Physical Backdoor Trigger Activation of Autonomous Vehicle using Reachability Analysis
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Abstract (arXiv)
Recent studies reveal that Autonomous Vehicles (AVs) can be manipulated by hidden backdoors, causing them to perform harmful actions when activated by physical triggers. However, it is still unclear how these triggers can be activated while adhering to traffic principles. Understanding this vulnerability in a dynamic traffic environment is crucial. This work addresses this gap by presenting physical trigger activation as a reachability problem of controlled dynamic system. Our technique identifies security-critical areas in traffic systems where trigger conditions for accidents can be reached, and provides intended trajectories for how those conditions can be reached. Testing on typical traffic scenarios showed the system can be successfully driven to trigger conditions with near 100% activation rate. Our method benefits from identifying AV vulnerability and enabling effective safety strategies.
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References · 10
- 2015Continuous control with deep reinforcement learningcited by 3,279
- 2017Hamilton-Jacobi Reachability: A Brief Overview and Recent Advancescited by 186
- 2016Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive controlcited by 134
- 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 67
- 2020Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systemscited by 35
- 2016Decomposition of Reachable Sets and Tubes for a Class of Nonlinear Systemscited by 16
- 2017Model-Based Control Using Koopman Operatorscited by 14
- 2020Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learningcited by 10
- 2018Approximating the nearest stable discrete-time systemcited by 6
- 2019Generalization error bounds for kernel matrix completion and extrapolationcited by 3
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