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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.

Introduction : In Brazil, driver aggressiveness in road traffic is a critical issue and could be an important contributing factor to the high number of traffic accidents . Because no instruments are available in Portuguese to register driving aggressiveness or driving anger in Brazil, we adapted English instruments into the Brazilian context. The aims of this study were to provide a Brazilian adaptation of the Driving Anger Expression Inventory (DAX) and to try to validate it by testing its psychometric properties and investigating its relationships with risky driving behaviors (DBQ), road accidents, driving sensation seeking, and hostility. Method: The Brazilian adaptations of the DAX, DBQ, the Driving Sensation Seeking Scale (DSSS) and the hostility Scale were administered to a sample of 512 undergraduate students (with a mean age of 23.7 years, 52.1% men). Results : Confirmatory factor analysis of the Brazilian DAX (DAX-BR) items yielded a four-factor solution with 43 items, which obtained the best goodness-of-fit to the data. Cronbach’s alpha for the DAX-BR factors ranged from 0.69 to 0.88. Other results on validity were a positive correlation (range 0.39–0.59) between the factors of the DAX-BR, DSSS, and DBQ. Conclusion : DAX-BR as the same structure as the original and is a reliable instrument for use with young drivers. Other studies should be conducted to further validate the DAX-BR in different types of populations such as older and more experienced drivers, professional drivers, and traffic regulation offenders whose driver's license has been taken away. Practical applications: This Brazilian version can be recommended for the assessment of driving anger expression in Brazil among young drivers in view of helping them driver more safely, and in particular to reduce traffic violations.

21st Congress of the International Ergonomics Association (IEA 2021), VANCOUVER, CANADA, 14-/06/2021 - 18/06/2021; International audience; Using an integrative user-centered approach, the research project aimed at designing and assessing an adaptive HMI to improve the safety and the quality of the take-over in a level-3 automated vehicle. Future autonomous driving users confirmed the need for the monitoring systems to improve safety as long as they are simple and not intrusive. They request specific information to understand and initiate appropriated actions in case of a critical situation. However, to avoid overloading and detri-mental effects, it is important that the system limits the amount of infor-mation at the right moment.

Driving anxiety can have a significant impact on one’s quality of life, particularly for those who experience intense levels. However, to date there are limited data about the perceived impact of driving anxiety on occupational life, and driving anxiety remains under-researched in France. To address this gap, an online survey assessing self-reported driving anxiety, how it manifests and its links with some aspects of personal and occupational life was designed. The survey was administered in France to respondents who identified as experiencing driving anxiety. Responses were obtained from 304 people aged 18 years or older. In the sample, 32.2%, 44.7% and 23% respectively reported mild, moderate and extreme levels of driving anxiety. In the extremely anxious group, males were under-represented, whereas people aged 35–44 and unlicensed drivers were over-represented. People who were unemployed (excluding students and retired people), reported that their anxiety had been a barrier to getting a job and a high percentage of these respondents had also previously considered leaving a position because of their anxiety. Respondents also reported their anxiety had a negative impact on their perceived quality of life in general. People who categorised as extremely anxious reported a negative impact four times higher than those in the mildly anxious group. The results highlight for the first time the perceived impact of driving anxiety on daily and occupational life in a French population. Further, the results demonstrate the importance of distinguishing between moderately and extremely anxious drivers as the intensity level has differential impacts on personal and work life quality.

Objectives Many older drivers incorrectly estimate their driving ability. The present study aimed to determine whether, and if so, to what extent unawareness of cognitive abilities affects self-awareness of driving ability. Method Two successive studies were conducted in older drivers. A cohort study investigated cognitive self-awareness and an experimental study examined driving self-awareness. In each one, self-awareness was assessed by cross-analyzing objective (respectively Trail-Making Tests A and B and the Digit Symbol Substitution Test and driving performance of on-road assessment) and subjective data (responses about everyday cognitive skills and driving ability). Older drivers were then classified as being over-, correct, or underestimators. The 3 cognitive and driving self-awareness profiles were then cross-analyzed. Results In the cohort study, 1,190 drivers aged 70 years or older were included. The results showed that 42.7% of older drivers overestimated their cognitive ability, 42.2% estimated it correctly, and 15.1% underestimated it. The experimental study included 145 participants from the cohort. The results showed that 34% of participants overestimated, 45% correctly estimated, and 21% underestimated their driving ability. There was a significant relationship between cognitive and driving self-awareness profiles (p = .02). This overlap was more marked in overestimators. Discussion Significant overlap between cognitive and driving self-awareness provides useful and new knowledge about driving in the aging population. Misestimation of cognitive ability could hamper self-awareness of driving ability, and consequently self-regulation of driving. It is now crucial to develop measures that promote self-awareness of ability.

Introduction: Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data. Results: The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.

This study aimed at investigating how driver's mental workload could be assessed during driving, using driving performance as well as electrophysiological and subjective data. Participants had to follow a lead vehicle at a safe and constant distance and to deal with two particular driving events (overtaking and pedestrian occurrence) within two sessions (baseline and experimental) on a driving simulator. Traffic density and time pressure (overtaking event) and time pressure (pedestrian event) were increased in the experimental session in order to induce a higher workload. Participants filled NASA TLX questionnaire after each driving session. Electrophysiological parameters (SCL, ECG), driving performance (SDLP and response to speed change of the lead vehicle: coherence, delay and gain) were analysed after each event in two temporal windows (30 sec and 5 min). Results showed that both performance and physiological variables differed as a function of traffic conditions and time pressure. Moreover, while performance variations were systematically observed over a long period (five minutes after the events), effects on mean SCL data obtained from experimental session notably differed from baseline values within thirty seconds after the events. Results are discussed in term of mental workload and suggestions are made about the safety systems that could monitor driver's mental state.

Lane Departure Warning Systems (LDWS) are known to be effective at preventing lane departures. The current study aimed to further investigate the influence of LDWS incorrect warnings along with the warning onset on driving performances. Performances were considered during missed warned lane departure episodes, as well as correctly warned lane departure episodes following incorrectly warned (false or/and missed warning) episodes. In the reported experiment, the incorrect warnings order of presentation was manipulated along with the warning onsets (partial and full lane departure). Results pointed out that a missed warning is impairing driving performances during the missed warned situation, whereas a false warning is impairing driving performances for the subsequent lane departure. Moreover, if false warning impairment is magnified by the occurrence of a subsequent missed warning, this multiplicative effect is restricted to the following lane departure only. These findings bring both theoretical and practical insights. From a theoretical perspective, the human-machine cooperation LDWS model was refined by the adjunction of a transitional influence of incorrect warnings on the information processing. From a practical perspective, the recommendation to avoid false warnings as much as missed warnings was made.

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.

Vehicle-dynamics data, now more readily available thanks to moderate-cost, embedded data logging solutions, have been used to study drivers' behavior (acceleration, braking, and yaw rate) through naturalistic driving research aimed at detecting critical safety events. In addition, self-reported measures have been developed to describe these events and to assess various individual risk factors such as sensation seeking, lack of experience, anger expression while driving, and sensitivity to distraction. In the present study, we apply both of these methods of gathering driving data in order to assess risk proneness as accurately as possible. Data were obtained from 131 drivers, who filled in an introductory questionnaire pertaining to their driving habits. Their vehicles were equipped with an external, automatic data-capture device for approximately two months. During that period, the participants reported critical safety events that occurred behind the wheel by (a) pressing a button connected to the device and (b) describing the events in logbooks. They also filled in weekly questionnaires, and at the end of the participation period, a final questionnaire with various self-reported measures pertaining to their driving activity. We processed the data by (a) performing a multiple correspondence analysis of the characteristics assessed via the automatic data capture and self-reports, and (b) categorizing the participants via hierarchical clustering of their coordinates on the dimensions obtained from the correspondence analysis. This allowed us to identify a group of drivers (n=43) at risk, based on several self-reported measures, in particular, their recent crash involvement, and the frequency of critical acceleration/deceleration events as an objectivemeasure. However, the at-risk drivers did not themselves report more critical safety events than the other two groups.; Les recherches relatives au risque routier et ses antécédents se sont couramment appuyées sur l'analyse des accidents, des sanctions pour infractions et(ou) des comportements à risque. Peu d'entre elles se sont intéressées aux « presque accidents ». L'on entend par presque accident, un événement dangereux lequel, sans action corrective de la part d'autrui ou de soi-même, aboutirait à un accident. Dans le cadre du projet MEDOC, nous avons essayé d'apporter un éclairage sur les presque accidents se produisant au volant des automobilistes et d'identifier les automobilistes les plus exposés au risque. Croisant des mesures déclaratives et des mesures objectives, nous nous sommes appuyés sur un ensemble d'indicateurs 1) les variables liées à la dynamique du véhicule et aux actions de conduite et 2) les variables liées aux déclarations des participant(e)s pour décrire ces presque accidents et identifier les facteurs de risque individuels. Nous avons examiné ces résultats auprès d'un échantillon composé de 131 automobilistes (52,7 % d'hommes, en moyenne âgés de 39,7 ans, ayant le permis voiture depuis 19,2 ans et parcourant au volant 16 606 km par an) les presque accidents qu'ils ont rencontrés au cours de deux mois de conduite. Nous avons identifié trois groupes d'automobilistes en particulier un groupe d'automobilistes (n = 43) à risque (plutôt jeunes, masculins, impliqués dans des accidents de la route, ayant perdu beaucoup de points, chercheurs de sensations fortes et sensibles à la distraction, utilisateurs de leur véhicule pour exprimer leur colère, qui ont des occurrences plutôt élevées de presque accidents de type accélération).

