Probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning
Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road
participants, AVs have to accurately predict the behavior of surrounding vehicles and plan …
participants, AVs have to accurately predict the behavior of surrounding vehicles and plan …
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 behavior modeling using naturalistic human driving data with inverse reinforcement learning
Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …
personalized autonomous driving systems. In this paper, an internal reward function-based …
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 …
Modeling the effects of autonomous vehicles on human driver car-following behaviors using inverse reinforcement learning
The development of autonomous driving technology will lead to a transition period during
which human-driven vehicles (HVs) will share the road with autonomous vehicles (AVs) …
which human-driven vehicles (HVs) will share the road with autonomous vehicles (AVs) …
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 …
Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making
There are still some problems need to be solved though there are a lot of achievements in
the fields of automatic driving. One of those problems is the difficulty of designing a car …
the fields of automatic driving. One of those problems is the difficulty of designing a car …
Analyzing the suitability of cost functions for explaining and imitating human driving behavior based on inverse reinforcement learning
Autonomous vehicles are sharing the road with human drivers. In order to facilitate
interactive driving and cooperative behavior in dense traffic, a thorough understanding and …
interactive driving and cooperative behavior in dense traffic, a thorough understanding and …
Online prediction of lane change with a hierarchical learning-based approach
In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven
vehicles will share the road networks together. In such a mixed traffic environment, CAVs …
vehicles will share the road networks together. In such a mixed traffic environment, CAVs …
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 …
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