ForecastOcc: Vision-based Semantic Occupancy Forecasting

Understand

Autonomous driving requires forecasting both geometry and semantics over time to effectively reason about future environment states.

• Existing vision-based occupancy forecasting methods focus on motion-related categories such as static and dynamic objects, while semantic information remains largely absent.
• Recent semantic occupancy forecasting approaches address this gap but rely on past occupancy predictions obtained from separate networks.
• This makes current methods sensitive to error accumulation and prevents learning spatio-temporal features directly from images.