Autonomous vehicles promise a safer future with a cleaner, more cost-efficient, and more reliable transportation system. However, the current approach to autonomy for vehicles has focused on building small, expensive, disparate intelligences that are closed off to the rest of the world. Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) connectivity can bridge some of that gap through enabling communication between a vehicle and its surrounding environment. This has to be paired with a low-cost approach to perception to enable broader research into and adoption of connected and autonomous vehicles (CAVs). To that end, this paper presents the development and calibration process of a low-cost, easy-to-deploy machine vision pipeline for multiple cameras that does not sacrifice image quality or frame rate. The pipeline is integrated with the CARMA Platform, developed by the FHWA for connected vehicle research.

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