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) 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.
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