发表机构
Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对学习型驾驶规划器闭环中的失效问题，提出分层递推时域控制框架CLRE，通过最优控制与OBB可行性筛选执行安全轨迹，在Bench2Drive上显著提升驾驶得分与路线完成率并减少碰撞。
AI 中文摘要
基于学习的驾驶规划器通常在开环条件下针对记录的轨迹进行训练和评估。在闭环中，具有小位移误差的轨迹仍可能导致车辆停滞、使车辆与周围智能体发生冲突，或执行时产生急刹车。我们引入了闭环细化与执行（CLRE），这是一种分层递推时域控制框架，旨在缓解这些失效模式，同时保持上游规划器不变且不添加新的学习模型。上层将标称轨迹视为参考，并求解一个有限时域最优控制问题，在路线进展与预测智能体交互之间进行权衡。从多个初始化求解该问题可得到候选集，而基于预测的定向包围盒（OBB）可行性测试仅保留在时域内最小预测OBB间隙达到阈值的候选。下层通过规划器提供的跟踪控制器执行成本最低的幸存候选，若无幸存候选则执行路线中心线备份，并辅以基于距离的速度限制和饱和比例制动律。在126条Bench2Drive路线上以VAD作为上游规划器的闭环仿真中，CLRE将驾驶得分从43.41提升至56.42，路线完成率从57.27提升至72.23，并将碰撞事件从70次减少至53次。
英文摘要
Learning-based driving planners are usually trained and evaluated in open loop against logged trajectories. In closed loop, a trajectory with small displacement error can still stall the vehicle, steer it into a conflict with surrounding agents, or be executed with abrupt braking. We introduce Closed-Loop Refinement and Execution (CLRE), a hierarchical receding-horizon control framework designed to mitigate these failure modes while leaving the upstream planner frozen and adding no new learned model. The upper layer treats the nominal trajectory as a reference and solves a finite-horizon optimal control problem that trades route progress against interaction with predicted agents. Solving it from several initializations gives a candidate set, and a prediction-conditioned oriented-bounding-box (OBB) feasibility test retains only candidates whose minimum predicted OBB clearance over the horizon meets a threshold. The lower layer executes the lowest-cost survivor, or a route-centerline backup when none remains, through the tracking controller supplied with the planner, augmented by a range-based speed bound and a saturated proportional braking law. In closed-loop simulation on the 220-route Bench2Drive validation set with VAD as the upstream planner, CLRE raises the driving score from 42.26 to 55.89 and route completion from 55.69 to 71.51, and reduces collision events from 117 to 97.
Comments8 pages, 6 figures, 3 tables. Submitted to the 2027 American Control Conference (ACC 2027)
