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 …

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 …

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 …

Autonomous driving is one of the current cutting edge technologies. For autonomous cars,
their driving actions and trajectories should not only achieve autonomy and safety, but also …

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 …

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 present TartanDrive, a large scale dataset for learning dynamics models for off-road
driving. We collected a dataset of roughly 200,000 off-road driving interactions on a modified …

Predicting crowd behaviour in the distant future has increased in prominence among the
computer vision community as it provides intelligence and flexibility for autonomous …

Enabling robots to autonomously navigate complex environments is essential for real-world
deployment. Prior methods approach this problem by having the robot maintain an internal …

Driving at an unsignalized intersection is a complex traffic scenario that requires both traffic
safety and efficiency. At the unsignalized intersection, the driving policy does not simply …