CARE：自适应激光雷达感知中用于首次目标检测的相机残差储备

中文摘要 AI 辅助

自适应激光雷达扫描将有限的感知预算集中在从过往目标轨迹预测的感兴趣区域，在自动驾驶中降低数据量的同时保持检测精度。然而，现有的扫描策略面临三个挑战：第一，基于历史轨迹的方法依赖过往轨迹，导致未见过的目标检测延迟或遗漏；第二，在预测区域外的随机或均匀采样无法感知新目标出现的位置；第三，相机引导的替代方案将预算分配给所有相机检测结果，对已覆盖的目标进行重采样，在拥挤场景中降低召回率，在预算紧张时降低测距性能。本文提出了相机残差储备（CAmera-REsidual reserve，CARE），这是一种无需训练的分配规则，将固定光线预算的一部分预留用于当前相机检测中轨迹预测无法解释的方向，其余部分遵循基础历史策略，未使用的储备返回随机底层。本文的贡献有三点：第一，在nuScenes数据集（150个场景，4148个事件）上进行无泄漏的光线预算评估，测量基于历史轨迹扫描的首次检测损失，采用使用前一关键帧的严格因果变体；第二，CARE在10%、20%和35%预算下，将首次检测召回率较历史策略分别提高5.2、5.2和4.3个百分点，配对区间排除零值，相机提示驱动了这一增益，首次检测与整体性能的权衡是依赖预算的帕累托选择；第三，提出安全约束遗忘模块，从超出速度相关保护距离的后退或静态轨迹中释放预算，在紧张预算下，无保护的遗忘会严重损害近场召回率，因此保护机制是保障安全的关键。该流程在真实车辆上实现端到端运行，在闭环仿真中，比基于历史轨迹的扫描更早检测到被遮挡的行人，制动更可靠。

英文摘要

Adaptive LiDAR scanning concentrates a limited sensing budget on regions of interest predicted from past object tracks, lowering data volume in autonomous driving while maintaining detection accuracy. However, existing scanning policies face three challenges. First, history-driven approaches depend on past tracks, so unseen objects are detected late or missed. Second, random or uniform sampling outside the predicted regions has no awareness of where new objects appear. Third, camera-guided alternatives spend budget on all camera detections, resampling objects already covered, costing recall in crowded scenes and range when budgets are scarce. This paper introduces the CAmera-REsidual reserve (CARE), a training-free allocation rule that reserves part of a fixed ray budget for the directions of current camera detections that the track forecasts cannot explain; the rest follows the base history policy, and unused reserve returns to a random floor. The paper makes three contributions. First, a leakage-free ray-budget evaluation on nuScenes (150 scenes, 4,148 events) measuring the first-sighting loss of history-driven scanning, with a strict-causal variant using the preceding keyframe. Second, CARE raises first-sighting recall by 5.2, 5.2, and 4.3 points at 10%, 20%, and 35% budgets over the history policy, with paired intervals excluding zero; the camera cue drives this gain, and the first-sighting versus overall trade-off is a budget-dependent Pareto choice. Third, a safety-bounded forgetting module that releases budget from receding or static tracks beyond a speed-dependent guard distance; at tight budgets, forgetting without the guard significantly harms near-field recall, so the guard is what keeps it safe. The pipeline runs end to end on a real vehicle and, in closed-loop simulation, detects an occluded pedestrian earlier and brakes more reliably than history-driven scanning.

发表机构

• The University of Tokyo（东京大学）
• National Institute of Informatics（信息学研究所）

机构由 AI 辅助整理，请以论文原文为准。