用雷达定位引导相机验证障碍物，大幅降低计算负担。
Radar Guided Camera Verification for Automatic Emergency Braking Rethinking Object Detection in Radar Camera Fusion
- 基于雷达区域引导相机局部验证，无需训练和GPU。
- 搜索空间缩小98.7%，单区域处理延迟仅0.121毫秒。
- 实车测试中漏刹为零，适合车载实时系统部署。
雷达-相机融合广泛用于自动紧急制动（AEB）系统，因雷达提供可靠的测距与速度信息，而相机可对目标进行视觉确认。现有系统多依赖高算力目标检测完成确认，但若雷达已定位目标，相机只需验证障碍物存在，而非完整识别。本文提出一种雷达范围边缘密度门机制，在雷达引导的图像兴趣区域执行障碍物验证。该方法无需训练数据、模型权重或GPU加速，已集成至具备线控刹车功能的完整雷达-相机融合AEB系统中。在实车72次驾驶会话、131,603帧相机图像上评估，该方法将相机搜索空间缩小达98.7%，平均每区域处理延迟0.121毫秒，AUC达0.898，召回率达0.994。在33个预设威胁场景中，系统实现零漏刹。
原文摘要 · Abstract (English)
Radar camera fusion is widely used in Automatic Emergency Braking AEB systems because radar provides reliable range and velocity measurements while cameras provide a proper visual confirmation of the objects . Most of the deployed systems perform this confirmation using computationally intensive object detectors. However, if the radar has already localized a target, the camera may only need to verify the obstacles presence rather than solving a full problem by identifying the object. Our work proposes a radar scoped edge density gate that performs obstacle verification within radar guided image regions of interest. This method requires no training data, model weights, or GPU acceleration and was integrated into a complete radar camera fusion AEB system with brake by wire actuation. Evaluated on a real instrumented vehicle across 72 driving sessions and 131,603 camera frames, the proposed approach reduced the camera search space by up to 98.7 percentage, achieved a mean processing latency of 0.121 ms per ROI, an AUC of 0.898, and a recall of 0.994. Across 33 staged threat scenarios, the complete AEB system recorded zero missed brake events.
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