Behavior Analysis and Modeling of Traffic Participants - Song, Xiaolin;Cao, Haotian
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A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase…mehr

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Produktbeschreibung
A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles. However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the road-driver-vehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition. Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) andStrategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers' demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.
Autorenporträt
Xiaolin Song received her B.E., M.E., and Ph.D. at the College of Mechanical and Vehicle Engineering, Hunan University in 1988, 1991, and 2007, respectively. From 2008 to the present, she has been a professor and a Ph.D. supervisor at Hunan University. She was an advanced visiting scholar of the University of Michigan (Ann Arbor), the University of Waterloo, and the University of Texas at Austin. She is a Vice-Chairman of the Rules Committee of Formula Student China, as well as an Academic Committee Member of the College of Mechanical and Vehicle Engineering, Hunan University. She has been an independent Principal Investigator(PI) and Co-PIs for multiple General Projects of the Natural Science Foundation of China (NFSC) and Hunan Provincial Natural Science Foundation, and dozens of other provincial and ministerial projects or industrial companies. Her research interests include the active safety of intelligent vehicles, vehicle dynamics control, driver behavior modeling, and human factors in driving safety. Haotian Cao is currently a Postdoctoral Fellow at the College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China. He received a B.E. in vehicle engineering and a Ph.D. in mechanical engineering from the College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China, in 2011 and 2018, respectively. He was a visiting scholar at the Human Factors group, the University of Michigan Transportation Research Institute (UMTRI) from 2016-2017. He is currently a committee member of the Chinese Association of Automation Parallel Intelligence (2018-2022), and a referee of over 30 international journals and conferences. He is also the Principal Investigator of Youth Projects funded by the Natural Science Foundation of China (NFSC) and Hunan Provincial Natural Science Foundation, as well as Co-PIs for several projects from the NFSC and industry companies. His interests include trajectory planning and following control for intelligentvehicles, driver models, driver behavior modeling, and naturalistic driving data analysis.