Mining Cross-Modal Perception Gaps in Autonomous Driving: When Camera and LiDAR Disagree

Key points

• Because the patterns shift with distance from the ego vehicle, we read the mined model through Spatially Stratified Feature Attribution (SSFA), which conditions Shapley attribution on distance zones and exposes structure that global attribution averages away.1
• The modality-preference boundary tracks the object itself: a traffic cone is nearly six times as likely as a truck to be seen by the camera alone (16.8% vs. 2.9%), regardless of range.1
• We further find that modality dominance is directionally non-stationary, with objects behind the vehicle being markedly more LiDAR-dominant than those in front.1
• We compile the mined structure into a Modality Reliability Map and inject it into four pretrained fusion detectors through a lightweight attention layer.1

Sources (1)

• [1]Mining Cross-Modal Perception Gaps in Autonomous Driving: When Camera and LiDAR Disagree
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 02:56 PM

  Multi-modal fusion for 3D object detection assumes that cameras and LiDAR are broadly complementary.

  Because the patterns shift with distance from the ego vehicle, we read the mined model through Spatially Stratified Feature Attribution (SSFA), which conditions Shapley attribution on distance zones and exposes structure that global attribution averages away.

Extractive summary: sentences quoted from the sources.