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Background Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7 – 20.5 % higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2 – 17.3 %). Conclusions In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off.

Objective: Conventional visual field (VF) tests are limited in predicting on-road driving performance of individuals with glaucoma. We developed a new performance-based VF test in a driving simulator for individuals with glaucoma. The aim was to evaluate the psychometric properties of the newly developed test. Methods: In this pilot study, 17 drivers with glaucoma aged 65.24 ± 9.69 and 13 control drivers aged 61.27 ± 11.45 completed the performance-based VF task. Construct validity was determined by comparing performance on the new task with results on conventional visual and perimetry tests including Humphrey Visual Field Analyzer, Keystone Vision Screener, and Useful Field of View (UFOV). Concurrent validity was evaluated by comparing performance on the new task with on-road driving performance, scored using the Test Ride for Investigating Practical fitness-to-drive (TRIP). Ten individuals (7 with glaucoma and 3 controls) participated in the test–retest reliability assessment. Results: Drivers with glaucoma identified fewer symbols (P = .047) and took longer to respond to the symbols (P = .048) compared to controls. In the glaucoma group, correct responses on the performance-based VF test correlated strongly (r = −0.51, P = .046) with UFOV divided attention. Both glaucoma and control groups achieved submaximal to maximal scores on the TRIP (median [Q1–Q3], glaucoma: 193 [191–196]; controls: 196 [195–196]; P = .16). No strong correlations were found between scores on the performance-based VF test and on-road driving performance in glaucoma. The intraclass correlation coefficients ranged between 0.77 for response time and 0.92 for correct responses, indicating good to excellent test–retest reliability. Conclusions: We established the construct validity and test–retest reliability of the performance-based VF test. Future studies should include a larger sample with more severe driving difficulties to demonstrate the concurrent validity between performance-based VF testing and on-road driving performance in glaucoma.

Night driving is challenging for driving simulations. At night, the constraints on Computer Graphics performance are unusually strong, as many relevant details have low contrasts and low luminance values, while the overall contrast is very high, due to light sources from road lighting and vehicle headlamps. Some level of realism is needed in terms of contrast rendering, because contrast impacts visibility and motion perception, which in turn impact the driving behavior. This is usually achieved in Computer Graphics with Tone Mapping Operators (TMOs), in order to display the computed images on a standard, Low Dynamic Range (LDR) display device. We explore in this paper the drawbacks of these operators for nighttime driving simulations, focusing on motion cues, and discuss the potential benefit of the emerging technology of High Dynamic Range (HDR) display devices. We have focused on a night-time road environment with an incoming motorcycle. Two experiments have been conducted, with 33 participants. Time-to-Collision (TTC) was considered as a proxy for motion perception. TTC estimations were collected on a HDR display and compared to degraded visual environments. The first experiment shows that the visibility of the motorcycle’s outline is the main visual cue for the TTC estimation in nighttime conditions (but this doesn’t happen with cars). It suggests that the main drawbacks of TMOs with respect to TTC estimation involves the mapping of low contrasts, which either enhances or impairs the motorcycle visibility. The second experiment explores this hypothesis and shows that enhancing the visibility of the motorcycle leads to biases in the TTC estimation, whereas removing it does not impact the TTC estimation — at the cost of other realism problems. These results suggest that motion cues at night are more realistic with a HDR Display, and that such display devices may be useful in situations where a realistic perception of hardly visible contrasts is needed.

