Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

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

Autonomous driving requires a comprehensive understanding of the surrounding
environment for reliable trajectory planning. Previous works rely on dense rasterized scene …

High-definition (HD) map provides abundant and precise static environmental information of
the driving scene, serving as a fundamental and indispensable component for planning in …

Understanding how the 3D scene evolves is vital for making decisions in autonomous
driving. Most existing methods achieve this by predicting the movements of object boxes …

Directly producing planning results from raw sensors has been a long-desired solution for
autonomous driving and has attracted increasing attention recently. Most existing end-to …

Currently high-definition (HD) map construction leans towards a lightweight online
generation tendency which aims to preserve timely and reliable road scene information …

With the surge in autonomous driving technologies, the reliance on comprehensive and high-
definition bird's-eye-view (BEV) representations has become paramount. This burgeoning …

Learning a human-like driving policy from large-scale driving demonstrations is promising,
but the uncertainty and non-deterministic nature of planning make it challenging. In this …

The rise of large foundation models, trained on extensive datasets, is revolutionizing the
field of AI. Models such as SAM, DALL-E2, and GPT-4 showcase their adaptability by …