DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development

Abstract

DETROIT is an open-source vehicle-agnostic end-to-end framework for vehicular data collection, translation and sharing that facilitates the rapid development of automotive apps. With vehicles becoming increasingly connected, unlocking sheer amounts of data from the in-vehicle network (IVN) can accelerate the development of many useful apps. Unlike existing commercial and academic solutions that can only access a restricted set of standardized emission-related sensor data and lack feasible data accessibility by third-party developers, DETROIT offers a convenient interface to develop apps which can access a broad range of powertrain-related sensors and car-body events thanks to crowd-sourcing vehicular translation tables by fully automated CAN bus reverse-engineering. DETROIT is developed with the objectives of simplicity, scalability, privacy and liability. To the best of our knowledge, this is the first end-to-end framework consisting of a frontend, backend and a developer portal to cover vehicular data collection, translation and sharing with app developers. Besides an extensive framework benchmark to show the light resource overhead and feasibility of DETROIT, we also have evaluated it by reimplementing two existing mobility apps from academia. Developers have reported that DETROIT offers high sensor fidelity, enhanced application flexibility, as well as low implementation complexity.

Publication
In 2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking
Mert D. Pesé
Mert D. Pesé
Assistant Professor

My research interests include all sorts of automotive-related security and privacy, including on in-vehicle networks, connected car protocols, Android Automotive and adversarial machine learning against autonomous vehicles.