Large Scale Efficient Data Readout for Vehicle Fleets

Abstract

As vehicles become more technologically advanced, the data generated by a single vehicle reach significant amounts. Diverse data has high potential in use cases such as machine learning by providing insights into different conditions. Currently, there is no clear solution for collecting this data as vehicle systems are restricted in terms of compute, memory, storage, and bandwidth. This thesis investigates the problem of large scale vehicle data readout and presents a solution to it, providing a significant increase by leveraging lossless streaming based compression at low cost. Furthermore, it addresses the architecture necessary in order to sufficiently process the data globally and how best to integrate this efficiently with a massive number of vehicle systems. Lastly, a generalized model is formulated at the micro scale, which establishes the requirements in terms of compute and memory on a single vehicle system based on the findings presented. At the macro scale, the infrastructure required to support the solution is discussed.