Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …

In his blogs of 2012, Dr. Göte Nyman coined Internet of Behaviors (IoB). In his idea, people's
behaviors are very good predictors of their needs, and hence technology companies can …

In a mixed traffic environment of human and autonomous driving, it is crucial for an
autonomous vehicle to predict the lane change intentions and trajectories of vehicles that …

Lane change is a common-yet-challenging driving behavior for automated vehicles. To
improve the safety and efficiency of automated vehicles, researchers have proposed various …

Since traditional reinforcement learning (RL) approaches need active online interaction with
the environment, previous works are mainly investigated in the simulation environment …

Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can
be used for validation and verification of self-driving system performance without relying on …

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based
method for operational decision-making in industrial processes. Due to the frequent …

Dear Editor, This letter presents an inspection method for process monitoring of underwater
oil transportation via multiple autonomous underwater vehicles (AUV). To improve the …

Autonomous driving systems are crucial complicated cyber–physical systems that combine
physical environment awareness with cognitive computing. Deep reinforcement learning is …

Safety limits the application of traditional reinforcement learning (RL) methods to
autonomous driving. To address the challenge of safe exploration in autonomous driving …