Driveirl: Drive in real life with inverse reinforcement learning
T Phan-Minh, F Howington, TS Chu… - … on Robotics and …, 2023 - ieeexplore.ieee.org
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 …
using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a diverse set …
Driving in real life with inverse reinforcement learning
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 …
traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a …
An auto-tuning framework for autonomous vehicles
H Fan, Z Xia, C Liu, Y Chen, Q Kong - arXiv preprint arXiv:1808.04913, 2018 - arxiv.org
Many autonomous driving motion planners generate trajectories by optimizing a reward/cost
functional. Designing and tuning a high-performance reward/cost functional for Level-4 …
functional. Designing and tuning a high-performance reward/cost functional for Level-4 …
Deep inverse reinforcement learning for behavior prediction in autonomous driving: Accurate forecasts of vehicle motion
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 …
close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in …
Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning
General-purpose trajectory planning algorithms for automated driving utilize complex reward
functions to perform a combined optimization of strategic, behavioral, and kinematic …
functions to perform a combined optimization of strategic, behavioral, and kinematic …
Driving with style: Inverse reinforcement learning in general-purpose planning for automated driving
S Rosbach, V James, S Großjohann… - 2019 IEEE/RSJ …, 2019 - ieeexplore.ieee.org
Behavior and motion planning play an important role in automated driving. Traditionally,
behavior planners instruct local motion planners with predefined behaviors. Due to the high …
behavior planners instruct local motion planners with predefined behaviors. Due to the high …
Large-scale cost function learning for path planning using deep inverse reinforcement learning
We present an approach for learning spatial traversability maps for driving in complex, urban
environments based on an extensive dataset demonstrating the driving behaviour of human …
environments based on an extensive dataset demonstrating the driving behaviour of human …
Learning and adapting behavior of autonomous vehicles through inverse reinforcement learning
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 …
participants. Unlike current traffic participants, autonomous vehicles in the future will also …
Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories
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 …
component for many robotics applications, such as autonomous navigation and tracking …
Spatiotemporal costmap inference for MPC via deep inverse reinforcement learning
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 …
traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this …