基于 GCN 和 CIL 的端到端自动驾驶换道方法

For the lane change of autonomous driving, to solve the problems of unstable output and difficulty to extract dynamic interactive scene feature in conventional end-to-end method, an end-to-end learning method for autonomous lane change based on graph convolutional network(GCN)and conditional imitation learning(CIL)is proposed in this paper. Firstly, the dynamic interactive information of driving scenarios is aggregated in the form of graph-structured data. Secondly, the driving behavior instructions that the ego vehicle should take are output through GCN, which is then combined with CIL. The driving instructions output by GCN are taken as high-level commands for guiding CIL, and are finally mapped to underlying control actions of the vehicle with other perception data to complete autonomous lane change without collision. Experimental verification is carried out on CARLA simulation platform. The experimental results prove that the performance of this method is better than that of conventional end-to-end method, and it has better success rate and generalization performance.