Comprehensive Robustness Analysis of LiDAR-based 3D Object Detection in Autonomous Driving

Recent advancements in LiDAR-only 3D object detectionhave demonstrated improved detection accuracy over benchmark datasets.However, the adversarial robustness of these models remains untested.Very few adversarial robustness studies exist for LiDAR-only 3D ob-ject detection and unfortunately, even they are limited to legacy models.Moreover, there is a systemic gap in the existing evaluation frameworksthat rely simply on mAP ignoring other structural and predictive factors.To fill this gap, we propose a holistic framework that evaluates adversar-ial robustness using two structural factors (point cloud density and pointcloud localization) and three predictive factors (misclassification, local-ization error, distance from ego). Using this framework, we perform anempirical study and critical analysis on recent and legacy state-of-the-art models using adversarial attacks specifically designed for LiDAR-based models. Our key finding is that high-capacity, voxel-based detec-tors are more susceptible to structured coordinate perturbations thanpillar-based detectors. Additionally, non-anchor-based detectors demon-strate poor adversarial robustness, which necessitates rethinking modeltraining techniques. Overall, our results demonstrate that recent modelsare as vulnerable to adversarial attacks as their predecessors. Therefore,we argue that there is a need to improve the evaluation benchmarksfor 3D object detection that not only reward architectural modificationsfor improving detection accuracy, but also evaluate whether the designchoices improve adversarial robustness.