Addressing Rogue Vehicles by Integrating Computer Vision, Activity Monitoring, and Contextual Information

Brook Abegaz, David Chan-Tin, Neil Klingensmith, George K. Thiruvathukal, Eric Chan-Tin

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we address the detection of rogue autonomous vehicles using an integrated approach involving computer vision, activity monitoring and contextual information. The proposed approach can be used to detect rogue autonomous vehicles using sensors installed on observer vehicles that are used to monitor and identify the behavior of other autonomous vehicles operating on the road. The safe braking distance and the safe following time are computed to identify if an autonomous vehicle is behaving properly. Our preliminary results show that there is a wide variation in both the safe following time and the safe braking distance recorded using three autonomous vehicles in a test-bed. These initial results show significant progress for the future efforts to coordinate the operation of autonomous, semi-autonomous and non-autonomous vehicles.

Original languageAmerican English
JournalComputer Science: Faculty Publications and Other Works
DOIs
StatePublished - Sep 1 2020

Keywords

  • Monitoring
  • Autonomous Vehicles
  • Rogue Vehicle
  • Sensors

Disciplines

  • Computer Sciences
  • Graphics and Human Computer Interfaces

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