Introduction: Driving an automobile is an important activity for the social participation of individuals with traumatic brain injury (TBI). Return to safe driving is usually addressed during rehabilitation, but we know little about driving behaviors in the years following TBI rehabilitation.Objective: To explore self-reported and objective (official driving records) post-rehabilitation driving behaviors and offenses in individuals with TBI: (a) having passed a driving evaluation, (b) who did not undergo a driving evaluation, and (c) non-injured controls.Methods: Cross-sectional design with 162 adults: (a) 48 participants with mild, moderate, or severe TBI whose drivers' license was suspended and reinstated following a driving evaluation during rehabilitation (TBI-DE; M = 42.2 years of age, SD = 11.5); (b) 24 participants with TBI who maintained their driving privileges without undergoing a driving evaluation (TBI-NE; M = 36.5 years of age, SD = 9.9); (c) 90 non-injured controls (M = 43.8 years of age, SD = 11.4). Participants with TBI were recruited from seven rehabilitation centers, 2-3 years after the end of rehabilitation in the province of Quebec, Canada. During a telephone interview, data were obtained regarding self-reported driving: (a) habits; (b) self-efficacy; (c) anger expression; (d) sensation-seeking; (e) violations/errors; (f) accidents, driving offenses, and demerit points for the two-year interval predating the study. Objective data for driving offenses, accidents, and demerit points were obtained from the automobile regulatory body for the same period and for the two-year interval before the injury for the TBI groups.Results: Compared to non-injured controls, the TBI-DE group reported significantly lower scores for self-reported verbal aggressive expression of anger and driving violations/errors. Conversely, their official driving records showed significantly more demerit points for the last 2 years, and a significantly higher frequency of serious post-rehabilitation accidents (10), compared to the TBI-NE group (one) and the control group (none). Compared to pre-injury levels, individuals with TBI had significantly more demerit points post-rehabilitation.Conclusions: Individuals with TBI may underestimate risky driving behaviors even if they have been deemed fit to drive. Reduced self-awareness, memory, and dysexecutive problems following TBI could influence self-report of driving behaviors and explain discrepancies between self-reported and objective driving-related behaviors. Recommendations for research and practice are provided.; La possibilité de conduire est importante pour la participation sociale des adultes ayant subi un TCC. Le retour à la conduite automobile sécuritaire est abordé pendant la réadaptation, mais nous en savons peu sur les capacités et comportements de conduite dans les années après le TCC. Objectif : Évaluer si les personnes avec un TCC ayant réussi une évaluation de la conduite automobile se différencient, en termes de comportements et d'infractions de conduite, de celles n'ayant pas eu d'évaluation de la conduite, et de témoins sans TCC. Participants (a) 48 TCC léger/modéré/grave - permis de conduire suspendu et rétabli après une évaluation de la conduite auto pendant la réadaptation 24 TCC - permis maintenu sans évaluation de la conduite (b) 2-3 ans après la réadaptation Recrutés via 7 centres de réadaptation (Montréal: CRLB, CRCL, HJR; Montérégie & Estrie: CRM, CRE; Laurentides & Lanaudière: CR Le Bouclier; Québec: IRDPQ)(c) 90 témoins sans TCC/trouble neurologique recrutés dans la population générale; appariés pour région géographique (d) Tous conduisaient plus de 1000 km/an depuis au moins deux ans (témoins) ou avant le TCC Mesures (e) Comportements de conduite auto-rapportés (entrevue tél.) (f) Accidents/sanctions auto-rapportées depuis 2 ans, et documentées depuis 2 ans et dans les 2 ans pré TCC (SAAQ). Conclusion. Les personnes avec un TCC ayant réussi une évaluation de la conduite automobile : ont repris la conduite automobile au même niveau que les personnes TCC n'ayant pas eu d'évaluation et que les témoins pourraient sous-évaluer leurs comportements de conduite automobile à risque Selon les résultats du groupe de discussion mené avec des intervenants de 10 des 12 programmes d'évaluation de la conduite automobile du Qc : l'autocritique face aux habiletés de conduite devrait être évaluée davantage lors de l'évaluation sur route, p. ex. en conduite non dirigée une collaboration serrée entre le professionnel en évaluation de la conduite et l'équipe de réadaptation peut contribuer à mieux cerner les aspects cognitifs, comportementaux et psychologiques pré-post TCC pouvant avoir un impact sur la conduite, et à déterminer le meilleur moment pour effectuer l'évaluation une réflexion est à faire sur l'harmonisation des pratiques d'évaluation sur route, la place donnée à l'entraînement en conduite et le besoin d'effectuer des suivis post-réadaptation chez les personnes TCC qui ont réussi l'évaluation de la conduite mais qui pourraient être considérées à risque.

BIBM 2018, IEEE International Conference on Bioinformatics and Biomedicine, Madrid, ESPAGNE, 03-/12/2018 - 06/12/2018; Machine learning (ML) technics have been recently used to detect emotion and predict crash severity. This research work aims at assessing different classifications and machine learning technics in predicting the alcohol consumption and associated functional states. 28 young drivers were tested for a 45 min drive with a blood alcohol concentration (BAC) of 0.0, 0.2 and 0.5g/m. Subjective functional states were analysed using Thayer's scale and NASA-TLX. The physiological parameters (electroencephalogram, electrodermal and cardiac activity) and driver simulators parameters (speed, lateral positioning and wheel steering) were acquired during the three alcohol sessions. Data were analysed on 10s temporal windows without superposition nor gap. Two analyses using classification and ML technics were used: to determine both capacity of the algorithms to detect alcohol consumption (BAC level) and functional states (effort, performance and alertness) from NASA and Thayer's scales. Different algorithms were trained using 10 folds cross validation technics using Weka (University of Waikato, NZ). Using both vehicle and physiological data was beneficial for BAC prediction and ROC area of the top three algorithms were found between 0.62 and 0.72 with higher results for Random Forest (RF) algorithms. In functional states prediction, results were similar for all effort, performance and alertness predictions with ROC area reaching 0.75 for RF. Once algorithm setting optimized, performances for BAC prediction reached 0.73 while were, lower than for functional states prediction with ROC area of 0.91 when pairing data. Such results could help in the strategy for detecting alcohol consumption in drivers.

