GDP: Enhancing End-To-End Autonomous Driving with Goal-Driven Planner

Abstract

End-to-end (E2E) autonomous driving has emerged as a promising paradigm with the pervasive power of model architectures and the availability of large-scale driving datasets. Despite tremendous efforts in recent research, most E2E driving frameworks rely on rather general driving commands, such as "Go Straight" or "Turn Left", which fail to encapsulate the complexities of nuanced driving behaviors and lead to possible semantic ambiguities. Furthermore, such commands are not adequately translated into specific goal locations, which severely limits the planner's capacity to make informed, long-term decisions. This limitation hinders the integration of near-term trajectory planning with long-term goal achievement. To tackle these challenges, we propose the Goal-Driven Planner (GDP), accommodating an appealing plug-and-play feature, which particularly leverages explicit goal points and incorporates two complementary learning objectives: (i) predicting a scene-aware long-term route to the goal, and (ii) refining the near-term trajectory through interaction with the long-term routing. Extensive experiments conducted on the nuScenes and NAVSIM datasets showcase the effectiveness of GDP. When integrated into off-the-shelf E2E autonomous driving frameworks like UniAD, VAD-Tiny, and DiffusionDrive, GDP decreases L2 errors and collision rates, also improves closed-loop metrics in the open-loop evaluation. Essentially, these results highlight the strong generalization capability of GDP and its promising practical significance in enhancing planning reliability and safety in real-world autonomous driving systems.