Deep reinforcement learning for human-like driving policies in collision avoidance tasks of self-driving cars
R Emuna, A Borowsky, A Biess - arXiv preprint arXiv:2006.04218, 2020 - arxiv.org
The technological and scientific challenges involved in the development of autonomous
vehicles (AVs) are currently of primary interest for many automobile companies and …
vehicles (AVs) are currently of primary interest for many automobile companies and …
Dynamic input for deep reinforcement learning in autonomous driving
M Huegle, G Kalweit, B Mirchevska… - 2019 IEEE/RSJ …, 2019 - ieeexplore.ieee.org
In many real-world decision making problems, reaching an optimal decision requires taking
into account a variable number of objects around the agent. Autonomous driving is a domain …
into account a variable number of objects around the agent. Autonomous driving is a domain …
Unexpected collision avoidance driving strategy using deep reinforcement learning
In this paper, we generated intelligent self-driving policies that minimize the injury severity in
unexpected traffic signal violation scenarios at an intersection using the deep reinforcement …
unexpected traffic signal violation scenarios at an intersection using the deep reinforcement …
A decision-making strategy for vehicle autonomous braking in emergency via deep reinforcement learning
Autonomous braking through vehicle precise decision-making and control to reduce
accidents is a key issue, especially in the early diffusion phase of autonomous vehicle …
accidents is a key issue, especially in the early diffusion phase of autonomous vehicle …
Deductive reinforcement learning for visual autonomous urban driving navigation
Existing deep reinforcement learning (RL) are devoted to research applications on video
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …
A three-level game-theoretic decision-making framework for autonomous vehicles
In this paper, a three-level decision-making framework is developed to generate safe and
effective decisions for autonomous vehicles (AVs). A key component in this decision …
effective decisions for autonomous vehicles (AVs). A key component in this decision …
Game theoretic decision making for autonomous vehicles' merge manoeuvre in high traffic scenarios
M Garzón, A Spalanzani - 2019 IEEE Intelligent Transportation …, 2019 - ieeexplore.ieee.org
This paper presents a game theoretic decision making process for autonomous vehicles. Its
goal is to provide a solution for a very challenging task: the merge manoeuvre in high traffic …
goal is to provide a solution for a very challenging task: the merge manoeuvre in high traffic …
Driving decision and control for automated lane change behavior based on deep reinforcement learning
To fulfill high-level automation, an automated vehicle needs to learn to make decisions and
control its movement under complex scenarios. Due to the uncertainty and complexity of the …
control its movement under complex scenarios. Due to the uncertainty and complexity of the …
Decision-making strategy on highway for autonomous vehicles using deep reinforcement learning
Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …
driving efficiency. In this work, a deep reinforcement learning (DRL)-enabled decision …
A deep reinforcement learning approach for autonomous highway driving
J Zhao, T Qu, F Xu - IFAC-PapersOnLine, 2020 - Elsevier
Autonomous driving has been the trend. In this paper, a Deep Reinforcement Learning
(DRL) method is exploited to model the decision making and interaction between vehicles …
(DRL) method is exploited to model the decision making and interaction between vehicles …