The purpose of this study was to identify the influence of situation complexity and driving experience on subjective workload and driving performance, and the less costly and the most effective strategies faced with a hazard pedestrian crossing. Four groups of young drivers (15 traditionally trained novices, 12 early-trained novices, 15 with three years of experience and 15 with a minimum of five years of experience) were randomly assigned to three situations (simple, moderately complex and very complex) including unexpected pedestrian crossings, in a driving simulator. The subjective workload was collected by the NASA-TLX questionnaire after each situation. The main results confirmed that the situation complexity and the lack of experience increased the subjective workload. Moreover, the subjective workload, the avoidance strategies and the reaction times influenced the number of collisions depending on situation complexity and driving experience. These results must be taken into account to target the prevention actions.

Recent improvements of communication technologies leads to several innovations in road vehicles energy consumption. As an example, several ecodriving applications already appeared on all smartphone application markets. Using embedded smartphone signals, such applications provide real time feedback to drivers according to their performances. However most of these applications does not take into account upcoming events such as curves, slopes or crossings to advise the driver on the best actions to undertake to lower energy consumption. Furthermore, they do not analyze data coming from vehicle sensors. In this paper, we present an android application, developed within the FP7 European project ecoDriver, which provides several innovative properties: advice according to upcoming events, a real time evaluation of the driving behavior, the analysis of past actions, an interface with OBD2 connector and some more. This paper further develops the complete architecture and links between each innovative function. Future works will concentrate on integrating image processing in this application in order to detect the possible presence of a front vehicle.

Except for Quebec, all Canadian provinces have introduced administrative laws to lower the permitted blood alcohol concentration (BAC) to .05% or .04% for driving-or having the care of-a motor vehicle. Using linear mixed effects models for longitudinal data, this study evaluates the effect of administrative BAC laws on fatal alcohol related crashes and law enforcement patterns in Canada from 1987 to 2010. Results reveal a significant decrease of 3.7% (95% C.I.: 0.9-6.5%) in fatally injured drivers with a BAC level equal or greater than .05% following the introduction of these laws. Reductions were also observed for fatally injured drivers with BAC levels greater that .08% and .15%. The introduction of administrative BAC laws led neither to significant changes in the rate of driving while impaired (DWI) incidents reported by police officers nor in the probability of being charged for DWI under the Criminal Code.

ObjectivesTo assess crash risk and driving habits associated with chronic medical conditions among drivers entering old age.DesignProspective cohort study.SettingFrench cohort GAZEL.Participants12 460 drivers in the analysis of road traffic crash, among whom 11 670 completed the follow-up period (2007–2014). We assessed driving cessation among 11 633 participants over the same period, and mileage and driving avoidance among the 4973 participants who returned a road safety questionnaire in 2015.Main outcome measuresYearly occurrence of at least one road crash as a driver; time to driving cessation; mileage; driving avoidance: at night, with bad weather, in heavy traffic, with glare conditions, over long distances.ResultsSeveral potentially risky conditions (angina, myocardial infarction, coronary disease; stroke; nephritic colic, urinary stones; glaucoma) were associated with lower mileage and/or driving avoidance and did not increase crash risk. Neither driving avoidance nor lower mileage was found for other conditions associated with an increased crash risk: hearing difficulties (adjusted OR 1.19, 95% CI 1.06 to 1.34); joint disorders (1.17, 95% CI 1.06 to 1.30). Depression, anxiety and stress was associated with an increased crash risk (1.23, 95% CI 1.01 to 1.49) despite increased driving avoidance. Parkinson’s disease was associated with driving cessation (adjusted HR 32.61, 95% CI 14.21 to 65.17).ConclusionsDepending on their condition, and probably on the associated risk perception, drivers entering old age report diverse driving habits. For example, hearing difficulties is a frequent condition, rarely considered a threat to road safety, and nonetheless associated with an increased crash risk.

This study aims to measure and analyze unregulated compound emissions for two Euro 6 diesel and gasoline vehicles. The vehicles were tested on a chassis dynamometer under various driving cycles: Artemis driving cycles (urban, road, and motorway), the New European Driving Cycle (NEDC) and the World Harmonized Light-Duty Test Cycle (WLTC) for Europe, and world approval cycles. The emissions of unregulated compounds (such as total particle number (PN) (over 5.6 nm); black carbon (BC); NO2; benzene, toluene, ethylbenzene, and xylene (BTEX); carbonyl compounds; and polycyclic aromatic hydrocarbons (PAHs)) were measured with several online devices, and different samples were collected using cartridges and quartz filters. Furthermore, a preliminary statistical analysis was performed on eight Euro 4-6 diesel and gasoline vehicles to study the impacts of driving conditions and after-treatment and engine technologies on emissions of regulated and unregulated pollutants. The results indicate that urban conditions with cold starts induce high emissions of BTEX and carbonyl compounds. Motorway conditions are characterized by high emissions of particle numbers and CO, which mainly induced by gasoline vehicles. Compared with gasoline vehicles, diesel vehicles equipped with catalyzed or additive DPF emit fewer particles but more NOx and carbonyl compounds.

If previous research studied acceptability of partially or highly automated driving, few of them focused on fully automated driving (FAD), including the ability to master longitudinal control, lateral control and maneuvers. The present study analyzes a priori acceptability, attitudes, personality traits and intention to use a fully automated vehicle. 421 French drivers (153 males, M = 40.2 years, age range 19-73) answered an online questionnaire. 68.1% Of the sample a priori accepted FAD. Predictors of intention to use a fully automated car (R2 =.671) were mainly attitudes, contextual acceptability and interest in impaired driving (i.e. the two components of FAD acceptability), followed by driving related sensation seeking, finally gender. FAD preferred use cases were on highways, in traffic congestion and for automatic parking. Furthermore, some drivers reported interest in impaired driving misuses, despite awareness of their responsibility for both the vehicle and the driving. These results are discussed regarding previous knowledge about acceptability of advanced driving assistance systems and consequences for the use of fully automated cars.

Previous research has shown that lane-width reduction makes drivers operate vehicles closer to the center of the road whereas hard-shoulder widening induces a position farther away from the road's center. The goal of the present driving-simulator study was twofold. First, it was aimed at further investigating the respective effects of lane and shoulder width on in-lane positioning strategies, by examining vehicle distance from the center of the lane. The second aim was to assess the impact on safety of three possible cross-sectional reallocations of the width of the road (i.e., three lane-width reductions with concomitant shoulder widening at a fixed cross-sectional width) as compared to a control road. The results confirmed that lane-width reduction made participants drive closer to the road's center. However, in-lane position was affected differently by lane narrowing, depending on the traffic situation. In the absence of oncoming traffic, lane narrowing gave rise to significant shifts in the car's distance from the lane's center toward the edge line, whereas this distance remained similar across lane widths during traffic periods. When the shoulders were at least 0.50m wide, participants drove farther away from both the road center and the lane center. Road reallocation operations resulted in vehicles positioned farther away from the edge of the road and less swerving behavior, without generating higher driving speeds. Finally, it is argued that road-space reallocation may serve as a good low-cost tool for providing a recovery area for steering errors, without impairing drivers' behavior.

