of all previous and DODEX are presented in Figs 4(c) and 4(d). We would like to check if under heavy traffic, DODEX still keeps the high throughput and coded packets. By applying 4-packet coding with multiple decoders, DODEX transfers more data even in the interference of the non-codable flows. Compared to DODEX, in case of 10 flows (4-6 of them are non-codable), all previous implementations are losing throughput because it takes more transmissions for 2-packet coding. Due to high interference from the non-codable flows, the throughputs gained from all architectures are reduced, both from competition of accessing MAC layer to send coded packets or the packet collisions and drops. DODEX can alleviate the problem by discovering more coding chances via coding with multiple decoders, draining the packets from the queue at the forwarder faster than the others (4-packet coding compared to 2-packet coding), thus, giving DODEX a better performance over the formers (6% over DODE, and 10%-20% over the others as Figs 4(c) and 4(d) shows)

CONCLUSION

In this paper, we have proposed a nouvel and practical network coding architecture for unicast traffic in wireless mesh network. We extend the result gained from DODE to explore a new way of intercepting the coding chance in opportunistic network coding, which helps to improve the network performance. We allow many decoders on the flow to share the decoding process instead of only one like the previous systems. More coding chances are discovered, and help to achieve the better network utilization.

APPENDIX A. NODE BEHAVIOR

Algorithm 1 Native packet interception 1: p i ← native packet 2: if ∃ linked list L ⊂ Q codable / p i is codable with ∀p j , p j ∈ L then 3:

Add p i to the list 4: else if there exists ∃ p j ∈ Q native / p i is codable with p j then 5:

Remove p i , p j from Q native , create the linked list with p i , p j and add the list to Q codable 6: else if there exists ∃ p j ∈ Q ovrhrd / p i is codable with p j then 7:

Remove p i , p j from Q ovhrd , create the linked list with p i , p j and add the list to Q codable 8: else if p i is the intended packet then 9:

Add p i to Q native 10: else 11:

Add p i to Q ovhrd 12: end if Algorithm 2 Coded packet interception 1: p i ← coded packet 2: if p ′ i is an intended packet for the node then 3:

Remove as many as possible the non-intended native packets in p ′ i 4:

if only one desired packet remained then 5:

Process Algorithm 1 Native packet interception 6:

else if there exists coded packet p" i and there still exists decoders on path to destination then 7:

Add p" i to Q coded 8:

end if 9: end if Algorithm 3 Packet transmission 1: Access the medium 2: if Q codable ≠ ∅ then 3:

Remove the linked list from Q codable 4:

Create the coded packet from the first linked list 5:

Send the packet 6: else if Q coded ≠ ∅ then 7:

Remove the first coded packet from Q coded 8:

Send the packet 9: else if Q native ≠ ∅ then 10:

Remove the first native packet from Q native 11:

Send the packet 12: end if

  the authors of COPE designed the two next-hop coding pattern: two flows intersecting at a node c are codable if the next hop of c on a flow is the previous forwarder of c on the other flow (Fig 1(a)) or its neighbor (Fig 1(b)), and vice versa.

  Generalized coding conditions in DODEX for each pair of n flows Fi and Fj intersecting at node c c ∈ ((Fi ∩ Fj) ∪ (N (nexthop(c, Fi)) ∩ N (nexthop(c, Fj )) ∩ N (prevhop(c, Fi)) ∩ N (prevhop(c, Fj )))) 1. There exists a node dc,i,j ∈ (D(c, Fi) ∩ (U (c, Fj ) ∪ N (U (c, Fj ))). 2. There exists a node dc,j,i ∈ (D(c, Fj ) ∩ (U (c, Fi) ∪ N (U (c, Fi))). where: • Fi denotes the i th flow in the network, i ∈ {1..n} • nexthop(c, F ) prevhop(c, F ) denotes the nexthop/previous hop of node c on flow F • D(c, F )/U (c, F ) denotes the downstream/upstream nodes of flow F . • N (si) denotes the set of all neighbors nodes of node si • dc,i,j denotes the decoder of c on flow Fi to remove the packet of flow Fj

