Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning
Autonomous vehicles promise to improve traffic safety while, at the same time, increase fuel
efficiency and reduce congestion. They represent the main trend in future intelligent …
efficiency and reduce congestion. They represent the main trend in future intelligent …
Highway traffic modeling and decision making for autonomous vehicle using reinforcement learning
This paper studies the decision making problem of autonomous vehicles in traffic. We model
the interaction between an autonomous vehicle and the environment as a stochastic Markov …
the interaction between an autonomous vehicle and the environment as a stochastic Markov …
Autonomous highway driving using deep reinforcement learning
The operational space of an autonomous vehicle (AV) can be diverse and vary significantly.
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …
Due to this, formulating a rule based decision maker for selecting driving maneuvers may …
Autonomous planning and control for intelligent vehicles in traffic
This paper addresses the trajectory planning problem for autonomous vehicles in traffic. We
build a stochastic Markov decision process (MDP) model to represent the behaviors of the …
build a stochastic Markov decision process (MDP) model to represent the behaviors of the …
Survey of deep reinforcement learning for motion planning of autonomous vehicles
S Aradi - IEEE Transactions on Intelligent Transportation …, 2020 - ieeexplore.ieee.org
Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …
recent years related to several topics as sensor technologies, V2X communications, safety …
Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving
In the past decades, we have witnessed significant progress in the domain of autonomous
driving. Advanced techniques based on optimization and reinforcement learning become …
driving. Advanced techniques based on optimization and reinforcement learning become …
Deep reinforcement learning framework for autonomous driving
Reinforcement learning is considered to be a strong AI paradigm which can be used to
teach machines through interaction with the environment and learning from their mistakes …
teach machines through interaction with the environment and learning from their mistakes …
Reinforcement learning-based autonomous driving at intersections in CARLA simulator
R Gutiérrez-Moreno, R Barea, E López-Guillén… - Sensors, 2022 - mdpi.com
Intersections are considered one of the most complex scenarios in a self-driving framework
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …
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 …
Controlling an autonomous vehicle with deep reinforcement learning
A Folkers, M Rick, C Büskens - 2019 IEEE intelligent vehicles …, 2019 - ieeexplore.ieee.org
We present a control approach for autonomous vehicles based on deep reinforcement
learning. A neural network agent is trained to map its estimated state to acceleration and …
learning. A neural network agent is trained to map its estimated state to acceleration and …