Editorial: Psychophysiological Contributions to Traffic Safety (original) (raw)
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Transportation Research Procedia, 2020
The authors solve the issue of identifying changes in the psychophysical factors of drivers' health when driving without the use of special body sensors, which significantly expands the possibility of using the method for drivers of any vehicles. The method allows us to determine correlations between the parameters of vehicle movement and the psychophysical state of the driver in the process of preliminary testing. For that purpose, a special mobile application is used making it possible to determine the subject's reactions to dynamic images during a 1-2-minute test and compare the results with the parameters of movement. The processing results represent correlation functions used to identify the signs of the driver's state (fatigue, sickness, etc.), which affect traffic safety, by the parameters of movement. At the same time and in a similar way, factors related to the technical condition of the vehicle are identified. For that purpose, correlation functions between the indicators of movement, obtained at the current moment and during previous trips, are calculated. The method can be used to improve the safety of transportation with the use of both public and individual vehicles (regarding drivers of private cars, mini electric vehicles, etc.).
Psychophysiological Contributions to Traffic Safety
Frontiers Research Topics, 2020
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Characterizing driver behaviors relevant to traffic safety: A multi-stage approach
2013
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Evaluating safe driving behavior in a driving simulator
2017
This paper presents a methodology for evaluation of driving performance based on speeding, acceleration, lane control and safety distance. All these variables are measured in a motion-based driving simulator. We report on a user study in which we obtained the proposed variables for 29 drivers. These results will enable us to propose a general evaluation score of driving performance, which can be used for profiling driver behavior.
Analysis of Drivers' Responses under Hazardous Situations in Vehicle Traffic
2007 IEEE Intelligent Vehicles Symposium, 2007
This study focused on the analysis of drivers' reactions under hazardous scenarios in vehicle traffic. Driving behavior signals were utilized to detect a chain of changes in driver status and to retrieve incidents from a large real-world driving database obtained from the Center for Integrated Acoustic Information Research (CIAIR). All the existing 25 potentially hazardous scenes in the database were handlabeled and categorized. A new feature, based on joint-histograms of these behavioral signals and their dynamics was proposed and utilized to indicate anomalies in driving behavior. Brake pedal force-based method attained a true positive (TP) rate of 100% for a false positive (FP) rate of 4.5%, concerning the detection of 17 scenes where drivers slammed on the brakes. Results stressed the relevance of individuality in drivers' reactions for this retrieval. In 11 of the 25 hand-labeled scenes, drivers reacted verbally. Scenes where high-energy words were present were adequately retrieved by the speech-based detection, which achieved a TP rate of 54% (6 scenes), for a FP rate of 6.4%. In addition, the proposed integration method, which combined brake force and speech signals, was satisfactory in boosting the detection of the most subjectively dangerous situations.
Human Modelling in Assisted Transportation, 2011
Driver behaviour can be modelled in one of two approaches: 'Descriptive' models that describe the driving task in terms of what the driver does, and 'Functional' models that attempt to explain why the driver behaves the way he/she does, and how to predict drivers' performance in demanding and routine situations. Demanding situations elicit peak performance capabilities, and routine situations elicit typical (not necessarily best) behaviour. It seems that the optimal approach might be a hybrid of several types of models, extracting the most useful features of each. In recent years, a variety of driver support and information management systems have been designed and implemented with the objective of improving safety as well as performance of vehicles. To predict the impact of various assistance systems on driver behaviour predictive models of the interaction of the driver with the vehicle and the environment are necessary. The first step of the ITERATE project is to critically review existing Driver-Vehicle-Environment (DVE) models and identify the most relevant drivers' parameters and variables that need to be included in such models: (a) in different surface transport modes (this paper deals with road vehicles only, other transport domains are detailed in D1.1 & D1.2 of the ITERATE project), and (b) in different safety critical situations. On the basis of this review, we propose here a Unified Model of Driver behaviour (UMD), that is a hybrid model of the two approaches. The model allows for individual differences on pre-specified dimensions and includes the vehicle and environmental parameters. Within the ITERATE project this model will be used to support safety assessment of innovative technologies (based on the abilities, needs, driving style and capacity of the individual drivers). In this brief paper we describe only the behaviour of a single test driver, while the environment and vehicle are defined as parameters with fixed values (and detailed in D1.2 of the ITERATE project). The selected driver characteristics (and variables used to measure them) are culture (Country), attitudes/personality (Sensation Seeking), experience (Hazard Perception Skills), driver state (Fatigue), and task demand (Subjective workload).
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 1997
In the future, on-board driver monitoring systems could use time-to-collision (TTC) metric algorithms as a real-time measure of driving performance, and alert the driver if performance falls below minimum performance criteria. Such monitoring systems remain years away, but it is currently possible to measure TTC in a simulator. This paper discusses a study to determine whether TTC varies as a function of driver impairment in a simulated driving task. Alcohol was administered to eleven participants, and TTC measures were obtained at 0.00%, 0.04% and 0.08% blood alcohol concentrations (BAC). The results support use of the median TTC, which varied as a function of BAC, as a measure of in-traffic maneuvering performance.
Frontiers in Neuroscience, 2018
Drivers' hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25-50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver's hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver's hazardous state, which could serve as the basis for more intelligent intervention systems.
Validation of in-car observations, a method for driver assessment
Transportation Research Part A: Policy and Practice, 2004
An in-car observation method with human observers in the car was studied to establish whether observers could be trained to observe safety variables and register driverÕs behaviour in a correct and coherent way, and whether the drivers drove in their normal driving style, despite the presence of the observers. The study further discussed the observed variables from a safety perspective. First three observers were trained in the observation method and on-road observations were carried out. Their observations were then compared with a key representing a correct observation. After practising the observation method the observers showed a high correlation with the key. To establish whether the test drivers drove in a normal way during the in-car observations, comparisons of 238 spot-speed measurements were carried out. DriverÕs speeds when driving their own private cars were compared with their speeds during the in-car observations. The analysis showed that the drivers drove in the same way when being observed as they did normally. Most of the variables studied in the in-car observations had a well documented relevance to traffic safety. Overall, in-car observation was shown to be a reliable and valid method to observe driver behaviour, and observed changes provide relevant data on traffic safety. drivers and by accident statistics Highway Loss Data Institute, 1994). Typically, driver behaviour is studied using driving simulators, instrumented vehicles or human observers inside the vehicle. While all these methods have their advantages,their disadvantages are that they can generate data that is not always reliable or valid.