TY - GEN
T1 - Risk Avoidance Decision-Making for Intelligent Vehicles Based on Spatio-Temporal Risk Prediction
AU - Yang, Fan
AU - Yang, Chao
AU - Qie, Tianqi
AU - Wang, Weida
AU - Zuo, Yinchu
AU - Wang, Yansong
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In order to improve the collision risk assessment method of intelligent vehicles in complex traffic scenarios and avoid collision risk in real time, this paper proposes a risk avoidance decision-making method for intelligent vehicles based on spatio-temporal risk prediction. Firstly, the risk measurement of spatio-temporal coupling is used as the evaluation index, which supervises the lateral and longitudinal collision risk of vehicles, and monitors the change of driving risk index in real time. Then, based on convolutional neural networks-bidirectional long short-term memory networks (CNN-BiLSTM), the spatio-temporal correlation of risk index is analyzed, and the potential collision risk is predicted to avoid driving risk in advance. The experimental results show that the proposed method can accurately evaluate the lateral and longitudinal risk. Compared with CNN and CNN-LSTM methods, the mean square error of the proposed method in different prediction horizon is reduced by 58.9% and 50.3% on average. Results show that the proposed method can effectively predict the potential collision risk.
AB - In order to improve the collision risk assessment method of intelligent vehicles in complex traffic scenarios and avoid collision risk in real time, this paper proposes a risk avoidance decision-making method for intelligent vehicles based on spatio-temporal risk prediction. Firstly, the risk measurement of spatio-temporal coupling is used as the evaluation index, which supervises the lateral and longitudinal collision risk of vehicles, and monitors the change of driving risk index in real time. Then, based on convolutional neural networks-bidirectional long short-term memory networks (CNN-BiLSTM), the spatio-temporal correlation of risk index is analyzed, and the potential collision risk is predicted to avoid driving risk in advance. The experimental results show that the proposed method can accurately evaluate the lateral and longitudinal risk. Compared with CNN and CNN-LSTM methods, the mean square error of the proposed method in different prediction horizon is reduced by 58.9% and 50.3% on average. Results show that the proposed method can effectively predict the potential collision risk.
KW - decision making
KW - intelligent vehicles
KW - risk prediction
UR - http://www.scopus.com/inward/record.url?scp=85217209589&partnerID=8YFLogxK
U2 - 10.1109/CVCI63518.2024.10830017
DO - 10.1109/CVCI63518.2024.10830017
M3 - Conference contribution
AN - SCOPUS:85217209589
T3 - Proceedings of the 2024 8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024
BT - Proceedings of the 2024 8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th CAA International Conference on Vehicular Control and Intelligence, CVCI 2024
Y2 - 25 October 2024 through 27 October 2024
ER -