结合CVaR的障碍函数保安全间隙认证用于驾驶轨迹选择

中文摘要 AI 辅助

自动驾驶运动规划器生成并选择候选轨迹时需考虑与周围智能体的交互，但现有评估无法认证所选轨迹的实际安全间隙。本框架评估由规划智能体选出的轨迹，校准其规划时间余量与实际实现安全间隙之间的差距。可分离轴障碍余量确定性地下界精确的有向包围盒（OBB）安全间隙，将统计认证与安全余量关联。规划时，该余量通过名义预测和采样的下尾条件风险价值（CVaR）评估；轨迹选定后，对可交换驾驶会话进行保形校准，以吸收预测和采样误差，且保形校准的统计有效性独立于预测器的正确性。本方法在冻结的300次nuPlan研究会话上，使用原生预测驾驶模型（PDM）闭环提议进行评估：在10%的目标未覆盖率下，采样的下尾CVaR将保形修正从1.43米降至0.03米，非负安全间隙认证率从68.7%提升至87.3%；在所有评估统计量下，精确间隙覆盖率保持在93.3%至96.7%之间，高于90%的目标值。

英文摘要

Autonomous driving motion planners generate and select candidate trajectories while accounting for interactions with surrounding agents. However, these evaluations do not certify the actual safety clearance of the selected trajectory. The framework evaluates the trajectory selected by ant planners and calibrates the gap between its plan time margin and realized safety clearance. A differentiable separating axis barrier margin deterministically lower bounds exact signed oriented-bounding-box (OBB) safety clearance, connecting the statistical certificate to safety margin. At plan time, the margin is evaluated using either a nominal prediction and sampled lower tail Conditional Value-at-Risk (CVaR), while post-selection conformal calibration over exchangeable drive sessions absorbs prediction and sampling errors. Conformal calibration provides statistical validity independently of predictor correctness. The method is evaluated on a frozen 300 session nuPlan study using native Predictive Driver Model (PDM) Closed loop proposals. At 10% target miscoverage, sampled lower CVaR reduces the conformal correction from 1.43m to 0.03m and increases the rate of nonnegative safety clearance certificates from 68.7% to 87.3%. Across all evaluated statistics, exact-clearance coverage remains above the 90% target at 93.3--96.7%.

发表机构

• The Ohio State University（俄亥俄州立大学）

机构由 AI 辅助整理，请以论文原文为准。