Autonomous driving systems require the ability to fully understand and predict the surrounding environment to make informed decisions in complex scenarios. Recent advancements in learning-based systems have highlighted the importance of integrating prediction and planning modules. However, this integration has brought forth three major challenges: inherent trade-offs by sole prediction, consistency between prediction patterns, and social coherence in prediction and planning. To address these challenges, we introduce a hybrid-prediction integrated planning (HPP) system, which possesses three novelly designed modules. First, we introduce marginal-conditioned occupancy prediction to align joint occupancy with agent-wise perceptions. Our proposed MS-OccFormer module achieves multi-stage alignment per occupancy forecasting with consistent awareness from agent-wise motion predictions. Second, we propose a game-theoretic motion predictor, GTFormer, to model the interactive future among individual agents with their joint predictive awareness. Third, hybrid prediction patterns are concurrently integrated with Ego Planner and optimized by prediction guidance. HPP achieves state-of-the-art performance on the nuScenes dataset, demonstrating superior accuracy and consistency for end-to-end paradigms in prediction and planning. Moreover, we test the long-term open-loop and closed-loop performance of HPP on the Waymo Open Motion Dataset and CARLA benchmark, surpassing other integrated prediction and planning pipelines with enhanced accuracy and compatibility.

翻译：自动驾驶系统需要具备全面理解和预测周围环境的能力，以便在复杂场景中做出明智决策。基于学习的系统的最新进展突显了整合预测与规划模块的重要性。然而，这种整合带来了三大挑战：单一预测的内在权衡、预测模式间的一致性，以及预测与规划中的社交一致性。为应对这些挑战，我们提出了一种混合预测集成规划（HPP）系统，该系统包含三个新颖设计的模块。首先，我们引入边缘条件占用预测，将联合占用与个体感知对齐。所提出的MS-OccFormer模块通过个体运动预测的一致感知，实现了每次占用预测的多阶段对齐。其次，我们提出博弈论运动预测器GTFormer，利用个体的联合预测感知对其交互式未来进行建模。第三，混合预测模式与Ego Planner并行集成，并通过预测引导进行优化。HPP在nuScenes数据集上取得了最先进的性能，展现了预测与规划端到端范式的卓越准确性和一致性。此外，我们在Waymo开放运动数据集和CARLA基准上测试了HPP的长期开环与闭环性能，在准确性和兼容性方面超越其他集成预测与规划流程。