TY - GEN
T1 - Pedestrian Behavior Prediction Method for Intelligent Vehicles Based on Convolutional Neural Network
AU - Gao, Hongbo
AU - He, Xi
AU - Wang, Liuchang
AU - Zhang, Fei
AU - Cai, Kaiquan
AU - Fang, Xiaozhao
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Convolutional neural network has excellent representation learning ability, which makes it unique in the field of behavior prediction. This paper presents a prediction method of pedestrian behavior around intelligent vehicles, which makes use of the advantages of convolutional neural network, combines pedestrian intention and road environment information to predict pedestrian behavior around intelligent vehicle, and optimizes pedestrian avoidance module in automatic driving system. The experimental results show that the area of the closed graph composed of the predicted trajectory and the actual trajectory is 0.1269 m2. The method proposed in this paper can effectively predict the pedestrian behavior trajectory, ensure the safety of drivers and pedestrians to the maximum extent, and provide a new solution for intelligent vehicles and intelligent driving path planning.
AB - Convolutional neural network has excellent representation learning ability, which makes it unique in the field of behavior prediction. This paper presents a prediction method of pedestrian behavior around intelligent vehicles, which makes use of the advantages of convolutional neural network, combines pedestrian intention and road environment information to predict pedestrian behavior around intelligent vehicle, and optimizes pedestrian avoidance module in automatic driving system. The experimental results show that the area of the closed graph composed of the predicted trajectory and the actual trajectory is 0.1269 m2. The method proposed in this paper can effectively predict the pedestrian behavior trajectory, ensure the safety of drivers and pedestrians to the maximum extent, and provide a new solution for intelligent vehicles and intelligent driving path planning.
KW - Convolution Neural Network
KW - Deep Learning
KW - Intelligent Drive
KW - Intelligent Vehicles
KW - Pedestrian Trajectory Prediction
UR - https://www.scopus.com/pages/publications/85146490252
U2 - 10.1109/ICUS55513.2022.9987009
DO - 10.1109/ICUS55513.2022.9987009
M3 - 会议稿件
AN - SCOPUS:85146490252
T3 - Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
SP - 787
EP - 792
BT - Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
Y2 - 28 October 2022 through 30 October 2022
ER -