Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving

TL;DR

This paper systematically investigates diffusion models for end-to-end autonomous driving, demonstrating their effectiveness and scalability through real-vehicle testing and a novel Hyper Diffusion Planner framework.

Contribution

It introduces the Hyper Diffusion Planner, a diffusion-based autonomous driving planner, and provides insights into training strategies, trajectory representation, and real-world deployment.

Findings

10x performance improvement over the base model

Effective reinforcement learning post-training strategy

Successful deployment on real-vehicle platform across urban scenarios

Abstract

Diffusion models have become a popular choice for decision-making tasks in robotics, and more recently, are also being considered for solving autonomous driving tasks. However, their applications and evaluations in autonomous driving remain limited to simulation-based or laboratory settings. The full strength of diffusion models for large-scale, complex real-world settings, such as End-to-End Autonomous Driving (E2E AD), remains underexplored. In this study, we conducted a systematic and large-scale investigation to unleash the potential of the diffusion models as planners for E2E AD, based on a tremendous amount of real-vehicle data and road testing. Through comprehensive and carefully controlled studies, we identify key insights into the diffusion loss space, trajectory representation, and data scaling that significantly impact E2E planning performance. Moreover, we also provide an effective reinforcement learning post-training strategy to further enhance the safety and robustness of the learned planner. The resulting diffusion-based learning framework, Hyper Diffusion Planner (HDP), is deployed on a real-vehicle platform and evaluated across 6 urban driving scenarios and 200 km of real-world testing, achieving a notable 10x performance improvement over the base model. Our work demonstrates that diffusion models, when properly designed and trained, can serve as effective and scalable E2E AD planners for complex, real-world autonomous driving tasks.