Guest Editorial Introduction to the Special Issue on Deep Learning Models for Safe and Secure Intelligent Transportation Systems

Alireza Jolfaei, Neeraj Kumar, Min Chen, Krishna Kant

Research output: Contribution to journalEditorial

Abstract

The autonomous vehicular technology is approaching a level of maturity that gives confidence to end-users in many cities around the world for their usage so as to share the roads with manual vehicles. Autonomous and manual vehicles have different capabilities which may result in surprising safety, security, and resilience impacts when mixed together as a part of the intelligent transportation system (ITS). For example, autonomous vehicles can communicate electronically with one another, make fast decisions and associated actuation, and generally act deterministically. In contrast, manual vehicles cannot communicate electronically, are limited by the capabilities and slow reaction of human drivers, and may show some uncertainty and even irrationality in behavior due to the involvement of humans. At the same time, humans can react properly to more complex situations than autonomous vehicles. Unlike manual vehicles, the security of computing and communications of autonomous vehicles can be compromised thereby precluding them from achieving individual or group goals.
Original languageEnglish
Pages (from-to)4224-4229
Number of pages6
JournalIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number7
DOIs
Publication statusPublished - Jul 2021
Externally publishedYes

Keywords

  • Deep learning
  • Safety
  • Security
  • Autonomous vehicles
  • Intrusion detection
  • Generative adversarial networks
  • Vehicular ad hoc networks

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