Occupancy Prediction-Guided Neural Planner for Autonomous Driving

Understand

Forecasting the scalable future states of surrounding traffic participants in complex traffic scenarios is a critical capability for autonomous vehicles, as it enables safe and feasible decision-making.

• Recent successes in learning-based prediction and planning have introduced two primary challenges: generating accurate joint predictions for the environment and integrating prediction guidance for planning purposes.
• To address these challenges, we propose a two-stage integrated neural planning framework, termed OPGP, that incorporates joint prediction guidance from occupancy forecasting.
• The preliminary planning phase simultaneously outputs the predicted occupancy for various types of traffic actors based on imitation learning objectives, taking into account shared interactions, scene context, and actor dynamics within a unified Transformer structure.