Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …

The recent proliferation of computing technologies (eg, sensors, computer vision, machine
learning, and hardware acceleration) and the broad deployment of communication …

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace
due to the convenience, safety, and economic benefits. Although a number of surveys have …

In this paper, we explore the potential of using a large language model (LLM) to understand
the driving environment in a human-like manner and analyze its ability to reason, interpret …

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 …

Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …

Existing learning-based autonomous driving (AD) systems face challenges in
comprehending high-level information, generalizing to rare events, and providing …

We study how vision-language models (VLMs) trained on web-scale data can be integrated
into end-to-end driving systems to boost generalization and enable interactivity with human …

End-to-end autonomous driving recently emerged as a promising research direction to
target autonomy from a full-stack perspective. Along this line many of the latest works follow …

End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …