Hybrid-Prediction Integrated Planning for Autonomous Driving
Haochen Liu Zhiyu Huang Wenhui Huang Haohan Yang Xiaoyu Mo Chen Lv
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.