通过深度感知检测不一致，实时估算自动驾驶车辆碰撞风险。
FuzzRisk: Online Collision Risk Estimation for Autonomous Vehicles based on Depth-Aware Object Detection via Fuzzy Inference
- 用双检测结果不一致度，结合模糊推理评估风险
- 在nuScenes数据集上验证，风险预测与真实碰撞率高度匹配
- 适合用于自动驾驶系统在线安全监控，提升闭环仿真可靠性
本文提出一种新型监控框架，基于自动驾驶车辆（AV）的物体检测性能推断碰撞风险。该框架获取两组不同算法的检测结果：一组来自深度图提取的安全关键2.5D物体，另一组来自常规AV的3D物体检测器。通过交并比（IoU）和深度差异度量，实验验证两组预测的不一致程度与3D检测器对真实标注的误差强相关。基于此相关性，构建模糊推理系统，将不一致度映射为碰撞风险指标。特别地，模糊系统优化以匹配现有离线指标，该指标与AV碰撞率吻合良好。最后，在大规模nuScenes数据集上验证了监测器生成相关风险估计的能力，并证明其可在闭环仿真中有效保障AV安全。
原文摘要 · Abstract (English)
This paper presents a novel monitoring framework that infers the level of collision risk for autonomous vehicles (AVs) based on their object detection performance. The framework takes two sets of predictions from different algorithms and associates their inconsistencies with the collision risk via fuzzy inference. The first set of predictions is obtained by retrieving safety-critical 2.5D objects from a depth map, and the second set comes from the ordinary AV's 3D object detector. We experimentally validate that, based on Intersection-over-Union (IoU) and a depth discrepancy measure, the inconsistencies between the two sets of predictions strongly correlate to the error of the 3D object detector against ground truths. This correlation allows us to construct a fuzzy inference system and map the inconsistency measures to an AV collision risk indicator. In particular, we optimize the fuzzy inference system towards an existing offline metric that matches AV collision rates well. Lastly, we validate our monitor's capability to produce relevant risk estimates with the large-scale nuScenes dataset and demonstrate that it can safeguard an AV in closed-loop simulations.
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