Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving
and transportation systems to digitize and synergize connected automated vehicles …

Construction machinery and robots are essential equipment for major infrastructure. The
application of deep learning technology can improve the construction quality and alleviate …

To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …

The air-conditioning system (ACS), as a high-power component on the fuel cell hybrid
electric bus (FCHEB), has a significant impact on the whole vehicle economy while …

Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …

Driver anomaly quantification is a fundamental capability to support human-centric driving
systems of intelligent vehicles. Existing studies usually treat it as a classification task and …

Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …

Modelling, predicting and analysing driver behaviours are essential to advanced driver
assistance systems (ADAS) and the comprehensive understanding of complex driving …

Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs)
applications, but limited computing resource makes it challenging to deploy a well-behaved …

While autonomous vehicles are vital components of intelligent transportation systems,
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …