Download PDFOpen PDF in browser

Threats and Alert Analytics in Autonomous Vehicles

12 pagesPublished: March 9, 2020


Autonomous vehicles or self-driving cars emerged with a promise to deliver a driving experience that is safe, secure, law-abiding, alleviates traffic congestion and reduces traffic accidents. These self-driving cars predominantly rely on wireless technology, vehicular ad-hoc networks (VANETs) and Vehicle to Vehicle (V2V) networks, Road Side Units (RSUs), Millimeter Wave radars, light detection and ranging (LiDAR), sensors and cameras, etc. Since these vehicles are so dexterous and equipped with such advanced driver assistance technological features, their dexterity invites threats, vulnerabilities and hacking attacks. This paper aims to understand and study the technology behind these self-driving cars and explore, identify and address popular threats, vulnerabilities and hacking attacks to which these cars are prone. This paper also establishes a relationship between these threats, trust and reliability. An analysis of the alert systems in self-driving cars is also presented.

Keyphrases: Advanced Driver Assistance Systems, ethics, Reliability, Security, self-driving cars, Threats, Trust, Vulnerabilities

In: Gordon Lee and Ying Jin (editors). Proceedings of 35th International Conference on Computers and Their Applications, vol 69, pages 48--59

BibTeX entry
  author    = {Aakanksha Rastogi and Kendall Nygard},
  title     = {Threats and Alert Analytics in Autonomous Vehicles},
  booktitle = {Proceedings of 35th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {69},
  pages     = {48--59},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/j6h1}}
Download PDFOpen PDF in browser