A new risk-aware occupancy method, risk-aware occupancy, is proposed for end-to-end autonomous driving. It encodes global scene occupancy, map-derived traffic constraints, and future dynamic agent occupancy into a unified BEV map, capturing risk evidence for trajectory planning. A neural network, ROIDrive, predicts risk-aware occupancy and injects it into planning queries for safety-oriented trajectory generation.