Graph-Based Scenario-Adaptive Lane-Changing Trajectory Planning for Autonomous Driving

Trajectory planning is one of the key challenges to the rapid and large-scale deployment of autonomous driving. The lane-changing trajectory planning algorithm for autonomous driving is typically formulated as a optimization process of a cost function, which can be challenging to manually tune for different traffic scenarios. This paper presents a graph-based scenario-adaptive lane-changing trajectory planning approach that overcomes this challenge. Specifically, the cost function recovery method based on maximum entropy inverse reinforce- ment learning (IRL) is proposed to recover the cost functions of the all demonstrated lane-changing trajectories, and the cost function database is constructed. Then, the scenario matching model based on spatial-temporal graph convolutional network (ST-GCN) is proposed to match the recovered cost functions with the traffic scenarios, making the lane-changing trajectory planning method scenario-adaptive. Our proposed method is evaluated through simulations on the well-known NGSIM dataset and experiments on two typical lane-changing scenarios on the autonomous driving platform. The results show that our method is capable of learning the lane-changing cost function from demonstration and performing scenario-adaptive lane-changing trajectory planning.