Deep reinforcement learning-based driving strategy for avoidance of chain collisions and its safety efficiency analysis in autonomous vehicles
Vehicle control in autonomous traffic flow is often handled using the best decision-making
reinforcement learning methods. However, unexpected critical situations make the collisions …
reinforcement learning methods. However, unexpected critical situations make the collisions …
A rear anti-collision decision-making methodology based on deep reinforcement learning for autonomous commercial vehicles
W Hu, X Li, J Hu, X Song, X Dong, D Kong… - IEEE Sensors …, 2022 - ieeexplore.ieee.org
Driving decision-making determines the safety and rationality of autonomous commercial
vehicles. Aiming at the issue of safe driving decision-making, herein, a rear anti-collision …
vehicles. Aiming at the issue of safe driving decision-making, herein, a rear anti-collision …
Unexpected collision avoidance driving strategy using deep reinforcement learning
In this paper, we generated intelligent self-driving policies that minimize the injury severity in
unexpected traffic signal violation scenarios at an intersection using the deep reinforcement …
unexpected traffic signal violation scenarios at an intersection using the deep reinforcement …
Optimal safety planning and driving decision-making for multiple autonomous vehicles: A learning based approach
In the early diffusion stage of autonomous vehicle systems, the controlling of vehicles
through exacting decision-making to reduce the number of collisions is a major problem …
through exacting decision-making to reduce the number of collisions is a major problem …
Learning-based conceptual framework for threat assessment of multiple vehicle collision in autonomous driving
AJM Muzahid, SF Kamarulzaman… - 2020 Emerging …, 2020 - ieeexplore.ieee.org
The autonomous driving is increasingly mounting, promoting, and promising the future of
fully autonomous and, correspondingly presenting new challenges in the field of safety …
fully autonomous and, correspondingly presenting new challenges in the field of safety …
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 …
Deep reinforcement learning with enhanced safety for autonomous highway driving
In this paper, we present a safe deep reinforcement learning system for automated driving.
The proposed framework leverages merits of both rule-based and learning-based …
The proposed framework leverages merits of both rule-based and learning-based …
Human as AI Mentor: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving
Despite significant progress in autonomous vehicles (AVs), the development of driving
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …
Self-awareness safety of deep reinforcement learning in road traffic junction driving
Z Cao, J Yun - arXiv preprint arXiv:2201.08116, 2022 - arxiv.org
Autonomous driving has been at the forefront of public interest, and a pivotal debate to
widespread concerns is safety in the transportation system. Deep reinforcement learning …
widespread concerns is safety in the transportation system. Deep reinforcement learning …
Automated driving maneuvers under interactive environment based on deep reinforcement learning
Safe and efficient autonomous driving maneuvers in an interactive and complex
environment can be considerably challenging due to the unpredictable actions of other …
environment can be considerably challenging due to the unpredictable actions of other …
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