Large-scale driving datasets such as Waymo Open Dataset and nuScenes substantially
accelerate autonomous driving research, especially for perception tasks such as 3D …

Recent failures in real-world self-driving tests have suggested a paradigm shift from directly
learning in real-world roads to building a high-fidelity driving simulator as an alternative …

Autonomous driving technology, a catalyst for revolutionizing transportation and urban
mobility, has the tend to transition from rule-based systems to data-driven strategies …

Autonomous Driving (AD) faces crucial hurdles for commercial launch, notably in the form of
diminished public trust and safety concerns from long-tail unforeseen driving scenarios. This …

Simulation has the potential to massively scale evaluation of self-driving systems, enabling
rapid development as well as safe deployment. Bridging the gap between simulation and …

In the autonomous driving field, fusion of human knowledge into Deep Reinforcement
Learning (DRL) is often based on the human demonstration recorded in a simulated …

In the past decade, autonomous driving has experienced rapid development in both
academia and industry. However, its limited interpretability remains a significant unsolved …

One of the most challenging problems in human-machine co-work is the gap between
human intention and the machine's understanding and execution. Large Language Models …

Existing research on autonomous driving primarily focuses on urban driving, which is
insufficient for characterising the complex driving behaviour underlying high-speed racing …

Large Language Model (LLM) agents, capable of performing a broad range of actions, such
as invoking tools and controlling robots, show great potential in tackling real-world …