用强化学习指导采样，让自动驾驶更高效可靠
Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
- 用强化学习智能选择采样位置，避开无效轨迹
- 采样量减少99%，运行时间降低84%，成功率不变
- 适合需要快速决策的复杂城市驾驶场景
基于采样的运动规划在自动驾驶中应用广泛，因其模块化和可分析性而受到青睐。但在复杂城市场景中，均匀或启发式采样常产生大量不可行或无关轨迹。本文提出一种混合框架，通过强化学习（RL）代理引导采样过程，聚焦于可能生成可行轨迹的动作空间区域，同时保持轨迹生成与评估的完全解析性与可验证性。采样由基于可解码深度集合编码器的世界模型（WM）支持，可处理不同数量的交通参与者，并重建潜在表示。该方法在CommonRoad仿真环境中评估，相比均匀采样基线，最多减少99%的采样次数，运行时间最多降低84%，且在成功率达和无碰撞率上保持相当水平。该方法显著提升了城市环境中自动驾驶车辆的决策速度与可靠性。
原文摘要 · Abstract (English)
Sampling-based motion planning is a well-established approach in autonomous driving, valued for its modularity and analytical tractability. In complex urban scenarios, however, uniform or heuristic sampling often produces many infeasible or irrelevant trajectories. We address this limitation with a hybrid framework that learns where to sample while keeping trajectory generation and evaluation fully analytical and verifiable. A reinforcement learning (RL) agent guides the sampling process toward regions of the action space likely to yield feasible trajectories, while evaluation and final selection remains governed by deterministic feasibility checks and cost functions. We couple the RL sampler with a world model (WM) based on a decodable deep set encoder, enabling both variable numbers of traffic participants and reconstructable latent representations. The approach is evaluated in the CommonRoad (CR) simulation environment and compared against uniform-sampling baselines, showing up to 99% fewer required samples and a runtime reduction of up to 84% while maintaining planning quality in terms of success and collision-free rates. These improvements lead to faster, more reliable decision-making for autonomous vehicles in urban environments.
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