Accurate behavior anticipation is essential for autonomous vehicles when navigating in
close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in …

Predicting the motion of a mobile agent from a third-person perspective is an important
component for many robotics applications, such as autonomous navigation and tracking …

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

With the development of deep representation learning, the domain of reinforcement learning
(RL) has become a powerful learning framework now capable of learning complex policies …

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 …

Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …

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 …

It can be difficult to autonomously produce driver behavior so that it appears natural to other
traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this …

Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …