Provisioning Robust and Efficient LiDAR Perception for Autonomous Driving

Key points

• We therefore formulate LiDAR perception provisioning as constructing a detector that remains accurate under point-cloud corruptions while meeting constraints on inference latency and peak GPU memory.1
• We estimate perturbation sensitivity using an ensemble of seven heterogeneous 3D detectors by tracking changes in confidence, BEV overlap, and unmatched detections.1
• We evaluate our method using AP (Average Precision) under clean and corrupted conditions, inference latency, and peak allocated GPU memory.1
• On KITTI for Car detection with PointRCNN, our method improves Moderate AP under corruption by 3.32 points over the original detector while reducing mean latency by 19.8% and peak allocated GPU memory by 53.9%.1

Sources (1)

• [1]Provisioning Robust and Efficient LiDAR Perception for Autonomous Driving
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 04:31 AM

  LiDAR-based 3D object detection is a core perception service for autonomous driving, but deployment requires robustness to point-cloud corruption while meeting latency and memory requirements.

  We therefore formulate LiDAR perception provisioning as constructing a detector that remains accurate under point-cloud corruptions while meeting constraints on inference latency and peak GPU memory.

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