Diffusion models excel at modeling complex and multimodal trajectory distributions for
decision-making and control. Reward-gradient guided denoising has been recently …

Diffusion models excel at modeling complex and multimodal trajectory distributions for
decision-making and control. Reward-gradient guided denoising has been recently …

We present PLUTO, a powerful framework that pushes the limit of imitation learning-based
planning for autonomous driving. Our improvements stem from three pivotal aspects: a …

Machine Learning (ML) has replaced traditional handcrafted methods for perception and
prediction in autonomous vehicles. Yet for the equally important planning task, the adoption …

This study introduces a novel approach to autonomous motion planning, informing an
analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate …

Achieving a proper balance between planning quality, safety and efficiency is a major
challenge for autonomous driving. Optimisation-based motion planners are capable of …

General-purpose planning algorithms for automated driving combine mission, behavior, and
local motion planning. Such planning algorithms map features of the environment and …

End-to-end motion planning models equipped with deep neural networks have shown great
potential for enabling full autonomous driving. However the oversized neural networks …

In recent years, imitation-based driving planners have reported considerable success.
However, due to the absence of a standardized benchmark, the effectiveness of various …

Learning-based approaches to autonomous vehicle planners have the potential to scale to
many complicated real-world driving scenarios by leveraging huge amounts of driver …