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.

翻译：预测复杂交通场景中周围交通参与者的可扩展未来状态，是自动驾驶车辆实现安全且可行决策的关键能力。近期基于学习的预测与规划成功案例带来了两项主要挑战：为环境生成准确的联合预测，以及将预测引导集成至规划流程。为应对这些挑战，我们提出一种名为OPGP的两阶段集成神经规划框架，该框架融合了来自占据预测的联合预测引导。初步规划阶段基于模仿学习目标，在统一的Transformer结构内考虑交互共享、场景上下文及参与者动态，同时输出各类交通参与者的预测占据。随后，转换后的占据预测引导优化过程，进一步在Frenet坐标系下实现安全平滑的规划。我们利用大规模真实驾驶数据集训练规划器，并在开环配置下进行验证。所提出的规划器优于基于学习的强基线方法，且因占据预测引导而展现出更优性能。