This paper explores the emerging knowledge-driven autonomous driving technologies. Our
investigation highlights the limitations of current autonomous driving systems, in particular …

The emergence of Multimodal Large Language Models ((M) LLMs) has ushered in new
avenues in artificial intelligence, particularly for autonomous driving by offering enhanced …

Digital twins for intelligent transportation systems are currently attracting great interests, in
which generating realistic, diverse, and human-like traffic flow in simulations is a formidable …

Simulators generate diverse and realistic traffic scenarios to boost the development of
autonomous driving systems. However, existing simulators often fall short in scenario …

With the rapid development of urban transportation and the continuous advancement in
autonomous vehicles, the demand for safely and efficiently testing autonomous driving and …

Most of the current studies on autonomous vehicle decision-making and control based on
reinforcement learning are conducted in simulated environments. The training and testing of …

Home robots operate in diverse and dynamic environments, delivering a range of functions
that enhance utility. Many of these functions span extended periods, from weeks to months …