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

Abstract Purpose of Review Current real-world interaction between humans and robots is
extremely limited. We present challenges that, if addressed, will enable humans and robots …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

While reinforcement learning (RL) has become a more popular approach for robotics,
designing sufficiently informative reward functions for complex tasks has proven to be …

Reward functions are difficult to design and often hard to align with human intent. Preference-
based Reinforcement Learning (RL) algorithms address these problems by learning reward …

A model used for velocity control during car following is proposed based on reinforcement
learning (RL). To optimize driving performance, a reward function is developed by …

Modern interactions with technology are increasingly moving away from simple human use
of computers as tools to the establishment of human relationships with autonomous entities …

Our goal is to efficiently learn reward functions encoding a human's preferences for how a
dynamical system should act. There are two challenges with this. First, in many problems it is …

Current driving behaviour models are designed for specific scenarios, such as curve driving,
obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse …

Automated Vehicle (AV) technology has the potential to significantly improve driver safety.
Unfortunately, drivers could be reluctant to ride with AVs due to their lack of trust and …