HyPlan：在不确定环境下通过混合学习辅助规划实现安全自动驾驶
HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving
AI总结：
HyPlan通过结合多智能体行为预测、深度强化学习和POMDP规划，实现安全且高效的自动驾驶导航
摘要：
我们提出了一种新的混合学习辅助规划方法，名为HyPlan，用于解决在部分可观测的交通环境中自动驾驶汽车的无碰撞导航问题。HyPlan结合了多智能体行为预测方法、深度强化学习与近端策略优化以及基于启发式的置信度驱动的在线POMDP规划进行垂直剪枝，以减少其执行时间而不牺牲驾驶的安全性。我们在CARLA-CTS2基准测试中对关键交通场景中的行人进行了实验性能分析，结果表明HyPlan可能比选定的相关基线方法更安全，并且比考虑的其他在线POMDP规划器显著更快。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
