Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model
into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge …

Autonomous driving requires a comprehensive understanding of the surrounding
environment for reliable trajectory planning. Previous works rely on dense rasterized scene …

A primary hurdle of autonomous driving in urban environments is understanding complex
and long-tail scenarios, such as challenging road conditions and delicate human behaviors …

End-to-end autonomous driving recently emerged as a promising research direction to
target autonomy from a full-stack perspective. Along this line many of the latest works follow …

In contrast to extensive studies on general vision pre-training for scalable visual
autonomous driving remains seldom explored. Visual autonomous driving applications …

Predicting how the world can evolve in the future is crucial for motion planning in
autonomous systems. Classical methods are limited because they rely on costly human …

Human-level driving is an ultimate goal of autonomous driving. Conventional approaches
formulate autonomous driving as a perception-prediction-planning framework, yet their …

Directly producing planning results from raw sensors has been a long-desired solution for
autonomous driving and has attracted increasing attention recently. Most existing end-to …