The rapid development of autonomous driving technology has driven continuous innovation
in perception systems, with 4D millimeter-wave (mmWave) radar being one of the key …

This paper utilizes parallel control to investigate the problem of event-triggered deep
reinforcement learning and develops an event-triggered deep Q-network (ETDQN) for …

This article is focused on bibliographic analysis and collaboration pattern analysis of the text
papers published in the IEEE Transactions on Intelligent Vehicles (TIV) from January 2019 …

As driving scenarios and autonomous vehicles (AVs) become increasingly intricating, there
is an increasing need for innovative frameworks that can enhance and test AV capabilities …

Scenario engineering plays a vital role in various Industry 5.0 applications. In the field of
autonomous driving systems, driving scenario data are important for the training and testing …

The latest developments in parallel driving foreshadow the possibility of delivering
intelligence across organizations using foundation models. As is well-known, there are …

Although deep reinforcement learning (DRL) methods are promising for making behavioral
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …

The development of smart cities has been a significant trend in recent years, aiming to
improve the quality of life of citizens by leveraging technology and data. However, the …

Lane kee** assistance system (LKAS) is critical components of autonomous driving
technologies, and their reliable operation is crucial to ensure passenger safety and maintain …

Intelligent systems and human-machine interactions have consistently provided
convenience in both work and daily life. Artificial Intelligence Generated Content (AIGC) can …