12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
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.
Brook Abegaz, Eric Chan-Tin, Neil Klingensmith, and George K. Thiruvathukal. 2020. Addressing Rogue Vehicles by Integrating Computer Vision, Activity Monitoring, and Contextual Information. In 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '20). Association for Computing Machinery, New York, NY, USA, 62–64. DOI:https://doi.org/10.1145/3409251.3411724
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