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.

Introduction The development of skills essential for avoiding crashes depends, in particular, on how drivers explain the causes of dangerous driving behaviors that resulted in a near crash. Here, we analyze causes attributed to such behaviors by car drivers in a self-report study. We explore the relationships between the dimensions of causal attribution, attribution of responsibility for the near crash, and drivers' comparative judgments. Method For approximately two months, drivers used logbooks to document the near crashes that occurred during their trips. The causes attributed in those reports to driving behaviors resulting in near crashes were then coded by two judges on the basis of several causal dimensions. Drivers also estimated their own and an average driver's skill levels, and their risk of being involved, as a driver, in a crash. Results We distinguished main types of causes of the near crashes reported. Drivers had a tendency to more often attribute external causes to their own behaviors resulting in near crashes than to those of others. The probability of attributing a controllable cause increased with overestimation of one's own skills and decreased with underestimation of one's own risk in comparison to other drivers. The probability of attributing a stable cause increased with underestimation of one's own risk. Conclusions When they explained their own behaviors resulting in near crashes, drivers mentioned different causes than when they explained those of others. Overestimation of one's own skills as compared to other drivers could be beneficial for developing crash-avoiding skills, insofar as it seems to foster attribution of controllable causes. By contrast, underestimation of one's own risk could have the opposite effect. Practical applications Vulnerability to road risks should be stressed in driver’s training and risk communication campaigns. However, self-confidence with respect to one's skills should not always be targeted as a safety problem.

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 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.

Rationale: Alcohol is the most frequently detected substance in fatal automobile crashes, but its precise mode of action is not always clear. Objective: The present study was designed to establish the influence of blood alcohol concentration as a function of the complexity of the scenarios. Road scenarios implying automatic or controlled driving performances were manipulated in order to identify which behavioral parameters were deteriorated. Method: A single blind counterbalanced experiment was conducted on a driving simulator. Sixteen experienced drivers [25.3 ± 2.9 years old, 8 men and 8 women] were tested with 0, 0.3 g/l, 0.5 g/l and 0.8 g/l of alcohol. Driving scenarios varied: road tracking, car following and an urban scenario including events inspired by real accidents. Statistical analyses were performed on driving parameters as a function of alcohol level. Results: Automated driving parameters such as standard deviation of lateral position [SDLP] measured with the road tracking and car following scenarios were impaired by alcohol, notably with the highest dose. More controlled parameters such as response time to braking and number of crashes when confronted with specific events [urban scenario] were less affected by the alcohol level. Conclusion: Performance decrement was greater with driving scenarios involving automated processes than with scenarios involving controlled processes.

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.

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

We examined older adults' self-regulation within the region of proximal learning (RPL) framework. Younger and older drivers completed four circuits of increasing difficulty in a driving simulator and were then given a limited amount of time to train for a test. Although older drivers chose to train on easier circuits than younger ones, both age groups focused on the easier circuits first, only moving to the more difficult ones later. They were thus equally able to identify their RPL. This framework appears to apply beyond the obvious scope of metamemory and provides a behavioral assessment of self-regulation in driving settings.

Gender differences in accidentology, notably on roads, are well documented and current research in social psychology tends to explain these differences by gender stereotypes, notably the association of risk-taking with social expectations concerning masculinity. To date, however, little research has explored gender stereotypes associated with vehicle driving. Beliefs about driving by men and women, as well as the effect of the age and gender of the perceiver, were explored using the free association method with 599 preadolescents and adolescents between 10 and 16 years of age. The results show that gender stereotypes are indeed associated with driving from the age of 10. While the representation of male drivers is already stable at this age, the representation of female drivers appears to develop with age. Furthermore, there is a notable in-group serving bias, but only among girls. The results are discussed in terms of an essentialist representation of genders, in-group / outgroup relations, age differences in gender stereotypes associated with driving, and practical consequences on driver's training and socialization to risk-taking.

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.