Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning
X Bi, M He, Y Sun - arXiv preprint arXiv:2406.09755, 2024 - arxiv.org
Lane-changing decisions, which are crucial for autonomous vehicle path planning, face
practical challenges due to rule-based constraints and limited data. Deep reinforcement …
practical challenges due to rule-based constraints and limited data. Deep reinforcement …
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 …
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 …
LK-TDDQN: A Lane Keeping Transfer Double Deep Q Network Framework for Autonomous Vehicles
Autonomous driving has brought about a growing interest in enhancing traffic efficiency and
ensuring road safety. One of the fundamental functions of autonomous driving technology is …
ensuring road safety. One of the fundamental functions of autonomous driving technology is …
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 …
Automated lane change strategy using proximal policy optimization-based deep reinforcement learning
Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan,
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …
May i cut into your lane?: A policy network to learn interactive lane change behavior for autonomous driving
In this paper, we propose a new lane change policy network which can learn interactive lane
change behavior using reinforcement learning. The proposed policy network decides …
change behavior using reinforcement learning. The proposed policy network decides …
Lane changing using multi-agent dqn
K Nagarajan, Z Yi - 2021 IEEE International Conference on …, 2021 - ieeexplore.ieee.org
This study explores the feasibility of autonomous lane changing using a novel approach of
multi-agent Deep Q-Network. Most existing algorithms that use Deep Reinforcement …
multi-agent Deep Q-Network. Most existing algorithms that use Deep Reinforcement …
DQ-GAT: Towards safe and efficient autonomous driving with deep Q-learning and graph attention networks
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …
Lcs-tf: Multi-agent deep reinforcement learning-based intelligent lane-change system for improving traffic flow
Discretionary lane-change is one of the critical challenges for autonomous vehicle (AV)
design due to its significant impact on traffic efficiency. Existing intelligent lane-change …
design due to its significant impact on traffic efficiency. Existing intelligent lane-change …