When a Smaller Map Carries More Safety Signal
TL;DR for operators Chen, Zou, Zhao, and Gao propose replacing some of the scene detail presented to an autonomous-driving planner with a dense map organized around planning risk.1 Their risk-aware occupancy representation combines geometric obstacles, traffic-rule constraints, drivable-area information, and predicted future agent occupancy in a bird’s-eye-view field. The main integration result is substantial within the reported nuScenes validation setting. Adding the risk map to GenAD reduces average collision rate from 0.40% to 0.26% under the UniAD protocol and from 0.17% to 0.08% under ST-P3. Those are relative reductions of 35.0% and 52.9%, respectively. ...