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 …
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 …
Deep hierarchical reinforcement learning for autonomous driving with distinct behaviors
Deep reinforcement learning has achieved great progress recently in domains such as
learning to play Atari games from raw pixel input. The model-free characteristics of …
learning to play Atari games from raw pixel input. The model-free characteristics of …
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 …
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 …
Driving in dense traffic with model-free reinforcement learning
Traditional planning and control methods could fail to find a feasible trajectory for an
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …
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 …
Joint optimization of sensing, decision-making and motion-controlling for autonomous vehicles: A deep reinforcement learning approach
The three main modules of autonomous vehicles, ie, sensing, decision making, and motion
controlling, have been studied separately in most existing works on autonomous driving …
controlling, have been studied separately in most existing works on autonomous driving …
Overtaking maneuvers in simulated highway driving using deep reinforcement learning
Most methods that attempt to tackle the problem of Autonomous Driving and overtaking
usually try to either directly minimize an objective function or iteratively in a Reinforcement …
usually try to either directly minimize an objective function or iteratively in a Reinforcement …
Model-free deep reinforcement learning for urban autonomous driving
Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …
and multi-agent interactions. Current decision making methods are mostly manually …
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- reinforcement learning highway driving
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- highway driving overtaking maneuvers
- deep hierarchical reinforcement
- deep inverse reinforcement