Several applications of artificially modeled drivers, such as autonomous vehicles (AVs) or
surrounding traffic in driving simulations, aim to provide not only functional but also human …

Various studies have been conducted on Multi-Agent Reinforcement Learning (MARL) to
control multiple agents to drive effectively and safely in a simulation, demonstrating the …

Automated Vehicles (AVs) have started operating on public roads, with unmanned taxi
services offered, for instance, in Beijing and San Francisco. The coexistence of AVs and …

With the development of autonomous driving, future traffic will be composed of various
participants. Integrating autonomous vehicles into the traffic flow composed of various types …

Despite the potential of autonomous vehicles (AV) to improve traffic efficiency and safety,
many studies have shown that traffic accidents in a hybrid traffic environment where both …

Decision-making for autonomous driving incorporating different types of risks is a
challenging topic. This paper proposes a novel risk metric to facilitate the driving task …

Effective driving style analysis is critical to developing human-centered intelligent driving
systems that consider drivers' preferences. However, the approaches and conclusions of …

Understanding cultural backgrounds is crucial for the seamless integration of autonomous
driving into daily life as it ensures that systems are attuned to diverse societal norms and …

Assessing drivers' interaction capabilities is crucial for understanding human driving
behaviour and enhancing the interactive abilities of autonomous vehicles. In scenarios …

Autonomous vehicles (AVs) have the potential to revolutionize transportation, but their
effective integration into the real world requires addressing the challenge of interacting with …