Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment
Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation
system in the future. Many studies have been made to improve AVs' ability of environment …
system in the future. Many studies have been made to improve AVs' ability of environment …
Adaptive decision-making for automated vehicles under roundabout scenarios using optimization embedded reinforcement learning
The roundabout is a typical changeable, interactive scenario in which automated vehicles
should make adaptive and safe decisions. In this article, an optimization embedded …
should make adaptive and safe decisions. In this article, an optimization embedded …
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 …
Velocity control in car-following behavior with autonomous vehicles using reinforcement learning
Car-following behavior is a common driving behavior. It is necessary to consider the
following vehicle in the car-following model of autonomous vehicle (AV) under the …
following vehicle in the car-following model of autonomous vehicle (AV) under the …
Continuous decision‐making for autonomous driving at intersections using deep deterministic policy gradient
Intersections have been identified as the most complex and accident‐prone traffic scenarios
on road. Making appropriate decisions at intersections for driving safety, efficiency, and …
on road. Making appropriate decisions at intersections for driving safety, efficiency, and …
Driving behavior modeling and characteristic learning for human-like decision-making in highway
C Xu, W Zhao, C Wang, T Cui… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
To make autonomous vehicles consider driver's personalized characteristics, this paper
proposes an integrated model and learning combined (IMLC) algorithm to realize human …
proposes an integrated model and learning combined (IMLC) algorithm to realize human …
Online prediction of lane change with a hierarchical learning-based approach
In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven
vehicles will share the road networks together. In such a mixed traffic environment, CAVs …
vehicles will share the road networks together. In such a mixed traffic environment, CAVs …
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 …
Deep reinforcement learning based game-theoretic decision-making for autonomous vehicles
This letter presents an approach for implementing game-theoretic decision-making in
combination with deep reinforcement learning to allow vehicles to make decisions at an …
combination with deep reinforcement learning to allow vehicles to make decisions at an …
Deep learning-based vehicle behavior prediction for autonomous driving applications: A review
S Mozaffari, OY Al-Jarrah, M Dianati… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Behaviour prediction function of an autonomous vehicle predicts the future states of the
nearby vehicles based on the current and past observations of the surrounding environment …
nearby vehicles based on the current and past observations of the surrounding environment …
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