TY - GEN
T1 - A human-like longitudinal decision model of intelligent vehicle at signalized intersections
AU - Cheng, Wen
AU - Wang, Gang
AU - Wu, Shaobin
AU - Xiong, Guangming
AU - Gong, Jianwei
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Recently there are significant researches on behavior decision of automatic driving vehicle at intersections. However, previous studies pay most attention to the issues of fuel consumption, efficiency and safety, less consider the feelings of occupants and drivers. This paper proposes a Longitudinal Decision Model (LDM) which can take comfort indicator and timeliness indicator into account to get an optimal speed profile based on Markov Decision Process (MDP) at signalized intersections; and a method of trapezoidal speed planning which is designed based on human driving process is embedded in this model. Not only is the model able to consider the immediate effect of the current vehicle's action but also take the long-term influencing factors into account to obtain the optimal traveling scheme of the vehicle. The experimental results show that the model is reliable, and these results of the actual implementation are highly consistent with the planning.
AB - Recently there are significant researches on behavior decision of automatic driving vehicle at intersections. However, previous studies pay most attention to the issues of fuel consumption, efficiency and safety, less consider the feelings of occupants and drivers. This paper proposes a Longitudinal Decision Model (LDM) which can take comfort indicator and timeliness indicator into account to get an optimal speed profile based on Markov Decision Process (MDP) at signalized intersections; and a method of trapezoidal speed planning which is designed based on human driving process is embedded in this model. Not only is the model able to consider the immediate effect of the current vehicle's action but also take the long-term influencing factors into account to obtain the optimal traveling scheme of the vehicle. The experimental results show that the model is reliable, and these results of the actual implementation are highly consistent with the planning.
UR - http://www.scopus.com/inward/record.url?scp=85050633925&partnerID=8YFLogxK
U2 - 10.1109/RCAR.2017.8311897
DO - 10.1109/RCAR.2017.8311897
M3 - Conference contribution
AN - SCOPUS:85050633925
T3 - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
SP - 415
EP - 420
BT - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2017
Y2 - 14 July 2017 through 18 July 2017
ER -