Safe and feasible motion generation for autonomous driving via constrained policy net

Policy networks have great potential to learn sophisticated driving policy under complicated interaction between human drivers. However, it is hard for policy networks to satisfy safety and feasibility constraints, which is not a challenging task for conventional motion generation methods, such as optimization-based approach. In this paper, we propose Constrained Policy Net (CPN), which can learn safe and feasible driving policy from arbitrary inequality-constrained optimization-based expert planners. Instead of supervised learning with L
₂
norm as the loss, we incorporate the domain knowledge of the expert planner directly into the training loss of the policy net by applying barrier functions to the safety and feasibility constraints of the optimization problem. An exemplar scenario with obstacles on both sides is used to implement the proposed CPN. Test results demonstrate that the policy net can learn to generate motions near boundaries of safety and feasibility constraints to achieve high driving quality as the baseline optimization while the constraints are satisfied.