Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction

Abstract

Due to the challenge that the behavior of traffic participants in the driving environment is highly stochastic and uncertain, it is difficult for self-driving vehicles to make accurate decisions based only on the current environmental state. In this paper, we propose a driving strategy learning method based on spatial-temporal feature prediction. Firstly, the spatial interaction between vehicles is implicitly modeled using a graph convolutional neural network and multi-head attention mechanism, and the gated loop unit is embedded to capture the sequential temporal relationship to establish a prediction model incorporating spatial-temporal features. Then, a reinforcement learning-based driving strategy method is constructed using some of the predictive features of the ego-vehicle and surrounding vehicles as predictive state inputs. Finally, based on the real dataset and CARLA simulation platform, the prediction ability of the prediction model and the effectiveness of the prediction-based decision-making model are verified. The simulation results prove that the prediction algorithm can achieve the minimum error compared with the baseline trajectory prediction algorithm, and effectively improves the accuracy and reliability of the autopilot decision-making in various dynamic scenarios.