Eye movements analysis offers the possibility to investigate what is behind the eyes: our brain. Among the variety of activities studied by cognitive ergonomics, car driving received particular attention regarding visual exploration. Here, the four main techniques used to analyse eye movement data while driving have been applied to gaze positions analysis while negotiating bends under manual and highly automated driving. Gaze positions of eighteen drivers were recorded on a driving simulator. (1) Gaze plots and (2) areas of interest analysis based on the visual scene (without information on displayed images) did not reveal detailed differences between manual and highly automated driving, whereas (3) dynamic areas of interest and (4) dynamic point based on a dynamic element of the driving scene showed a disengagement from visual information required to steer the vehicle in bends in highly automated driving. These results help researchers to better characterize visual search in bend taking and by consequence to understand the degradations of the driving performance in highly automated driving.; L'oculométrie est une technique qui offre l'opportunité d'investiguer, au travers des mouvements des yeux, ce qui se trouve derrière : notre cerveau. L'oculométrie moderne bénéfice, entre autres domaines, à la recherche en psychologie ergonomique. Parmi la variété des situations étudiées en psychologie ergonomique, l'activité de conduite automobile a fait l'objet de nombreuses études dont un bon nombre font appel à l'oculométrie. Les analyses des données oculométriques ont progressé à mesure du développement de cette technique, et sont aujourd'hui nombreuses et abouties en ce qui concerne l'analyse des parcours oculaires sur des images statiques. En revanche, dans le cadre de l'étude de la conduite automobile, comme des activités dynamiques en général, les images présentées au conducteur sont dépendantes à la fois de l'environnement de conduite, mais aussi de ses propres actions sur le véhicule via le volant et les pédales, ce qui rend les techniques d'analyse habituelles des données oculométriques moins pertinentes. Face à cette situation, les chercheurs ayant un intérêt pour l'étude de la conduite automobile ont développé plusieurs techniques d'analyses des mouvements oculaires qui peuvent être regroupées en quatre classes de méthodes. Les quatre méthodes se définissent par rapport à l'analyse des positions du regard (1) dans un référentiel écran en deux dimensions, (2) selon des zones d'intérêts fixes définies dans ce même référentiel écran, (3) selon des zones d'intérêts dynamiques définies dans un référentiel relatif à la tâche de conduite (correspondant à un élément de la scène visuelle qui se déplace dans celle-ci) et (4) dans un référentiel défini sur un point dynamique de la scène visuelle (un point de la scène visuelle qui se déplace dans celle-ci). Afin de caractériser l'influence de la méthode d'analyse sur l'interprétation des résultats, chacune de ces quatre classes de méthode a été présentée et appliquée ici à la prise de virage en conduite automobile simulée en condition de conduite classique et en conduite hautement automatisée. En condition hautement automatisée, l'assistance maintenait le véhicule dans sa voie en agissant directement sur le volant sans aucune intervention de la part du conducteur. Dix-huit conducteurs ont pris part à une étude sur simulateur de conduite où les positions du regard ont été enregistrées. Une variabilité importante dans l'interprétation des données est apparue selon la méthode d'analyse considérée. A l'appui des résultats collectés via les quatre classes de techniques d'analyse et des connaissances relatives à la prise d'informations visuelle en virage, il apparaît qu'un point ou une zone dynamique dans la scène visuelle sont mieux à même de rendre compte des modifications des stratégies de prise d'information visuelles dans le contexte dynamique de la conduite automobile. Ces méthodes ont permis de mettre en avant et de spécifier une différence dans les prises d'informations visuelles en présence de l'assistance et comparativement à la condition non-assistée. La discussion est destinée à éclairer le lecteur au regard du choix de la technique d'analyse des mouvements oculaires en présentant les avantages et les limites de chacune des quatre classes de méthode et l'impact du choix de la méthode d'analyse des données oculaires sur l'analyse ergonomique des situations considérées et les enjeux sécuritaires associés.

Objective: An experiment was performed in a driving simulator to investigate the impacts of practice, trust, and interaction on manual control recovery (MCR) when employing fully automated driving (FAD). Background: To increase the use of partially or highly automated driving efficiency and to improve safety, some studies have addressed trust in driving automation and training, but few studies have focused on FAD. FAD is an autonomous system that has full control of a vehicle without any need for intervention by the driver. Method: A total of 69 drivers with a valid license practiced with FAD. They were distributed evenly across two conditions: simple practice and elaborate practice. Results: When examining emergency MCR, a correlation was found between trust and reaction time in the simple practice group (i.e., higher trust meant a longer reaction time), but not in the elaborate practice group. This result indicated that to mitigate the negative impact of overtrust on reaction time, more appropriate practice may be needed. Conclusions: Drivers should be trained in how the automated device works so as to improve MCR performance in case of an emergency. Application: The practice format used in this study could be used for the first interaction with an FAD car when acquiring such a vehicle.

The allocation of overt visual attention is investigated in a multi-task and dynamical situation: driving. The Expectancy-Value model of attention allocation stipulates that visual exploration depends on the expectancy and the value of the task-related information available in each Area Of Interest (AOI). We consider the approach to an intersection as a multi-task situation where two subtasks are involved: vehicle control and interactions with other drivers. Each of these subtasks is associated with some specific visual information present in the associated AOIs: the driver's lane and the intersecting road at the intersection. An experiment was conducted in a driving simulator, coupled with a head-mounted eye-tracker. The intersecting road's AOI's Expectancy was manipulated with the traffic density, and its Value was manipulated with the priority rule before the intersection (stop, yield, and priority). The distribution of visual attention and the dynamics of visual exploration were analyzed on 20 participants, taking into account the dwell time in the AOIs associated to the driving subtasks, and the gaze transitions between the AOIs. The results suggest that the visual attention to intersecting roads varied with the priority rule, and impacted the visual attention associated with the vehicle control subtask. In addition, a quantitative model was used to improve the understanding of the Expectancy and Value factors. The comparison of the data with the model's predictions enables quantifying the observed differences between the experimental factors. Finally, the results associated with the traffic density are discussed in relation to the nature of the relevant information while approaching the intersection.

