Evaluating the Effects of Cyberattacks in Mixed and Fully Connected Vehicle Environments Using a Novel Microscopic Traffic Model

Abstract

Cybersecurity has increased in importance due to advances in connected vehicle technology. To evaluate the impact of cyberattacks in mixed and fully connected vehicle environments, a novel microscopic traffic model is given that incorporates the connected autonomous vehicle (CAV) penetration rate. The intelligent driver (ID) model assumes uniform driver behavior based on a constant which is unsuitable for this environment. Thus, a variable exponent based on the cyberattack intensity is proposed that integrates the CAV penetration rate. The proposed model is evaluated over a 1000 m circular road for 500 s with a platoon of 28 vehicles with 60% of vehicles affected by an attack. The results obtained indicate that cyberattacks reduce traffic stability, particularly at low CAV penetration rates. At high penetration rates, these attacks have less of an impact due to faster reaction times and coordination of unaffected CAVs. Furthermore, the results demonstrate that the proposed model can effectively characterize traffic behavior under cyberattacks, and so can be used to alleviate congestion in the presence of cybersecurity threats.