This presentation of our IEEE SENSORS 2024 paper introduces a radar–LiDAR fusion strategy for occupancy grid mapping in automotive driving. Occupied cells in a driving scene are sparse, so the map is estimated within a sparse Bayesian learning framework. Within that framework, measurements from the two sensors are fused softly: LiDAR contributes accurate range detections, and radar contributes robustness in conditions where optical sensing degrades.
The recording was published by the IEEE Sensors Council. The work was later extended into a journal article, Spatial Sparsity-Aware Radar-LiDAR Fusion for Occupancy Grid Mapping in Automotive Driving (IEEE Sensors Journal, 2025).