  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. Subsequently, the transformed occupancy
prediction guides optimization to further inform safe and smooth planning under
Frenet coordinates. We train our planner using a large-scale, real-world
driving dataset and validate it in open-loop configurations. Our proposed
planner outperforms strong learning-based methods, exhibiting improved
performance due to occupancy prediction guidance.
