Learning Homotopy Prediction for Optimization-Based Trajectory Planners for Autonomous Driving

Abstract Towards the real-world deployment of autonomous vehicles, it is crucial that autonomous driving systems plan safe, collision-free trajectories. However, generating a collision-free trajectory around potential obstacles requires solving a non-convex problem for conventional optimization-based planners. This is characterized by multiple local minima reflecting possible maneuvers in different homotopies that the ego vehicle can execute to avoid collision. Finding a solution among these maneuvers involves combinatorial decision-making, which incurs increasing computational costs as more obstacles are considered. In this paper, we propose a hybrid approach using learning-based decision-making for optimization planning. Our learning-based decision-maker predicts homotopic boundary constraints for the optimization planner, effectively determining a maneuver for the ego vehicle without any combinatorial decision process. In addition, the homotopic bounds enable us to reformulate the non-convex optimization problem into a more tractable quadratic programming (QP) problem. We evaluate our approach in unsignalized intersection scenarios using a simulator, demonstrating that it achieves better driving performance than existing decision-making and planning methods in non-convex driving situations.