000 04952nam a22005655i 4500
001 978-3-031-01509-0
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007 cr nn 008mamaa
008 220601s2022 sz | s |||| 0|eng d
020 _a9783031015090
_9978-3-031-01509-0
024 7 _a10.1007/978-3-031-01509-0
_2doi
050 4 _aTK1-9971
072 7 _aTHR
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTHR
_2thema
082 0 4 _a621.3
_223
100 1 _aSong, Xiaolin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979518
245 1 0 _aBehavior Analysis and Modeling of Traffic Participants
_h[electronic resource] /
_cby Xiaolin Song, Haotian Cao.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXII, 160 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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) 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.
650 0 _aElectrical engineering.
_979519
650 0 _aMechanical engineering.
_95856
650 0 _aAutomotive engineering.
_979520
650 0 _aTransportation engineering.
_93560
650 0 _aTraffic engineering.
_915334
650 1 4 _aElectrical and Electronic Engineering.
_979521
650 2 4 _aMechanical Engineering.
_95856
650 2 4 _aAutomotive Engineering.
_979522
650 2 4 _aTransportation Technology and Traffic Engineering.
_932448
700 1 _aCao, Haotian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979523
710 2 _aSpringerLink (Online service)
_979524
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000133
776 0 8 _iPrinted edition:
_z9783031003813
776 0 8 _iPrinted edition:
_z9783031026379
830 0 _aSynthesis Lectures on Advances in Automotive Technology,
_x2576-8131
_979525
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01509-0
912 _aZDB-2-SXSC
942 _cEBK
999 _c84793
_d84793