Problem
Research questions and friction points this paper is trying to address.
adaptive LiDAR scanning
first-sighting loss
camera-guided
history-driven
CARE: Camera-Residual Reserves for First Sightings in Adaptive LiDAR Sensing
🤖 AI Summary
本文提出CARE方法，通过为当前相机检测但历史轨迹未能预测的方向预留部分激光雷达预算，解决了自适应激光雷达扫描中首次发现物体的延迟或遗漏问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive LiDAR Scanning
First Sighting Recall
Camera-Residual Reserve (CARE)
Ray-Budget Allocation
Safety-Bounded Forgetting
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