Figure 2 :

 2 Figure 2: Queuing system

Figure 3 :

 3 Figure 3: Test scenarios for exposing 3 and 4-packet coding in DODEX

Figure 4 :

 4 Figure4: Test scenario results more coding chances (F4, F5 and F6 are in reverse directions with F1, F2 and F3, respectively), the previous works can compensate the throughput gain and the number of coded packets by creating the coded packets from these flows with opposite ways. Nevertheless, DODEX still maintains higher throughput because DODEX can detect not only all coding chances like the previous but also the 3-packet coding with multiple decoders.In the second topology exposing the coding 3 or 4 packets in a single transmission, the performances of all previous and DODEX are presented in Figs4(c) and 4(d). We would like to check if under heavy traffic, DODEX still keeps the high throughput and coded packets. By applying 4-packet coding with multiple decoders, DODEX transfers more data even in the interference of the non-codable flows. Compared to DODEX, in case of 10 flows (4-6 of them are non-codable), all previous implementations are losing throughput because it takes more transmissions for 2-packet coding. Due to high interference from the non-codable flows, the throughputs gained from all architectures are reduced, both from competition of accessing MAC layer to send coded packets or the packet collisions and drops. DODEX can alleviate the problem by discovering more coding chances via coding with multiple decoders, draining the packets from the queue at the forwarder faster than the others (4-packet coding compared to 2-packet coding), thus, giving DODEX a better performance over the formers (6% over DODE, and 10%-20% over the others as Figs 4(c) and 4(d) shows)

Table 1 :

 1 Flows in Test scenarios

	3 flows var-
	ied in test sce-
	nario 1

ACKNOWLEDGMENTS

We appreciate the support and suggestion from Professor Nadia Boukhatem from Computer Science & Network Department, TELECOM ParisTech to accomplish this work.


Ryan Robart
email: ryan.robart@insa-lyon.fr

Etienne Parizet

Jean Christophe Chamard

Karl Janssens

Fabio Bianciardi

Josef Schlittenlacher

Wolfgang Ellermeier

Perceval Pondrom

James Cockram

Paul Speed-Andrews

Gemma Hatton

eVADER: A Perceptual Approach to Finding Minimum Warning Sound Requirements for Quiet Cars

Introduction

The danger of quiet cars, such as hybrid and electric vehicles, to pedestrians has become an important issue for public policy [START_REF]Minimum Sound Requirements for Hybrid and Electric Vehicles[END_REF], car manufacturers as well as the scientific community. The eVADER project (Electric Vehicle Alert for Detection and Emergency Response) has taken a progressive approach to this problem by using psychoacoustics to try to establish some basic guidlines for adding sound to such vehicles. Project contributors include: car manufacturers; technological , material, & engineering companies; academic laboratories; and the European Blind Union, which is a non-profit organization dedicated to the safety and quality of life of the visually-impaired people of Europe.

Requirements for potential warning sounds included the following premises:

1. Sounds should be easily detected by pedestrians despite a range of background noise.

2. Sounds should add as little noise as possible to the environment in the interest of avoiding noise pollution and annoyance.

3. Sounds should convey information regarding vehicle dynamics, such as speed, to pedestrians (especially the visually-impaired).

The research presented here focused specifically on premise 1, with special consideration given to the loudness of the warning sounds as required by premise 2. Another experiment which will focus directly on annoyance (premise 2), is planned but has not yet begun. Experiments focused on premis 3 are currently underway and will not be included in this report. Past research has used a localization paradigm to test the detectability of hybrid and internal combustion cars by measuring listener reaction times to binaural recordings of such vehicles [START_REF] Robart | Are hybrid cars too quiet? Proceedings oft he 157th meeting oft he Acoustical Society of America: Paper 5aNS8[END_REF]. A similar method was adopted in this experiment with some important modifications. These modifications are associated with creating a virtual environment that seemed as natural as possible to listeners while gathering data. Also, in the interest of testing prototypical sounds, these sounds had to be designed according to a quantifiable structure, and then added to the virtual environment.

The primary goal of this experiment was to test if some distinct features of potential warning sounds might be heard more easily than others if the overall level is held constant among all sounds. In order to derive the appropriate features to test, it was necessary to review related literature from various areas of study. It is beyond the scope of this document to completely review such research. Still it should be noted that the psychoacoustic research regarding listener sensitivity and masking was considered [START_REF] Moore | An Introduction to the Psychology of Hearing[END_REF]. Perceptual research regarding auditory scene analysis was also taken into account [START_REF] Bregman | Auditory scene analysis: The perceptual organization of sound[END_REF]. Perhaps most importantly, the literature focused on alarm sound design and perception was also instructive in our endeavour [START_REF] Misdariis | Do electric cars have to make noise? An emblematic opportunity for designing sounds and soundscapes[END_REF][START_REF] Edworthy | Heterogeneity in auditory alarm sets makes them easier to learn[END_REF][START_REF] Parizet | Signaux d'alerte : analyse bibliographique[END_REF][START_REF] Tabata | Development of Nissan Approaching Vehicle Sound for Pedestrians[END_REF][START_REF] Catchpole | Localizable auditory warning pulses[END_REF][START_REF] Edworthy | Learning auditory warnings: The effects of sound type, verbal labeling and imagery on the identification of alarm sounds[END_REF][START_REF] Edworthy | Improving auditory warning design: relationship between warning sound parameters and perceived urgency[END_REF][START_REF] Patterson | Auditory warning sounds in the work environment[END_REF]. As a result, the following three features were selected for testing: tonal content; frequency detuning; and amplitude modulation.

Since it is likely that warning sounds composed of broadband noise would difficult to segregate from a noisy environment [START_REF] Moore | An Introduction to the Psychology of Hearing[END_REF][START_REF] Bregman | Auditory scene analysis: The perceptual organization of sound[END_REF], all sounds were composed of a specified number of harmonic sinusoidal harmonic tones. Each of the proposed sound-features (factors) varied according to 3 levels, as displayed in table 1.

Table 1: Description of stimulus dimensions of for each level of each sound feature selected to be tested. These 9 prototype sounds were layered onto a recording of an electric vehicle to emulate an electric vehicle with added sound emitting from a loudspeaker (EV+S).

Adhering to the localization paradigm used by Robart & Rosenblum [START_REF] Robart | Are hybrid cars too quiet? Proceedings oft he 157th meeting oft he Acoustical Society of America: Paper 5aNS8[END_REF], recordings of an electric vehicle (EV) and a normal car (Diesel) were included as reference stimuli. Thus, there were a total of 11 stimuli used in the experiment. General predictions were that the Diesel should be detected earlier and more accurately than the EV. Furthermore it was predicted that there could be some ambiguity among the EV+S's if the timbre factors (frequency detuning, tonal content, and amplitude modulation) have varying impact on perceptibility.

Methods & Materials Experiment Design

In a fractional repeated measures design [START_REF] Taguchi | Taguchi methods. Orthogonal arrays and linear graphs[END_REF][START_REF] Koehl | Influence of structural variability upon sound perception: Usefulness of fractional factorial designs[END_REF] 11 stimuli were presented 8 times each, 4 repetitions from each direction. A fractional design was chosen primarily for two reasons. The first was the fact that all combinations of factors (3 x 3) would require 27 distinct stimuli. Since each stimulus lasted 10.8 seconds, it was determined that the experiment would likely be uncomfortably long with 27 stimuli. The second reason for using a fractional design was due to the novelty of our application, and a lack of research that specifically designates a paradigm to manipulate the chosen factors (frequency detuning, tonal content, and amplitude modulation). As a result, Taguchi's method [START_REF] Taguchi | Taguchi methods. Orthogonal arrays and linear graphs[END_REF] was chosen as the best way to begin to choose the combinations of factors. Using an orthogonal array (table 2), we could designate which factor combinations to use as the sounds in the EV+S's.

Table 2:

Orthogonal array used to choose factor X level combinations used for the design of stimuli. The stimulus code for each sound refers to the level combinations of each factor as shown above.

Sound Design

Sounds were constructed using a custom synthesizer (Max/MSP) and normalized to ensure that all sounds had the same overall level. As previously stated, binaural recordings of an electric and diesel cars were used as reference stimuli. These recordings consisted of a single car approaching and eventually passing directly in front of a dummy-head. The track was 60 meters and the dummy head was situated in the middle (30 meters). The speed of the cars was 20 km/h, which remained constant (figure 1). This scenario had been selected as one of the most dangerous situations for pedestrians in a previous eVADER workpackage [START_REF] Garcia | Acoustic measurement of close to accident scenarios[END_REF]. The nine warning sounds were spatially synthesized so that they could be layered onto the recording of the electric vehicle. This was done to create the impression that the warning sounds were emitted from a loudspeaker mounted on the car. Recordings of a busy freeway and a rainstorm were mixed to create a realistic, and challenging background context. The level of the background set at approximately 65 dBA. This level was chosen to avoid making the task of hearing the electric vehicle too difficult (floor effects). The peak dBA levels for all stimuli is displayed in figure 2 .

Participants & Procedure

Ninety-one participants (aged 20-72) participated in the experiment and 33 were visually impaired. The experiment was conducted in three separate laboratories, but the procedure was maintained in every case. Participants were instructed to imagine that they wished to cross a somewhat busy street during a rainstorm. They were told that their task was to indicate as quickly and accurately as possible when they heard a car approaching them by pressing buttons on the keyboard. The space-bar indicated a leftward approach, while the enter key indicated a rightward approach. The participants were also told that in order to attain a realistic presentation, the cars would appear in a somewhat random fashion. That is, the onset of each stimulus was randomized between 1-20 seconds from the offset of the previous stimulus. So, sometimes the next trial would begin 1 second after the previous, or it could begin up to 20 seconds after the previous trial. Each participant received a unique presentation of stimulus order and inter-trial intervals. Participants were familiarized with all sounds and underwent a short training exercise before the actual experiment. The experiment was split into 2 blocks, 44 trials in each. After the first block, participants were given the option to take a short break if needed. The entire procedure lasted roughly 45-60 minutes.

Materials

The experiment was conducted in a quiet room, and sounds were delivered via Stax electrostatic headphones and amplifier system. The stimuli and background sounds were presented using Delphi software, which controlled the timing of events as well as recording data. Both reaction time and accuracy were measured as a result.

Results

Data from participants [START_REF] Parizet | Signaux d'alerte : analyse bibliographique[END_REF] were rejected if more than 40% of the stimuli were missed and/or erroneously localized.Between subject differences were quite high. In order to reduce the variance due to this, the reaction time data was centered on individual means to reduce variability (figure 3). No differences were found between visually impaired and sighted people. Furthermore, there was no difference between direction of approach, or different ages. The fractional 3(frequency detuning) X 3(tonal content) X 3 (amplitude modulation) ANOVA produced main effects for all factors (p < .05). However, these results are difficult to interpret, because interactions cannot be accounted for in a fractional design. However, it seems that the factor of amplitude modulation had the most discernable impact on detectability (figure 4). In that, as the amplitude modulation increased from level 1 to level 3, listeners reliably detected the vehicle faster.

Figure 4:

The interaction plot displays the differential effects of each factor at each level (faster is < 0, and slower is > 0). Zero may be thought of as the overall average reaction time. F1 refers to frequency detuning, F2 refers to tonal content and F3 refers to amplitude modulation.

Based on the data, it seems that different factor combinations are heard earlier than others. More specifically, the results suggest that the best warning sound should have a temporally irregular amplitude modulation structure (level 3); and contain few harmonics (level 1), and contain no frequency detuning (level 1). However, this must be concluded with caution as the interactions cannot be explained in a fractional design. Furthermore, a sound with all 3 of those features (113) was not tested as the 113 combination was not specified by the orthogonal table (figure 2). Still, it is evident that 2 of the EV+S's (133; 313) were detected as early as the Diesel.

