Robust deep reinforcement learning for security and safety in autonomous vehicle systems
The dependence of autonomous vehicles (AVs) on sensors and communication links
exposes them to cyber-physical (CP) attacks by adversaries that seek to take control of the …
exposes them to cyber-physical (CP) attacks by adversaries that seek to take control of the …
Deep reinforcement learning approach for autonomous vehicle systems for maintaining security and safety using LSTM-GAN
The success of autonomous vehicles (AV hs) depends upon the effectiveness of sensors
being used and the accuracy of communication links and technologies being employed. But …
being used and the accuracy of communication links and technologies being employed. But …
Cyber-physical security and safety of autonomous connected vehicles: Optimal control meets multi-armed bandit learning
Autonomous connected vehicles (ACVs) rely on intra-vehicle sensors such as camera and
radar as well as inter-vehicle communication to operate effectively which exposes them to …
radar as well as inter-vehicle communication to operate effectively which exposes them to …
Improved robustness and safety for autonomous vehicle control with adversarial reinforcement learning
X Ma, K Driggs-Campbell… - 2018 IEEE Intelligent …, 2018 - ieeexplore.ieee.org
To improve efficiency and reduce failures in autonomous vehicles, research has focused on
developing robust and safe learning methods that take into account disturbances in the …
developing robust and safe learning methods that take into account disturbances in the …
Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations
Despite the substantial advancements in reinforcement learning (RL) in recent years,
ensuring trustworthiness remains a formidable challenge when applying this technology to …
ensuring trustworthiness remains a formidable challenge when applying this technology to …
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 …
Stop-and-go: Exploring backdoor attacks on deep reinforcement learning-based traffic congestion control systems
Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could
help reduce traffic jams. Deep reinforcement learning methods demonstrate good …
help reduce traffic jams. Deep reinforcement learning methods demonstrate good …
Covert attacks through adversarial learning: Study of lane keeping attacks on the safety of autonomous vehicles
Road management systems are to improve in terms of integrity, mobility, sustainability, and
safety by the adoption of artificial intelligence and Internet of Things services. This article …
safety by the adoption of artificial intelligence and Internet of Things services. This article …
Adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles
V Behzadan, A Munir - IEEE Intelligent Transportation Systems …, 2019 - ieeexplore.ieee.org
With the rapidly growing interest in autonomous navigation, the body of research on motion
planning and collision avoidance techniques has enjoyed an accelerating rate of novel …
planning and collision avoidance techniques has enjoyed an accelerating rate of novel …
Safe reinforcement learning for autonomous vehicles through parallel constrained policy optimization
Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to
its potential to solve complex classification and control problems. However, existing RL …
its potential to solve complex classification and control problems. However, existing RL …
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