The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety
systems that aim to identify critical events such as near-crash situations and traffic violations …

Autonomous driving in urban environments is challenging because there are many agents
located in the environment all with their own individual agendas. With accurate motion …

The driving behavior of autonomous vehicles has a significant impact on safety for all traffic
participants. Unlike current traffic participants, autonomous vehicles in the future will also …

The application of deep reinforcement learning in driving policy learning for automated
vehicles is limited by the difficulty of designing reward functions. Most existing inverse …

The driving style of an Autonomous Vehicle (AV) refers to how it behaves and interacts with
other AVs. In a multi-vehicle autonomous driving system, an AV capable of identifying the …

The literature on inverse reinforcement learning (IRL) typically assumes that humans take
actions to minimize the expected value of a cost function, ie, that humans are risk neutral …

To learn a reward function that a driver adheres to is of importance to the human-like design
of autonomous driving systems. Inverse reinforcement learning (IRL) is one of the recent …

The present study proposes a framework for learning the car‐following behavior of drivers
based on maximum entropy deep inverse reinforcement learning. The proposed framework …

We consider the problem of modeling trajectories of drivers in a road network from the
perspective of inverse reinforcement learning. Cars are detected by sensors placed on …

In this paper, we introduce the first published planner to drive a car in dense, urban traffic
using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set …