Autonomous overtaking decision and motion planning of intelligent vehicles based on graph convolutional network and conditional imitation learning

To ensure safe overtaking of intelligent vehicles in dynamic interactive environments, this paper proposes an end-to-end learning method for autonomous overtaking based on Graph Convolutional Network (GCN) and Conditional Imitation Learning (CIL). This method completes the autonomous overtaking behavior by directly mapping the environmental perception data to the underlying vehicle control actions (e.g. throttle and steer angle). This method fully considers the influence of other vehicles' driving behavior on the overtaking behavior of the ego vehicle. Firstly, the dynamic interactive environments information around the ego vehicle is aggregated in the form of graph-structured data, and the aggregated global features are used as the input of the GCN to output the optimal action instructions that the ego vehicle should take. Secondly, combined with CIL, the action instructions output by the GCN are used as high-level commands to guide CIL. Finally, combined with other perception data, the underlying control actions of the vehicle will be output by CIL to complete safe overtaking in dynamic interactive environments. The method proposed in this paper can effectively extract the global information of the driving scene and complete the collision-free autonomous overtaking behavior, which greatly improves the intelligence of the driving system. The feasibility of the method has been verified by experiments on the CARLA simulation platform. The experimental results prove that the performance of this method is better than that of the conventional end-to-end learning framework, and it has better success rate and generalization performance.