Older drivers are more numerous on the roads. They are expert drivers, but with increasing age certain physiological changes can interfere with driving, which is a complex activity of daily living. Older drivers are involved in fewer accidents than younger drivers, but they have a higher accident rate per kilometer driven. The elderly are heavily represented in the balance sheet of road deaths, being motorists or pedestrians. This high mortality is largely explained by their physical frailty. In the presence of deficits, self-regulation of driving habits, changes/reductions or stopping in driving activity occur in the elderly. But cognitive deficits are associated with an increased risk of accidents. Among drivers with Alzheimer's disease, there is a heterogeneity of driving ability, making difficult the advisory role of a physician for driving. A protocol for physicians was developed to assess cognitive impairments that may affect driving in an elderly patient. The car plays an important role in the autonomy of the elderly and patient advice on stopping driving should take into account the risk/benefit ratio.

Phone use while driving has become one of the priority issues in road safety, given that it may lead to decreased situation awareness and deteriorated driving performance. It has been suggested that drivers can regulate their exposure to secondary tasks and seek for compatibility of phone use and driving. Phone use strategies include the choice of driving situations with low demands and interruptions of the interaction when the context changes. Traffic light situations at urban intersections imply both a temptation to use the phone while waiting at the red traffic light and a potential threat due to the incompatibility of phone use and driving when the traffic light turns green. These two situations were targeted in a roadside observation study, with the aim to investigate the existence of a phone use strategy at the red traffic light and to test its effectiveness. N = 124 phone users and a corresponding control group of non-users were observed. Strategic phone use behaviour was detected for visual-manual interactions, which are more likely to be initiated at the red traffic light and tend to be stopped before the vehicle moves off, while calls are less likely to be limited to the red traffic light situation. As an indicator of impaired situation awareness, delayed start was associated to phone use and in particular to visual-manual interactions, whether phone use was interrupted before moving off or not. Traffic light situations do not seem to allow effective application of phone use strategies, although drivers attempt to do so for the most demanding phone use mode. The underlying factors of phone use need to be studied so as to reduce the temptation of phone use and facilitate exposure regulation strategies.

An experiment was conducted in a driving simulator to test how eye-movements patterns evolve over time according to the decision-making processes involved in a driving task. Participants had to drive up to crossroads and decide to stop or not. The decision-making task was considered as the succession of two phases associated with cognitive processes: Differentiation (leading to a prior decision) and Consolidation (leading to a final decision). Road signs (Stop, Priority and GiveWay) varied across situations, and the stopping behavior (Go and NoGo) was recorded. Saccade amplitudes and fixation durations were analyzed. Specific patterns were found for each condition in accordance with the associated processes: high visual exploration (larger saccade amplitudes and shorter fixation durations) for the Differentiation phase, and lower visual exploration (smaller saccades and longer fixations) for the Consolidation phase. These results support that eye-movements can provide good indexes of underlying processes occurring during a decision-making task in an everyday context.

The aim of the present study is to identify when drivers perceive that they are overloaded by an unexpected event, as a function of the situation complexity and their driving practice. The main contribution of this paper to the Cognitive Ergonomics field is that the experimentation allows identifying several factors which show that drivers’ activity is not always adapted to unexpected situations. Fifty-seven young drivers (15 novices with a traditional driving education, 12 early-trained novices, 15 drivers with three years of experience and 15 drivers with at least five years of experience) were randomly assigned to three levels of situation complexity (simple, moderately complex and very complex) in a driving simulator. Self-reported levels of workload during unexpected pedestrian crossings were collected by a questionnaire (NASA-TLX) between each situation. Driving performance (reaction time to a pedestrian crossing that suddenly appears; number of collisions with this pedestrian) was also analysed. The experiment assessed the effect of four levels of driving experience and three levels of situation complexity on subjective workload and driving performance. Results confirmed that early-trained drivers have a higher subjective workload than more experienced drivers. Nevertheless, whatever the situation and the group, the increase of workload and RT provoke an increase of the number of collisions. Therefore, the driving automation acquired with experience doesn’t allow avoiding accidents when an unexpected event appears. Subjective and physiological data will be compared in a second study in order to identify if drivers’ behavior is more based on their state perception or on their physiological change.

