Hybrid-Prediction Integrated Planning for Autonomous Driving

Engineering notes

Our proposed MS-OccFormer module achieves spatial-temporal alignment with motion predictions across multiple granularities. The HPP framework establishes state-of-the-art performance on the nuScenes dataset, demonstrating superior accuracy and safety in end-to-end configurations.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Autonomous driving systems require a comprehensive understanding and accurate prediction of the surrounding environment to facilitate informed decision-making in complex scenarios. Recent advances in learning-based systems have highlighted the importance of integrating prediction and planning. However, this integration poses significant alignment challenges through consistency between prediction patterns, to interaction between future prediction and planning. To address these challenges, we introduce a Hybrid-Prediction integrated Planning (HPP) framework, which operates through three novel modules collaboratively. First, we introduce marginal-conditioned occupancy prediction to align joint occupancy with agent-specific motion forecasting. Our proposed MS-OccFormer module achieves spatial-temporal alignment with motion predictions across multiple granularities. Second, we propose a game-theoretic motion predictor, GTFormer, to model the interactive dynamics among agents based on their joint predictive awareness. Third, hybrid prediction patterns are concurrently integrated into the Ego Planner and optimized by prediction guidance. The HPP framework establishes state-of-the-art performance on the nuScenes dataset, demonstrating superior accuracy and safety in end-to-end configurations. Moreover, HPP’s interactive open-loop and closed-loop planning performance are demonstrated on the Waymo Open Motion Dataset (WOMD) and CARLA benchmark, outperforming existing integrated pipelines by achieving enhanced consistency between prediction and planning.

Links and sources

• Official / arXiv page
• PDF from original source

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