Foundation models have indeed made a profound impact on various fields, emerging as
pivotal components that significantly shape the capabilities of intelligent systems. In the …

Embodied scene understanding serves as the cornerstone for autonomous agents to
perceive, interpret, and respond to open driving scenarios. Such understanding is typically …

The rise of multimodal large language models (MLLMs) has spurred interest in language-
based driving tasks. However existing research typically focuses on limited tasks and often …

The pursuit of autonomous driving technology hinges on the sophisticated integration of
perception, decision-making, and control systems. Traditional approaches, both data-driven …

As autonomous driving technology matures, end-to-end methodologies have emerged as a
leading strategy, promising seamless integration from perception to control via deep …

Sensor fusion approaches for intelligent self-driving agents remain key to driving scene
understanding given visual global contexts acquired from input sensors. Specifically, for the …

Autonomous driving requires a structured understanding of the surrounding road maps and
networks to navigate. However, considering the flexibility of autonomous vehicles and the …

To safely navigate intricate real-world scenarios, autonomous vehicles must be able to
adapt to diverse road conditions and anticipate future events. World model (WM) based …

Vision-language generative AI has demonstrated remarkable promise for empowering cross-
modal scene understanding of autonomous driving and high-definition (HD) map systems …

Accurate scene understanding from multiple sensors mounted on cars is a key requirement
for autonomous driving systems. Nowadays, this task is mainly performed through data …