Adversarial Attacks and Defense in Deep Reinforcement Learning (DRL)-Based Traffic Signal Controllers

Adversarial Attacks and Defense in Deep Reinforcement Learning (DRL)-Based Traffic Signal Controllers

Security attacks on intelligent transportation systems (ITS) may result in life-threatening situations. Combining deep neural networks with reinforcement learning (RL) models called DRL shows promising results when applied to urban Traffic Signal Control (TSC) for adaptive adjustment of traffic ligh...

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Bibliographic Details
Main Authors: Ammar Haydari, Michael Zhang, Chen-Nee Chuah
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Open Journal of Intelligent Transportation Systems
Subjects:
Deep reinforcement learning
statistical anomaly detection
traffic signal control
adversarial attack
security
Online Access:https://ieeexplore.ieee.org/document/9566311/

Internet

https://ieeexplore.ieee.org/document/9566311/

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