Secure Automated Vehicle Platooning


Di Ma, PhD
Associate Professor, Security and Forensics Research Laboratory (SAFE Lab), Department of Computer and Information Science, U-M Dearborn


May 2016 – April 2018


David LeBlanc, PhD
Associate Research Scientist, Engineering Systems, U-M Transportation Research Institute

Jiafa Liu
Graduate Student Research Assistant, Department of Computer and Information Science, U-M Dearborn


To gain a deep understanding of the security risks/needs of automated platooning to understand the ability of sensor fusion to counter cyber-attacks, and to develop platooning control algorithms that take into account both safety and cybersecurity.  The proposed work will be visualized, demonstrated and evaluated through a large-scale vehicle network simulator.


It is widely believed that platooning requires DSRC to work properly.  We believe that the DSRC channel is most vulnerable to forging attacks, and hence cannot be leveraged for any safety-critical operations on its own.  It is also possible to spoof radar and LIDAR sensors, and blind cameras.  We will analyze security risks of automated platooning and evaluate the potential of sensor fusion in the context of cybersecurity by a structured risk assessment.  We will then analyze the potential of sensor fusion by assigning the input sensors confidence levels (e.g. camera is harder to spoof than DSRC), and establishing a model to utilize such confidence levels for a platooning control algorithm to account for safety and cybersecurity.  We will test the effectiveness of our algorithms in simulation.


We developed a secure and safe platooning framework which aims to keep the platoon safe even under cyber attacks. The framework consists of the following components:

  • safe platoon distance
  • multi-layer anomaly detection
  • incident response

In addition to this, we developed an educational lab on “Platoon Attack Simulation” to teach students how to use the PLEXE simulator to simulate cyber attacks in the platoon environment