The release of nuPlan marks a new era in vehicle motion planning research, offering the first
large-scale real-world dataset and evaluation schemes requiring both precise short-term …

In this work, we propose the world's first closed-loop ML-based planning benchmark for
autonomous driving. While there is a growing body of ML-based motion planners, the lack of …

In highly interactive driving scenarios, the actions of one agent greatly influence those of its
neighbors. Planning safe motions for autonomous vehicles (AVs) in such interactive …

Although planning is a crucial component of the autonomous driving stack, researchers
have yet to develop robust planning algorithms that are capable of safely handling the …

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 …

Robot planning is the process of selecting a sequence of actions that optimize for a task=
specific objective. For instance, the objective for a navigation task would be to find collision …

In this manuscript, we introduce a real-time motion planning system based on the Baidu
Apollo (open source) autonomous driving platform. The developed system aims to address …

The motion planners used in self-driving vehicles need to generate trajectories that are safe,
comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior …

Reliable trajectory planning like human drivers in real-world dynamic urban environments is
a critical capability for autonomous driving. To this end, we develop a vision and imitation …

We propose an integrated prediction and planning system for autonomous driving which
uses rational inverse planning to recognise the goals of other vehicles. Goal recognition …