Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving. (arXiv:2303.17297v1 [cs.CV])

3D object detection is an essential perception task in autonomous driving to
understand the environments. The Bird's-Eye-View (BEV) representations have
significantly improved the performance of 3D detectors with camera inputs on
popular benchmarks. However, there still lacks a systematic understanding of
the robustness of these vision-dependent BEV models, which is closely related
to the safety of autonomous driving systems. In this paper, we evaluate the
natural and adversarial robustness of various representative models under
extensive settings, to fully understand their …

adversarial autonomous autonomous driving benchmarks bird camera detection driving environments explicit inputs object performance popular robustness safety settings systems task under understand understanding