Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles
Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving
and transportation systems to digitize and synergize connected automated vehicles …
and transportation systems to digitize and synergize connected automated vehicles …
Deep learning technology for construction machinery and robotics
K You, C Zhou, L Ding - Automation in construction, 2023 - Elsevier
Construction machinery and robots are essential equipment for major infrastructure. The
application of deep learning technology can improve the construction quality and alleviate …
application of deep learning technology can improve the construction quality and alleviate …
Health-aware energy management strategy for fuel cell hybrid bus considering air-conditioning control based on TD3 algorithm
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 …
electric bus (FCHEB), has a significant impact on the whole vehicle economy while …
Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving
Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …
systems, and learning-based behavior planning presents a promising pathway toward …
Human-guided reinforcement learning with sim-to-real transfer for autonomous navigation
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 …
applications, but limited computing resource makes it challenging to deploy a well-behaved …
Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information
The escalating level of vehicle electrification and intelligence makes higher requirements for
the energy management strategy (EMS) of fuel cell vehicles. Environmental and road …
the energy management strategy (EMS) of fuel cell vehicles. Environmental and road …
Confidence-aware reinforcement learning for energy management of electrified vehicles
The reliability of data-driven techniques, such as deep reinforcement learning (DRL)
frequently diminishes in scenarios beyond their training environments. Despite DRL-based …
frequently diminishes in scenarios beyond their training environments. Despite DRL-based …
A reinforcement learning-based routing algorithm for large street networks
Evacuation planning and emergency routing systems are crucial in saving lives during
disasters. Traditional emergency routing systems, despite their best efforts, often struggle to …
disasters. Traditional emergency routing systems, despite their best efforts, often struggle to …
Quantitative identification of driver distraction: A weakly supervised contrastive learning approach
Accurate recognition of driver distraction is significant for the design of human-machine
cooperation driving systems. Existing studies mainly focus on classifying varied distracted …
cooperation driving systems. Existing studies mainly focus on classifying varied distracted …
Deep Reinforcement Learning-Based Energy-Efficient Decision-Making for Autonomous Electric Vehicle in Dynamic Traffic Environments
Autonomous driving techniques are promising for improving the energy efficiency of
electrified vehicles (EVs) by adjusting driving decisions and optimizing energy requirements …
electrified vehicles (EVs) by adjusting driving decisions and optimizing energy requirements …