Accuracy was measured by analysing correct vs. incorrect detections of the direction of approach. Interestingly, 313 produced fewer errors than any other sound with exception to the Diesel. A post-hoc t-test showed that the difference in average errors (313 vs. Diesel) was not significant t (83) = 1.34 (p = .18). Still the Diesel produced twice as many errors as the 313 sound (figure 5). A miss occurred when the participant failed to detect a stimulus during a trial. Post-hoc t-tests showed that the EV produced more misses than all other stimuli (p < .05). There were no significant differences in misses among all other stimuli.

Number of dir. errors Number of dir. errors

Conclusions

The general prediction that the EV would be heard later and less accurately than the Diesel was confirmed based on posthoc comparisons of detection and accuracy. This can be seen a replication of previous work [START_REF]Minimum Sound Requirements for Hybrid and Electric Vehicles[END_REF], and speaks to the strength of the general method. Regarding the EV+S's, the results indicate that the independent variables chosen (frequency detuning, tonal content, and amplitude modulation) have differential effects on the perceptibility of warning sounds. Based on the fractional ANOVA, and posthoc comparisons regarding detection and accuracy, it was concluded that sound 313 is the best combination of the factors tested for use as a quiet warning sound in eVADER's prototype system.

Interestingly, it seems that our incremental manipulation of the factors among the different stimuli significantly effected the detectability of the vehicles. These results cannot be due to SPL dBA (see figure 2). Overall, the results suggest that, with careful sound design, warning sounds need only add 2 dBA to an electric vehicle to be at least as detectable as a normal car. This is likely due two factors:

1. temporally irregular amplitude modulation (level 3 amplitude modulation)

few harmonics (level 1 tonal content)

The results also suggest that the harmonic structure should not contain frequency detuning. But the best EV+S tested (313) did contain a saw-tooth detuning of the highest two harmonics, so it is unclear if this is true. Certainly, further tests need to be conducted to test the strength of the suggested combination(s), and perhaps more importantly, to find the constraints of potential manipulations.

In light of the recently proposed minimum requirements for sounds added to quiet cars [START_REF]Minimum Sound Requirements for Hybrid and Electric Vehicles[END_REF], these results might seem surprising. For example, NHTSA recommends that warning sounds should be primarily composed of filtered, broadband noise. This could make figure-ground segregation difficult for listeners, especially if the warning sounds are emitted at low-levels. Furthermore, it is likely that adding broadband noise to cars would contribute to noise pollution. Moreover, while the current minimum requirements do not disallow amplitude modulation, it is certainly not required. Based on the results of this experiment, it is clear that there should be a requirement for a minimum amount of audible amplitude modulation. Future research should include perceptual tests of warning sounds based on the minimum requirements proposed by NHTSA. Hopefully, the research presented here will help increase the growing knowledge base of vehicle sound perception, as well as the use perceptual research when making such guidelines in the future.

Figure 1 :

 1 Figure 1: This image depicts illustrates both the recording procedure and the 'waiting to cross' scenario used in the experiment. In the recording procedure, a dummy-head was centrally located facing the track (red arrow).

Figure 2 :

 2 Figure 2: Peak levels of the 11 stimuli used in the experiment. The labels on the x axis describe the stimuli.The numbered stimuli, such as '111' etc., contain a coding that describe a certain combination of the levels of the three independent variables: frequency modulation; tonal content; amplitude modulation.

Figure 3 :

 3 Figure 3: The diamonds indicate the mean reaction times for all participants for each stimulus. The standard error was made to be equal among the stimuli by centering the data as you can see by the error bars.

Figure 5 :

 5 Figure 5: This graph represents all directional errors (no misses) for all 84 participants, separated according to stimuli. Stimulus 313 is the only sound that produced less (50% fewer) errors than the Diesel.

Aknowledgements

The authors acknowledge the funding of the EC for the project eVADER (Electric Vehicle Alert for Detection and Emergency Response), grant agreement # 285095. The work published within reflects only the opinion of the authors and should not be affiliated with any other institution, public or private.