[HTML][HTML] A Review on Reinforcement Learning-based Highway Autonomous Vehicle Control
Autonomous driving is an active area of research in artificial intelligence and robotics.
Recent advances in deep reinforcement learning (DRL) show promise for training …
Recent advances in deep reinforcement learning (DRL) show promise for training …
Cooperative lane-changing in mixed traffic: a deep reinforcement learning approach
Deep Reinforcement Learning (DRL) has made remarkable progress in autonomous vehicle
decision-making and execution control to improve traffic performance. This paper introduces …
decision-making and execution control to improve traffic performance. This paper introduces …
A Real-World Reinforcement Learning Framework for Safe and Human-Like Tactical Decision-Making
Lane-change decision-making for vehicles is a challenging task for many reasons, including
traffic rules, safety, and the stochastic nature of driving. Because of its success in solving …
traffic rules, safety, and the stochastic nature of driving. Because of its success in solving …
Autonomous highway merging in mixed traffic using reinforcement learning and motion predictive safety controller
Deep reinforcement learning (DRL) has a great potential for solving complex decision-
making problems in autonomous driving, especially in mixed-traffic scenarios where …
making problems in autonomous driving, especially in mixed-traffic scenarios where …
RACE-SM: Reinforcement Learning Based Autonomous Control for Social On-Ramp Merging
J Poots - arXiv preprint arXiv:2403.03359, 2024 - arxiv.org
Autonomous parallel-style on-ramp merging in human controlled traffic continues to be an
existing issue for autonomous vehicle control. Existing non-learning based solutions for …
existing issue for autonomous vehicle control. Existing non-learning based solutions for …
Learning highway ramp merging via reinforcement learning with temporally-extended actions
Several key scenarios, such as intersection navigation, lane changing, and ramp merging,
are active areas of research in autonomous driving. In order to properly navigate these …
are active areas of research in autonomous driving. In order to properly navigate these …
A comparative analysis of deep reinforcement learning-enabled freeway decision-making for automated vehicles
Deep reinforcement learning (DRL) has emerged as a pervasive and potent methodology
for addressing artificial intelligence challenges. Due to its substantial potential for …
for addressing artificial intelligence challenges. Due to its substantial potential for …
Performance Comparison of Deep RL Algorithms for Mixed Traffic Cooperative Lane-Changing
Lane-changing (LC) is a challenging scenario for connected and automated vehicles
(CAVs) because of the complex dynamics and high uncertainty of the traffic environment …
(CAVs) because of the complex dynamics and high uncertainty of the traffic environment …
On-ramp merging for connected autonomous vehicles using deep reinforcement learning
C Mahabal, H Fang, H Wang - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Ramp merging is a major challenge which leads to bottleneck congestion on the freeway,
rear-end and side collisions. The automated vehicle must safely execute this complex action …
rear-end and side collisions. The automated vehicle must safely execute this complex action …
Automated driving highway traffic merging using deep multi-agent reinforcement learning in continuous state-action spaces
L Schester, LE Ortiz - 2021 IEEE Intelligent Vehicles …, 2021 - ieeexplore.ieee.org
Achieving the highest levels of automated driving will require effective solutions to the key
challenging maneuver of highway on-ramp merging. This paper extends our previous work …
challenging maneuver of highway on-ramp merging. This paper extends our previous work …