This article presents a comprehensive survey of deep learning applications for object
detection and scene perception in autonomous vehicles. Unlike existing review papers, we …

Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …

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 …

In many robotic tasks, such as autonomous drone racing, the goal is to travel through a set of
waypoints as fast as possible. A key challenge for this task is planning the timeoptimal …

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Improper handling of on-ramp merging may cause severe decrease of traffic efficiency and
contribute to lower fuel economy, even increasing the collision risk. Cooperative control for …

Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

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 …

In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …