一句话：提出SORBET框架，评估自动驾驶3D障碍物检测模型对规范驱动点云扰动的鲁棒性。
自动驾驶系统依赖LiDAR点云进行实时3D障碍物检测，但现有模型对源于LiDAR传感器规范及物体颜色、材质捕获特性的扰动是否鲁棒尚缺乏评估。本文提出SORBET，依据规范生成细微传感器噪声与障碍物特定扰动，对点云施加扰动并系统测试检测模型鲁棒性。作者将SORBET用于五种经典3D障碍物检测模型，包括百度Apollo工业级L4系统，并分析检测偏差如何传播至轨迹预测。结果表明，即使移除两个点等极细微点云变化，也可能导致检测性能非平凡下降，且负面影响会传播到其他模块，危及自动驾驶安全，凸显规范驱动鲁棒性测试的重要性。
Autonomous driving systems (ADSs) rely on real-time sensor data, such as cameras and LiDARs, for time-critical decisions using deep neural networks. The accuracy of these decisions is crucial for the widespread adoption of ADSs, as errors can have serious consequences. 3D obstacle detection, in particular, is sensitive to point cloud data (PCD) noise from various sources. However, the robustness of current 3D obstacle detection models against specification-based perturbations remains unevaluated. These perturbations are derived from the specification of LiDAR sensors and previous research on LiDAR’s ability to capture objects of different colors and materials. They can manifest as very subtle sensor-based noises or obstacle-specific perturbations. Hence, we propose SORBET, a framework that tests the robustness of 3D obstacle detection models in ADS against such perturbations to the PCD to evaluate their robustness. We applied SORBET to evaluate the robustness of five classic 3D obstacle detection models, including one from an industry-grade Level 4 ADS (Baidu’s Apollo). Furthermore, we studied how the deviated obstacle detection results would propagate and negatively impact trajectory prediction. Our evaluation emphasizes the importance of testing 3D obstacle detection against specification-based perturbations. We find that even very subtle changes in the PCD (i.e., removing two points) may introduce a non-trivial decrease in the detection performance. Furthermore, such a negative impact will further propagate to other modules and endanger the safety of the ADS.
DOI 原文 ·
@inproceedings{paperbot9310,
title = {On the Robustness Evaluation of 3D Obstacle Detection Against Specifications in Autonomous Driving},
author = {Tri Minh-Triet Pham and Bo Yang and Jinqiu Yang},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00213}
}