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

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 …

With the emergence of Large Language Models (LLMs) and Vision Foundation Models
(VFMs), multimodal AI systems benefiting from large models have the potential to equally …

Recent advancements in autonomous driving have relied on data-driven approaches, which
are widely adopted but face challenges including dataset bias, overfitting, and …

In 2014, IEEE Intelligent Transportation Systems Society established a Technical Committee
on Transportation 5.0 with the mission of promoting and transforming the deployment of …

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 …

This letter reports on a TIV DHW (decentralized and hybrid workshop) that explores the
prospective influence of ChatGPT on research and development in intelligent vehicles. To …

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 …

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

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 …