Artificial Intelligence (AI) technology's rapid advancement has significantly changed various
industries' operations. This comprehensive review paper aims to provide readers with a …

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles
(CAV), including perception, planning, and control. However, its reliance on vehicular data …

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 …

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 …

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 …

3D-LiDAR-based cooperative perception has been generating significant interest for its
ability to tackle challenges such as occlusion, sparse point clouds, and out-of-range issues …

Diverse transport demands have resulted in the wide existence of heterogeneous vehicle
automation systems. While these systems have demonstrated effectiveness, they also pose …

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 …

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and
connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger …

Localization is critical for automated vehicles as it provides essential position, velocity, and
heading angle information to perform object tracking, trajectory prediction, motion planning …