Decision-making for autonomous vehicles on highway: Deep reinforcement learning with continuous action horizon
Decision-making strategy for autonomous vehicles de-scribes a sequence of driving
maneuvers to achieve a certain navigational mission. This paper utilizes the deep …
maneuvers to achieve a certain navigational mission. This paper utilizes the deep …
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 …
An empirical study of ddpg and ppo-based reinforcement learning algorithms for autonomous driving
S Siboo, A Bhattacharyya, RN Raj, SH Ashwin - IEEE Access, 2023 - ieeexplore.ieee.org
Autonomous vehicles mitigate road accidents and provide safe transportation with a smooth
traffic flow. They are expected to greatly improve the quality of the elderly or people with …
traffic flow. They are expected to greatly improve the quality of the elderly or people with …
Confrontation and Obstacle-Avoidance of Unmanned Vehicles Based on Progressive Reinforcement Learning
C Ma, J Liu, S He, W Hong, J Shi - IEEE Access, 2023 - ieeexplore.ieee.org
The core technique of unmanned vehicle systems is the autonomous maneuvering decision,
which not only determines the applications of unmanned vehicles but also is the critical …
which not only determines the applications of unmanned vehicles but also is the critical …
Demystifying deep reinforcement learning-based autonomous vehicle decision-making
With the advent of universal function approximators in the domain of reinforcement learning,
the number of practical applications leveraging deep reinforcement learning (DRL) has …
the number of practical applications leveraging deep reinforcement learning (DRL) has …
Trajectory planning for autonomous vehicles using hierarchical reinforcement learning
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
Deep reinforcement learning on autonomous driving policy with auxiliary critic network
Y Wu, S Liao, X Liu, Z Li, R Lu - IEEE transactions on neural …, 2021 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) is a machine learning method based on rewards, which
can be extended to solve some complex and realistic decision-making problems …
can be extended to solve some complex and realistic decision-making problems …
Deep reinforcement learning for autonomous vehicles: lane keep and overtaking scenarios with collision avoidance
SH Ashwin, R Naveen Raj - International Journal of Information …, 2023 - Springer
Numerous accidents and fatalities occur every year across the world as a result of the
reckless driving of drivers and the ever-increasing number of vehicles on the road. Due to …
reckless driving of drivers and the ever-increasing number of vehicles on the road. Due to …
Autonomous driving system using proximal policy optimization in deep reinforcement learning
NY Imam, E Rachmawati - IAES International Journal of …, 2023 - search.proquest.com
Autonomous driving is one solution that can minimize and even prevent accidents. In
autonomous driving, the vehicle must know the surrounding environment and move under …
autonomous driving, the vehicle must know the surrounding environment and move under …
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 …
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