PERFECT: Positional-Forgery Resistant Traffic Gap Estimation for Connected Intersection Management

Connected and Autonomous Vehicles (CAVs) are set to be the next global revolution in transportation. One of the major challenges in a CAV environment is handling the positional-forgery of Basic Safety Messages while calculating critical traffic gaps at a Stop Sign Gap Assist controlled intersection. Traditionally, positional-forgery has been handled by isolated instances of physical layer detection and correction mechanisms, which proved unreliable during the multi-hop and majority malicious vehicle scenarios. In this work, we propose a novel framework called Positional-forgEry Resistant traFfic gap Estimation for Connected inTersection management (PERFECT) that performs optimal traffic gap estimation even during multi-hop scenarios and majority malicious positional-forgery attacks. The framework reduces traffic gap estimation error during majority malicious scenarios by over 60%, keeps the average error within 8.17%–10.62% and minimizes the maximum error across a range of malicious situations.