A survey of deep reinforcement learning algorithms for motion planning and control of autonomous vehicles
In this survey, we systematically summarize the current literature on studies that apply
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …
reinforcement learning (RL) to the motion planning and control of autonomous vehicles …
[HTML][HTML] Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends
The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the
safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully …
safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully …
[HTML][HTML] Toward trustworthy decision-making for autonomous vehicles: A robust reinforcement learning approach with safety guarantees
While autonomous vehicles are vital components of intelligent transportation systems,
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
ensuring the trustworthiness of decision-making remains a substantial challenge in realizing …
Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
Deductive reinforcement learning for visual autonomous urban driving navigation
Existing deep reinforcement learning (RL) are devoted to research applications on video
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …
games, eg, The Open Racing Car Simulator (TORCS) and Atari games. However, it remains …
Metafollower: Adaptable personalized autonomous car following
Car-following (CF) modeling, a fundamental component in microscopic traffic simulation, has
attracted increasing interest of researchers in the past decades. In this study, we propose an …
attracted increasing interest of researchers in the past decades. In this study, we propose an …
Knowledge augmented machine learning with applications in autonomous driving: A survey
The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …
artificial intelligence and machine learning models. However, in real life applications these …
Real-time intelligent autonomous intersection management using reinforcement learning
U Gunarathna, S Karunasekera… - 2022 IEEE Intelligent …, 2022 - ieeexplore.ieee.org
Autonomous intersection management has the ability to reduce congestion at intersections
significantly, compared to classical traffic signal control in the era of connected autonomous …
significantly, compared to classical traffic signal control in the era of connected autonomous …
Graph reinforcement learning application to co-operative decision-making in mixed autonomy traffic: Framework, survey, and challenges
Proper functioning of connected and automated vehicles (CAVs) is crucial for the safety and
efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous …
efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous …
Deep reinforcement learning approach for automated vehicle mandatory lane changing
R Ammourah, A Talebpour - Transportation research record, 2023 - journals.sagepub.com
This paper proposes a reinforcement learning-based framework for mandatory lane
changing of automated vehicles in a non-cooperative environment. The objective is to create …
changing of automated vehicles in a non-cooperative environment. The objective is to create …