Time-Optimized Online Planning For Parallel Parking With Nonlinear Optimization and Improved Monte Carlo Tree Search

Abstract

Automatic parallel parking is critical to increase safety in urban narrow parking spots, maximize the traffic efficiency, and provide human drivers with mobility and convenience. Recent research integrates Monte Carlo tree search (MCTS) and artificial neural networks (ANNs) to calculate optimal lateral motions without considering the longitudinal aspect and narrow spots; advances in nonlinear programming-based (NPB) parking methods consider time-optimal parking motion in narrow spots using the time-consuming optimization calculation. To address the computational efficiency of the planning of time-optimal parking maneuvers, a complete framework relying on the two compositions was introduced. First, nonlinear optimization that formulates the minimum motion time and vehicle constraint was used to generate the data of parking motions offline. These motions were subsequently learned by ANNs. Second, the ANNs trained on the offline data were employed by an improved MCTS to generate approximate time-optimal parking motions online. The time-optimized performance and run-time performance of the proposed method were confirmed by comparing with that of NPB and other mainstream methods. A success rate of 100% for parking slots with merely 10% larger length than the vehicle was realized in simulations. Experiments were conducted on a full-sized pure electric vehicle to further confirm the effectiveness of the proposed method.