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

ArXi:2602.22801v2 Announce Type: replace-cross Diffusion models have become a popular choice for decision-making tasks in robotics, and 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.