时空遮挡区域中最坏情况隐藏车辆轨迹搜索
Worst-Case Hidden-Vehicle Trajectory Search in Spatiotemporal Occlusion Regions
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中文总结 AI 辅助
针对自动驾驶遮挡不确定性，提出历史条件极小极大轨迹搜索（HC-MTS），结合时间遮挡推理与响应感知搜索，在Waymo数据集上显著减少隐藏种子并识别可避免碰撞场景。
中文摘要 AI 辅助
遮挡在自动驾驶中造成了根本性的不确定性。现有方法通常传播逐帧假设，或针对预设的隐藏智能体预测优化自车行为，而未探索最坏情况下历史一致的交互。我们提出了历史条件极小极大轨迹搜索（HC-MTS），该方法将时间遮挡推理与响应感知搜索相结合。首先，HC-MTS构建有限的隐藏状态模式，每个模式由满足多帧可见性、占用、语义地图支持和类别特定运动学约束的后向见证者认证。随后，它求解一个双层极小极大问题：内层有限预言机在目的地达成和乘坐舒适性上最大化自车驾驶得分，而外层搜索选择使该最优响应值最小化的合法隐藏车辆轨迹。在Waymo开放运动数据集的八个场景中，将可见性记忆范围从K=1增加到K=20，使每个场景的平均车辆、行人和总保留隐藏种子数量分别减少18.12%、21.67%和18.45%。HC-MTS识别出六个可避免的反例，而在剩余两个场景中，在有限搜索预算内未发现产生合法碰撞的攻击者。
英文摘要
Occlusion creates fundamental uncertainty in autonomous driving. Existing methods often propagate frame-wise hypotheses or optimize ego behavior against prescribed hidden-agent predictions, leaving the worst history-consistent interaction unexplored. We introduce History-Conditioned Minimax Trajectory Search (HC-MTS), which combines temporal occlusion reasoning with response-aware search. First, HC-MTS constructs finite hidden-state modes, each certified by a backward witness satisfying multi-frame visibility, occupancy, semantic-map support, and class-specific kinematic constraints. It then solves a bilevel minimax problem: an inner finite oracle maximizes the ego driving score over destination attainment and ride comfort, while the outer search selects the legal hidden-vehicle trajectory that minimizes this best-response value. Across eight Waymo Open Motion Dataset scenarios, increasing the visibility-memory horizon from K=1 to K=20 reduces the mean per-scenario vehicle, pedestrian, and total retained hidden-seed counts by 18.12%, 21.67%, and 18.45%, respectively. HC-MTS identifies six avoidable counterexamples, while no legal collision-producing attacker is found in the remaining two scenes within the finite search budget.
发表机构
- Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。
