Abstract:

Due to the uncertainty of the driver's intention under the mixed traffic flow， the driving trajectory will present multimodal attributes. In order to improve safety and realize personalized driving， a trajectory planning algorithm for intelligent vehicle based on the multimodal trajectory prediction of environmental vehicles is proposed in this paper. Firstly， a trajectory prediction model is established by combining graph convolutional neural network （GCN） and long short-term memory network （LSTM） with attention mechanism to predict the probability distribution of future trajectories under different types of driving intention. Then， for the set of predicted trajectories under multi-intention probabilities of environmental vehicles， a certain probability threshold is set to select sure trajectories according to the automatic driving style preference， which is projected onto the planning path to generate the S-T diagram， and speed planning based on collision risk avoidance is carried out through dynamic planning and quadratic planning. Finally， based on the model predictive control （MPC）， the model proposed in this paper is simulated and tested in typical lane changing scenarios and real-road scenarios of NGSIM and compared with the existing model for validation. The results show that the model proposed in this paper is better than the model in comparison in terms of safety， comfort， and driving efficiency， which can realize the optimal trajectory planning under the premise of accurately predicting future trajectories of the environmental vehicles to ensure safe and efficient driving of autonomous vehicles.

Key words: autonomous driving, trajectory planning, multimodal trajectory prediction, driving intention