This paper develops an integrated safety-enhanced reinforcement learning (RL) and model
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …

We consider the crucial task of estimating an expert's discount factor in Inverse
Reinforcement Learning (IRL) to facilitate a better synthesis towards the resulting optimal …

Existing inverse reinforcement learning (IRL) algorithms have assumed each expert's
demonstrated trajectory to be produced by only a single reward function. This paper …

Inverse reinforcement learning (IRL) has been successfully applied in many robotics and
autonomous driving studies without the need for hand-tuning a reward function. However, it …

Human drivers have different driver behaviors when operating vehicles. These driving
behaviors, including the driver's preferred speed and rate of acceleration, impose a major …

As autonomous driving technology continues to advance and gradually become a reality,
ensuring the safety of autonomous driving in complex traffic scenarios has become a key …

Driving behavior modeling is crucial in autonomous driving systems for preventing traffic
accidents. Inverse reinforcement learning (IRL) allows autonomous agents to learn …

Advanced driver assistance systems have gained popularity as a safe technology that helps
people avoid traffic accidents. To improve system reliability, a lot of research on driving …

One of the most challenging problems in autonomous driving is trajectory planning for lane
changes. Conventional trajectory planning is generally realized by optimizing a specific cost …

Abstract Inverse Reinforcement Learning (IRL) deals with the problem of recovering the
reward function optimized by an expert given a set of demonstrations of the expert's policy …