Background : Use of cellular phones have been shown to be associated with crashes but many external distractions remain to be studied. Objective : To assess the risk associated with diversion of attention due to unexpected events or secondary tasks at the wheel. Design : Responsibility case-control study. Setting : Adult emergency department of the Bordeaux University Hospital (France) from April 2010 to August 2011. Participants : 955 injured drivers presenting as a result of motor vehicle crash. Main outcome measures : The main outcome variable was responsibility for the crash. Exposures were external distraction, alcohol use, psychotropic medicine use, and sleep deprivation. Potential confounders were sociodemographic and crash characteristics. Results : Beyond classical risk factor found to be associated with responsibility, results showed that distracting events inside the vehicle (picking up an object), distraction due to driver activity (smoking) and distracting events occurring outside were associated with an increased probability of being at fault. These distraction-related factors accounted for 8% of injurious road crashes. Limitations : Retrospective responsibility self-assessment Conclusions : Diverted attention may carry more risk than expected. Our results are supporting recent research efforts to detect periods of driving vulnerability related to inattention.

The visibility level (VL), a quality index in road lighting design, also serves to assess automotive lighting performance. Unlike illuminance levels, the VL provides a link between lighting design and driving performance. The reference performance of the VL is the detection of a small uniform target, standing on a uniform background (the road surface). Still, VL is subject to a number of caveats with regards to the driving task. We address the non-realistic nature of the reference target (a uniform square). A driving simulation experiment was conducted in a virtual night-time rural highway environment. The subjects drove along the road and came across different targets on the roadside. The detection distances were recorded and compared with the corresponding VL, showing rather good agreement.

Driving style can have a significant contribution to the noise radiated by individual vehicles. It has been reported that noisy drivers can radiate maximum noise levels which exceed the noise level of the mean traffic stream by 10 dB. Since regulations in most countries are based on averaged overall levels, it is rather difficult to discriminate and control the contribution of noisy drivers to the road traffic noise. In previous studies, an on-board measurement system was developed to evaluate engine and rolling noise levels in the vehicle near field. This system proved to be able to discriminate the noisiest vehicles in different environments and for different driving behaviors. We demonstrated that vehicles with aggressive drivers radiate to the near field noise levels 5-9 dB higher than the average of vehicles with normal drivers. However, most noise regulations are based on noise levels measured in the far field. Thus, we propose here a complete procedure for the extrapolation of the near field noise levels to the far field with a combination of analytical predictions and experimental measurements. Near and far field noise levels were concurrently measured on several trials including vehicle pass-by runs at constant velocities, and under aggressive acceleration and deceleration. An excellent agreement is obtained between the noise levels measured in the far field and these extrapolated from the near field.

Rationale: Some recent pharmacoepidemiological studies revealed an elevated risk of driving accidents after opioid analgesics uses. Among analgesics, codeine is often associated with paracetamol in numerous pharmaceutical specialties. Objectives: The objective of this study was to evaluate the dose-effect relationship of three usual therapeutic doses of codeine/paracetamol on driving ability, psychomotor performance, subjective alertness, in link with blood concentrations in healthy young volunteers. Methods: Driving performance, responses to psychomotor vigilance tests and scales reflecting alertness were evaluated during the morning after drug intake in a double-blind, randomized, placebo-controlled study. Sixteen healthy volunteers [23.4 + 2.7 years old, 8 men and 8 women] participated in this balanced, cross-over study. Three doses of codeine/paracetamol [20/400, 40/800, 60/1200 mg] were evaluated against placebo. Two blood samples were collected, 1 hour and 4 hours after drug intake. In serum, codeine, morphine concentrations were determined in serum using high-performance liquid chromatography electrospray ionization-tandem mass spectrometry, and paracetamol concentrations using fluorescence polarization immunoassay. Results: Driving and psychomotor performance were not affected by any of the three codeine/paracetamol doses. However, significant, though modest, correlations were observed between the driving parameters and both morphine and codeine blood concentrations. Conclusions: This study did not reveal any significant impairment in performance due to the three therapeutic doses used in healthy young volunteers. However, the relationships between drug blood concentration and behavioral measures suggest that an inter-subject variability in blood concentration may influence the power of the observed drug effect.

