TY - GEN
T1 - A Coalitional Autonomous Guiding Model Considering Traffic and Non-traffic Participants
AU - Liu, Xiaochuan
AU - Du, Haohua
AU - Ji, Fenzhu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the field of autonomous driving, there is a lot of work focused on how to more accurately predict the trajectory of traffic participants and make reasonable and safe decisions based on the behavior of the surrounding agents. However, non-traffic participants are also a very important part of the traffic interaction network, the traffic lights have a non-negligible impact on the prediction module and the decision-making module of autonomous driving. In this paper, the focus of attention is on the impact of traffic lights on the performance of autonomous driving. We propose a new decision-making model GTL that include a trajectory prediction module, it can effectively utilize the non-traffic elements. The multi-objective trajectory prediction model HEAT_E improved the prediction accuracy by 43.07% and 23.79% on the SinD and CitySim datasets compare with baseline, respectively. GTL shared the latent representation of trajectory predict context between HEAT_E and decision-making module. GTL outperforms the baseline model across the board.
AB - In the field of autonomous driving, there is a lot of work focused on how to more accurately predict the trajectory of traffic participants and make reasonable and safe decisions based on the behavior of the surrounding agents. However, non-traffic participants are also a very important part of the traffic interaction network, the traffic lights have a non-negligible impact on the prediction module and the decision-making module of autonomous driving. In this paper, the focus of attention is on the impact of traffic lights on the performance of autonomous driving. We propose a new decision-making model GTL that include a trajectory prediction module, it can effectively utilize the non-traffic elements. The multi-objective trajectory prediction model HEAT_E improved the prediction accuracy by 43.07% and 23.79% on the SinD and CitySim datasets compare with baseline, respectively. GTL shared the latent representation of trajectory predict context between HEAT_E and decision-making module. GTL outperforms the baseline model across the board.
KW - Deep Reinforcement Learning
KW - Multi-agent trajectory prediction
KW - Non-traffic participants
UR - https://www.scopus.com/pages/publications/85185890023
U2 - 10.1109/BIGCOM61073.2023.00047
DO - 10.1109/BIGCOM61073.2023.00047
M3 - 会议稿件
AN - SCOPUS:85185890023
T3 - Proceedings - 2023 9th International Conference on Big Data Computing and Communications, BigCom 2023
SP - 293
EP - 300
BT - Proceedings - 2023 9th International Conference on Big Data Computing and Communications, BigCom 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Big Data Computing and Communications, BigCom 2023
Y2 - 4 August 2023 through 6 August 2023
ER -