Decision-making for oncoming traffic overtaking scenario using double DQN
S Mo, X Pei, Z Chen - 2019 3rd Conference on Vehicle Control …, 2019 - ieeexplore.ieee.org
Great progress has been made in the field of machine learning in recent years. And learning-
based methods have been widely utilized for developing highly autonomous vehicle. To this …
based methods have been widely utilized for developing highly autonomous vehicle. To this …
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 …
Enhanced decision making in multi-scenarios for autonomous vehicles using alternative bidirectional Q network
MS Rais, K Zouaidia, R Boudour - Neural Computing and Applications, 2022 - Springer
To further enhance decision making in autonomous vehicles field, grant more safety,
comfort, reduce traffic, and accidents, learning approaches were adopted, mainly …
comfort, reduce traffic, and accidents, learning approaches were adopted, mainly …
Lane Change Decision-Making through Deep Reinforcement Learning
M Ghimire, MR Choudhury, GSSH Lagudu - arXiv preprint arXiv …, 2021 - arxiv.org
Due to the complexity and volatility of the traffic environment, decision-making in
autonomous driving is a significantly hard problem. In this project, we use a Deep Q …
autonomous driving is a significantly hard problem. In this project, we use a Deep Q …
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 …
Decision-making for complex scenario using safe reinforcement learning
J Xu, X Pei, K Lv - 2020 4th CAA International Conference on …, 2020 - ieeexplore.ieee.org
In recent years, machine learning is widely used in many fields. Compared with the rule-
based method, machine learning plays a more excellent role in the decision-making of the …
based method, machine learning plays a more excellent role in the decision-making of the …
Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network
N Deshpande, D Vaufreydaz… - 2020 16th international …, 2020 - ieeexplore.ieee.org
Decision making for autonomous driving in urban environments is challenging due to the
complexity of the road structure and the uncertainty in the behavior of diverse road users …
complexity of the road structure and the uncertainty in the behavior of diverse road users …
Lane change decision-making through deep reinforcement learning with rule-based constraints
Autonomous driving decision-making is a great challenge due to the complexity and
uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q …
uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q …
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 …
Dueling deep Q network for highway decision making in autonomous vehicles: A case study
This work optimizes the highway decision making strategy of autonomous vehicles by using
deep reinforcement learning (DRL). First, the highway driving environment is built, wherein …
deep reinforcement learning (DRL). First, the highway driving environment is built, wherein …