Autonomous vehicles (AVs) have the potential to significantly revolutionize society by
providing a secure and efficient mode of transportation. Recent years have witnessed …

Large-scale data is crucial for learning realistic and capable driving policies. However, it can
be impractical to rely on scaling datasets with real data alone. The majority of driving data is …

Autonomous driving necessitates the ability to reason about future interactions between
traffic agents and to make informed evaluations for planning. This paper introduces the\textit …

Traffic simulation aims to learn a policy for traffic agents that, when unrolled in closed-loop,
faithfully recovers the joint distribution of trajectories observed in the real world. Inspired by …

The burgeoning field of autonomous driving necessitates the seamless integration of
autonomous vehicles (AVs) with human-driven vehicles, calling for more predictable AV …

Household and individual-level sociodemographic data are essential for understanding
human-infrastructure interaction and policymaking. However, the Public Use Microdata …

Despite advancements in perception and planning for autonomous vehicles (AVs),
validating their performance remains a significant challenge. The deployment of planning …

Diffusion-based models are recognized for their effectiveness in using real-world driving
data to generate realistic and diverse traffic scenarios. These models employ guided …

생성형 인공지능 기술의 급속한 발전은 산업 및 일상생활 전반에 걸쳐 그 의존도를 높이고 있다.
그러나 대부분의 생성형 인공지능 솔루션은 주로 텍스트 또는 2D 이미지 생성에 중점을 두고 …