The trajectory prediction is significant for the driving safety of intelligent and connected vehicles. To accurately predict the vehicle trajectory, a hybrid method combining physic-based and data-based methods is proposed for intelligent and connected vehicles. The proposed method applied the physic-based method to represent vehicle kinematics. Then, the error of the physic-based method, which is the unmodeled features, is modeled with the data-based deep learning method using Encoder-Decoder Long short-term memory (LSTM). The proposed method is trained and evaluated by an actual vehicle dataset. When the prediction horizon is 3s, compared with the physic-based method, the longitudinal error, lateral error, and yaw angle error decreased by 93.9%, 86.6%, and 76.0%, respectively. Results show that the proposed method improves the trajectory prediction accuracy of autonomous and connected vehicles.
Trajectory prediction method using deep learning for intelligent and connected vehicles
2023-05-08
432737 byte
Conference paper
Electronic Resource
English
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