We present a novel hybrid learning-assisted planning method, named HyPlan, for solving the collision-free navigation problem for self-driving cars in partially observable traffic environments. HyPlan combines methods for multi-agent behavior prediction, deep reinforcement learning with proximal policy optimization and approximated online POMDP planning with heuristic confidence-based vertical pruning to reduce its execution time without compromising safety of driving. Our experimental performance analysis on the CARLA-CTS2 benchmark of critical traffic scenarios with pedestrians revealed that HyPlan may navigate safer than selected relevant baselines and perform significantly faster than considered alternative online POMDP planners.

翻译：本文提出了一种新颖的混合学习辅助规划方法——HyPlan，用于解决自动驾驶汽车在部分可观测交通环境中的无碰撞导航问题。HyPlan融合了多智能体行为预测方法、基于近端策略优化的深度强化学习，以及采用启发式置信度垂直剪枝的近似在线POMDP规划技术，在保证驾驶安全的同时显著降低了算法执行时间。我们在包含行人交互的CARLA-CTS2关键交通场景基准测试中进行的实验性能分析表明，相较于选定的相关基线方法，HyPlan能够实现更安全的导航，并且其执行速度显著优于其他在线POMDP规划器。