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

【Author】 Anand, Shajina; Raja, Gunasekaran; Kumar, Neeraj; Narayanan, Renuka; Raja, Ashmitha; Karthik, K. Bhavani Venkata

【Source】IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

【影响因子】6.239

【Abstract】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.

【Keywords】Blockchains; Distance measurement; Reliability; Laser radar; Estimation error; Computational modeling; Clustering algorithms; Connected and autonomous vehicles; forgery; group recommendation; security; stop sign gap assist; trust model; vehicle-to-everything

【发表时间】2022 AUG

【收录时间】2022-09-15

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-交通领域