Safe and interpretable motion planning in complex urban environments needs to reason about bidirectional multi-agent interactions. This reasoning requires to estimate the costs of potential ego driving maneuvers. Many existing planners generate initial trajectories with sampling-based methods and refine them by optimizing on learned predictions of future environment states, which requires a cost function that encodes the desired vehicle behavior. Designing such a cost function can be very challenging, especially if a wide range of complex urban scenarios has to be considered. We propose HYPE: HYbrid Planning with Ego proposal-conditioned predictions, a planner that integrates multimodal trajectory proposals from a learned proposal model as heuristic priors into a Monte Carlo Tree Search (MCTS) refinement. To model bidirectional interactions, we introduce an ego-conditioned occupancy prediction model, enabling consistent, scene-aware reasoning. Our design significantly simplifies cost function design in refinement by considering proposal-driven guidance, requiring only minimalistic grid-based cost terms. Evaluations on large-scale real-world benchmarks nuPlan and DeepUrban show that HYPE effectively achieves state-of-the-art performance, especially in safety and adaptability.

翻译：在复杂的城市环境中实现安全且可解释的运动规划，需要对双向多智能体交互进行推理。这种推理需要估计潜在自车驾驶操作的代价。许多现有规划器采用基于采样的方法生成初始轨迹，并通过优化对未来环境状态的学习预测来细化轨迹，这需要一个编码期望车辆行为的代价函数。设计这样的代价函数极具挑战性，尤其是在需要考虑广泛复杂城市场景的情况下。我们提出HYPE：基于自车提议条件预测的混合规划，该规划器将来自学习提议模型的多模态轨迹提议作为启发式先验，集成到蒙特卡洛树搜索（MCTS）细化过程中。为建模双向交互，我们引入了一种自车条件占据预测模型，从而实现一致且场景感知的推理。通过考虑提议驱动的引导，我们的设计显著简化了细化过程中的代价函数设计，仅需极简的基于网格的代价项。在大型真实世界基准测试nuPlan和DeepUrban上的评估表明，HYPE有效实现了最先进的性能，尤其在安全性和适应性方面。