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008			230204s2022 sz | s |||| 0|eng d
020			_a9783031015090
024	7		_a10.1007/978-3-031-01509-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aHE192.5 _b2022 EB
100	1		_aSong, Xiaolin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686103
245	1	0	_aBehavior Analysis and Modeling of Traffic Participants _cby Xiaolin Song, Haotian Cao
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XII, 160 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Advances in Automotive Technology _x2576-8131
505	0		_aAcknowledgments -- Introduction -- Trajectory Prediction of the Surrounding Vehicle -- Predictions of the Intention and Future Trajectory of the Pedestrian -- Driver Secondary Driving Task Behavior Recognition -- Car-Following Driving Style Classification -- Driving Behavior Analysis Based on Naturalistic Driving Data -- Bibliography -- Authors' Biographies.
520			_aA 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) and Strategic 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.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9139914 _aTransportes _xModelos matemáticos
700	1		_aCao, Haotian _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686100
776	0	8	_iPrinted edition: _z9783031000133
776	0	8	_iPrinted edition: _z9783031003813
776	0	8	_iPrinted edition: _z9783031026379
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01509-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI