Verification and validation (V&V) hold a significant position in the research and development
of automated vehicles (AVs). Current literature indicates that different V&V techniques have …

In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

Artificial intelligence (AI) continues to transform firm-customer interactions. However, current
AI marketing agents are often perceived as cold and uncaring and can be poor substitutes …

In this study, a decision-making and motion planning controller with continuous action space
is constructed in the highway driving scenario based on deep reinforcement learning. In the …

Merging is, in general, a challenging task for both human drivers and autonomous vehicles,
especially in dense traffic, because the merging vehicle typically needs to interact with other …

An outstanding challenge with safety methods for human-robot interaction is reducing their
conservatism while maintaining robustness to variations in human behavior. In this work, we …

In this paper, we develop a socially cooperative optimal control framework to address the
motion planning problem for connected and automated vehicles (CAVs) in mixed traffic …

Inverse Reinforcement Learning (IRL) is attractive in scenarios where reward engineering
can be tedious. However, prior IRL algorithms use on-policy transitions, which require …

The advent of autonomous vehicles brings major changes in the transportation systems
influencing the infrastructure design, the network performance, as well as driving functions …

Studying the representational capacity of neural networks to learn nonlinear rewards is
necessary in a complex and nonlinear environment. Over recent years, the maximum …