Trajectory planning based on spatio-temporal reachable set considering dynamic probabilistic risk

Trajectory planning in complex traffic situations has always been a challenging task for intelligent vehicles. Comparing to the decoupling method, the spatio-temporal trajectory planning method owns more flexibility and reasonability due to the combination of lateral and longitudinal motion. However, it still has some shortcomings, such as unreasonable risk assessment, high computational complexity and heavy dependence on other models for generating target points. Therefore, a novel spatio-temporal based trajectory planning framework considering probability risk is proposed in this paper. Firstly, a GNN-LSTM based on trajectory prediction algorithm is presented in terms of risk analysis, and particularly the predicted trajectories and the vehicle dynamic model are combined for risk assessment. Secondly, a rough-fine hierarchical planning framework based on reachable set and dynamic programming is proposed. In this framework, the reachable set is taken as spatio-temporal node to reduce the computational costs and dynamic programming can help the algorithm to eliminate the dependence of other models in evaluating target points. Finally, a traffic scenario with random interactive obstacles is built and tested on a HIL platform. The experimental results show that compared with other typical algorithms, the average driving efficiency, driving risk behavior and driving comfort of the vehicle are significantly improved.