HyPlan融合学习与规划，提升自动驾驶在不确定环境下的安全与效率。
HyPlan: Hybrid Learning-Assisted Planning Under Uncertainty for Safe Autonomous Driving
- 结合行为预测、强化学习与启发式剪枝的混合规划方法。
- 在CARLA-CTS2上比基线更安全，速度远超传统在线POMDP规划器。
- 适合追求高安全性和实时性的自动驾驶系统研发者。
我们提出一种新型混合学习辅助规划方法HyPlan，用于解决部分可观测交通环境下自动驾驶车辆的避障导航问题。HyPlan融合多智能体行为预测、基于近端策略优化的深度强化学习，以及近似在线部分可观测马尔可夫决策过程（POMDP）规划，并采用基于置信度的启发式垂直剪枝以显著降低执行时间，同时保障驾驶安全性。在包含行人等复杂场景的CARLA-CTS2基准测试中，HyPlan相比选定基线展现出更优的安全性表现，且运行速度显著快于其他考虑的在线POMDP规划方法。
原文摘要 · Abstract (English)
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.
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