While there is a large body of research indicating that individuals with moderate to severe dementia are unfit to drive, relatively little is known about the driving performance of older drivers with mild cognitive impairment (MCI). The aim of the current study was to examine the driving performance of older drivers with MCI on approach to intersections, and to investigate how their healthy counterparts perform on the same driving tasks using a portable driving simulator. Fourteen drivers with MCI and 14 age-matched healthy older drivers (aged 65-87 years) completed a 10-min simulator drive in an urban environment. The simulator drive consisted of stop-sign controlled and signal-controlled intersections. Drivers were required to stop at the stop-sign controlled intersections and to decide whether or not to proceed through a critical light change at the signal-controlled intersections. The specific performance measures included; approach speed, number of brake applications on approach to the intersection (either excessive or minimal), failure to comply with stop signs, and slower braking response times on approach to a critical light change. MCI patients in our sample performed more poorly than controls across a number of variables. However, because the trends failed to reach statistical significance it will be important to replicate the study using a larger sample to qualify whether the results can be generalised to the broader population.

OBJECTIVES: In some driving schools in France, several trainers may successively train a single trainee. This situation can be described as a case of asynchronous collaboration. In this paper, we examine the consequences of this situation in two different studies: the first explores the trainers' activity while the second concerns the trainees. PARTICIPANTS: The participants in the first study were 6 trainers with different levels of experience, each working with 2 different trainees. Four of them met their trainees for the very first time. The second study included 150 trainees from 13 driving schools in Paris. METHODS: In the first study, the driving lessons were recorded and fully transcribed before a thematic analysis was conducted. In the second study, the trainees completed a questionnaire. We collected both socio-demographic and driver training data. RESULTS: In the first study, we observed that changing trainers during a trainee's driving course had an impact on the trainers' activity. We identified difficulties in establishing a diagnosis of the trainee's progress, and the organization of the driving lesson was altered. The second study highlighted the fact that changing trainers at the beginning of a course increased the duration of the training period. CONCLUSION: The impacts of changing trainers during a trainee's driving course are discussed with regard to both the trainers' scaffolding activity and the trainees' skill development. Language: en

Road safety, whatever the considered environment, relies heavily on the ability to detect and track moving objects from a moving point of view. In order to achieve such a detection, the vehicle's ego-motion must first be estimated and compensated. This issue is crucial to complete a fully autonomous vehicle; this is why several approaches have already been proposed. This study presents a method, based solely on visual information that implements such a process. Information from stereo-vision and motion is derived to extract the vehicle's ego-motion. Ego-motion extraction algorithm is thoroughly evaluated in terms of precision and uncertainty. Given those statistical attributes, a method for dynamic objects detection is presented. This method relies on 3D image registration and residual displacement field evaluation. This method is then evaluated on several real and synthetic data sequences. It will be shown that it allows a reliable and early detection, even in hard cases (e.g. occlusions,...). Given a few additional factors (detectable motion range), overall performances can be derived from visual odometry performances.

This research was carried out as part of the French national multidisciplinary research project, PREDIT-SARI. Using a driving simulator, it aimed to test the effectiveness of road treatments intended to inform drivers about the risk of losing control on rural roads with "crest vertical curves" (Landis et al., 2004). [Rosey, F., Auberlet, J.M., Bertrand, J., Plainchault, P., 2008. Impact of perceptual treatments on lateral control during driving on crest vertical curves: a driving simulator study. Accid. Anal. Prev. 40, 1515-1523, Scopus.] used a fixed-base driving simulator to test four perceptual treatments intended to help drivers maintain lateral control when driving on crest vertical curves and found that two of them, rumble strips on both sides of the centerline and sealed shoulders, were more effective than the others. This first study prompted us to ask if non-visual sensory cues (e.g., vestibular or proprioceptive perceptions) could influence driver perception and consequently lateral control. We therefore conducted a second study on a motion-base driving simulator, using the same virtual 3D database. The results showed that: (1) drivers drive closer to the center of their lane when there are rumble strips on both sides of the centerline, or when there are sealed shoulders, than they do with the current marking system (i.e., continuous lines), and (2) the impact of the two tested perceptual treatments was replicated on both types of driving simulator.

International audience; This study examines risk comparative judgments and risky behaviors while driving a car among competitive road cyclists (n = 119) and among controls (i.e., drivers who do not have almost no cycling experience, n = 142). A questionnaire-based cross-sectional survey was conducted. Results showed that competitive road cyclists assess their own vulnerability to be involved in an accident while riding (VAR) as being lower than that of the average cyclists, and their abilities to manage risks while riding (AMRR) as being higher. They assessed their own vulnerability to be involved in an accident while driving a car (VAD) as being lower than that of the average drivers, and their own quality of reflexes while driving a car (QRD) as being higher. Their tendency to express comparative optimism while assessing their comparative VAD and QRD was higher than that of controls. They also reported more prudent behaviors while driving a car than did controls. Results are discussed, as well as implications in terms of prevention.