The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

Grid-centric perception is a crucial field for mobile robot perception and navigation.
Nonetheless, grid-centric perception is less prevalent than object-centric perception as …

General world models represent a crucial pathway toward achieving Artificial General
Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual …

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

SLEDGE is the first generative simulator for vehicle motion planning trained on real-world
driving logs. Its core component is a learned model that is able to generate agent bounding …

World models are receiving increasing attention in autonomous driving for their ability to
predict potential future scenarios. In this paper, we present BEVWorld, a novel approach that …

Despite significant progress in robotics and embodied AI in recent years, deploying robots
for long-horizon tasks remains a great challenge. Majority of prior arts adhere to an open …

Autonomous driving, particularly navigating complex and unanticipated scenarios, demands
sophisticated reasoning and planning capabilities. While Multi-modal Large Language …

Accurately and promptly predicting accidents among surrounding traffic agents from camera
footage is crucial for the safety of autonomous vehicles (AVs). This task presents substantial …

World models can foresee the outcomes of different actions, which is of paramount
importance for autonomous driving. Nevertheless, existing driving world models still have …