Synchronous and Open, Real World, Vehicle, ADAS, and Infrastructure Data Streams for Automotive Machine Learning Algorithms Research 2020-01-0736
The suite of CAVs-derived data streams have been demonstrated to enable improvements in system-level safety, emissions and fuel economy. This describes the gathering, processing, and use of on-road data collected from probe vehicles in Fort Collins, Colorado. Several synchronous datasets were gathered using a test vehicle equipped with sensors to measure ego vehicle position and velocity, ADAS-derived near-neighbor relative position and velocity, and infrastructure-level transit time and signal phase and timing. The datasets in both raw and data-fused formats is made available to the research community. The utility of these types of open data projects is briefly demonstrated by using them in the applications of vehicle velocity prediction, and real-time fuel economy modeling.
Aaron I. Rabinowitz, Tushar Gaikwad, Samantha White, Thomas Bradley, Zachary Asher
Colorado State University, Western Michigan University