An Inverse Reinforcement Learning Approach to Car Following Behaviors
In this study the authors provide new insights into the classic car-following theories by
learning drivers' behavioral preferences. The authors model car-following behavior using …
learning drivers' behavioral preferences. The authors model car-following behavior using …
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 …
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 …
Continuous deep maximum entropy inverse reinforcement learning using online POMDP
A vehicle navigating in an urban environment must obey traffic rules by properly setting its
speed, such as staying below the road speed limit and avoiding collision with other vehicles …
speed, such as staying below the road speed limit and avoiding collision with other vehicles …
Maximum Entropy Inverse Reinforcement Learning Based on Frenet Frame Sampling for Human-Like Autonomous Driving
Ensuring social acceptability is a key factor in the successful operation of autonomous
vehicles. To achieve this, it is important to extract driving habits from expert human drivers …
vehicles. To achieve this, it is important to extract driving habits from expert human drivers …
Estimation of personal driving style via deep inverse reinforcement learning
D Kishikawa, S Arai - Artificial Life and Robotics, 2021 - Springer
When applying autonomous driving technology in human-crewed vehicles, it is essential to
consider the personal driving style with ensuring not only safety but also the driver's …
consider the personal driving style with ensuring not only safety but also the driver's …
Maximum Entropy Inverse Reinforcement Learning Using Monte Carlo Tree Search for Autonomous Driving
JAR da Silva, V Grassi, DF Wolf - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Autonomous vehicles must be capable of driving safely and having some level of social
compliance with human drivers. Acting egoistically can make other drivers to take …
compliance with human drivers. Acting egoistically can make other drivers to take …
A cascaded supervised learning approach to inverse reinforcement learning
This paper considers the Inverse Reinforcement Learning (IRL) problem, that is inferring a
reward function for which a demonstrated expert policy is optimal. We propose to break the …
reward function for which a demonstrated expert policy is optimal. We propose to break the …
Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach
This paper develops an integrated safety-enhanced reinforcement learning (RL) and model
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …