Autonomous Driving (AD) has evolved significantly since its beginnings in the 1980s, with
continuous advancements driven by both industry and academia. Traditional AD systems …

Large Language Models (LLMs) have showcased remarkable proficiency in various
information-processing tasks. These tasks span from extracting data and summarizing …

End-to-end autonomous driving has received increasing attention due to its potential to
learn from large amounts of data. However, most existing methods are still open-loop and …

Autonomous driving has advanced significantly due to sensors, machine learning, and
artificial intelligence improvements. However, prevailing methods struggle with intricate …

While autonomous driving technology has made remarkable strides, data-driven
approaches still struggle with complex scenarios due to their limited reasoning capabilities …

Reinforcement Learning (RL) plays a crucial role in advancing autonomous driving
technologies by maximizing reward functions to achieve the optimal policy. However …

The Multi-modal Large Language Models (MLLMs) with extensive world knowledge have
revitalized autonomous driving, particularly in reasoning tasks within perceivable regions …

Ensuring safe, comfortable, and efficient navigation is a critical goal for autonomous driving
systems. While end-to-end models trained on large-scale datasets excel in common driving …

Embodied intelligence emphasizes direct interaction between machines and the physical
world, enabling intelligent agents to exhibit intelligent behaviors and autonomous evolution …

Autonomous driving (AD) technology promises to revolutionize daily transportation by
making it safer, more efficient, and more comfortable. Their role in reducing traffic accidents …