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 …
Deep reinforcement learning enabled decision-making for autonomous driving at intersections
Road intersection is one of the most complex and accident-prone traffic scenarios, so it's
challenging for autonomous vehicles (AVs) to make safe and efficient decisions at the …
challenging for autonomous vehicles (AVs) to make safe and efficient decisions at the …
Learning automated driving in complex intersection scenarios based on camera sensors: A deep reinforcement learning approach
Making proper decisions at intersections that are one of the most dangerous and
sophisticated driving scenarios is full of challenges, especially for autonomous vehicles …
sophisticated driving scenarios is full of challenges, especially for autonomous vehicles …
Safe and rule-aware deep reinforcement learning for autonomous driving at intersections
C Zhang, K Kacem, G Hinz… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
Driving through complex urban environments is a challenging task for autonomous vehicles
(AVs), as they must safely reach their mission goal, and react properly to traffic participants …
(AVs), as they must safely reach their mission goal, and react properly to traffic participants …
Prediction based decision making for autonomous highway driving
Autonomous driving decision-making is a challenging task due to the inherent complexity
and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake …
and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake …
Decision-making at unsignalized intersection for autonomous vehicles: Left-turn maneuver with deep reinforcement learning
Decision-making module enables autonomous vehicles to reach appropriate maneuvers in
the complex urban environments, especially the intersection situations. This work proposes …
the complex urban environments, especially the intersection situations. This work proposes …
Risk-aware high-level decisions for automated driving at occluded intersections with reinforcement learning
Reinforcement learning is nowadays a popular framework for solving different decision
making problems in automated driving. However, there are still some remaining crucial …
making problems in automated driving. However, there are still some remaining crucial …
Hierarchical framework integrating rapidly-exploring random tree with deep reinforcement learning for autonomous vehicle
This paper proposes a systematic driving framework where the decision making module of
reinforcement learning (RL) is integrated with rapidly-exploring random tree (RRT) as …
reinforcement learning (RL) is integrated with rapidly-exploring random tree (RRT) as …
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 …
An integrated decision-making framework for highway autonomous driving using combined learning and rule-based algorithm
C Xu, W Zhao, J Liu, C Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In order to solve the manual labelling, long-tail effect and driving conservatism of the existing
decision-making algorithm. This paper proposed an integrated decision-making framework …
decision-making algorithm. This paper proposed an integrated decision-making framework …