Simulation plays a critical role in the research and development of autonomous driving and
intelligent transportation systems. However, the current simulation platforms exhibit …

Agent-based modeling and simulation has evolved as a powerful tool for modeling complex
systems, offering insights into emergent behaviors and interactions among diverse agents …

In this paper, we explore the potential of using a large language model (LLM) to understand
the driving environment in a human-like manner and analyze its ability to reason, interpret …

Interactive traffic simulation is crucial to autonomous driving systems by enabling testing for
planners in a more scalable and safe way compared to real-world road testing. Existing …

Existing learning-based autonomous driving (AD) systems face challenges in
comprehending high-level information, generalizing to rare events, and providing …

Human drivers instinctively reason with commonsense knowledge to predict hazards in
unfamiliar scenarios and to understand the intentions of other road users. However, this …

The fusion of human-centric design and artificial intelligence capabilities has opened up
new possibilities for next-generation autonomous vehicles that go beyond traditional …

Over the past few years there is a growing interest in the learning-based self driving system.
To ensure safety, such systems are first developed and validated in simulators before being …

World models have demonstrated superiority in autonomous driving, particularly in the
generation of multi-view driving videos. However, significant challenges still exist in …

Despite significant recent progress in the field of autonomous driving modern methods still
struggle and can incur serious accidents when encountering long-tail unforeseen events …