The Holcombe Department of Electrical and Computer Engineering, Real-Time COntrol and Optimization Laboratory (RT-COOL), Clemson University, Clemson, SC, USA
The Holcombe Department of Electrical and Computer Engineering, Real-Time COntrol and Optimization Laboratory (RT-COOL), Clemson University, Clemson, SC, USA
The Holcombe Department of Electrical and Computer Engineering, Real-Time COntrol and Optimization Laboratory (RT-COOL), Clemson University, Clemson, SC, USA
Autonomous electric vehicles provide benefits to both drivers and the environment compared to conventional vehicles; however, they are burdened with an increase in potential pathways for cyber-attacks. Therefore, reliable cyber-security strategies for these vehicles must be pursued. This paper addresses this concern by implementing a threat detection strategy that utilizes an observer and a neural network. These tools monitor discrepancies between the vehicle’s lateral metrics, which are produced via sensor data, neural network output, and an observer. Previous literature focuses on physics-based analytics to create the threat decision, but here, a data based approach is utilized. The vehicle used in this study is a four-motor-drive autonomous electric vehicle that is propelled with brushless DC motors. The motors are controlled by direct torque control. In this study, three forms of cyber-attacks are implemented. These include data integrity attacks, replay attacks, and denial-of-service attacks. A performance metric is also created, which indicates the data-driven approach outperforms the physics-based approaches. All modeling and simulation were conducted in the MATLAB/Simulink environment.
