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 …
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 …
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 …
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 …
Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving
Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …
systems, and learning-based behavior planning presents a promising pathway toward …
Learning the Car‐following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning
Y Zhou, R Fu, C Wang - Journal of advanced transportation, 2020 - Wiley Online Library
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 …
based on maximum entropy deep inverse reinforcement learning. The proposed framework …
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 …
Efficient reinforcement learning for autonomous driving with parameterized skills and priors
When autonomous vehicles are deployed on public roads, they will encounter countless and
diverse driving situations. Many manually designed driving policies are difficult to scale to …
diverse driving situations. Many manually designed driving policies are difficult to scale to …
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 deep reinforcement learning with model-based path planners for automated driving
Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …