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.

Modern Connected and Autonomous Vehicles (CAVs) are equipped with an increasing number of Electronic Control Units (ECUs), many of which produce large amounts of data. Data is exchanged between ECUs via an in-vehicle network, with the Controller Area Network (CAN) bus being the de facto standard in contemporary vehicles. Furthermore, CAVs have not only physical interfaces but also increased data connectivity to the Internet via their Telematic Control Units (TCUs), enabling remote access via mobile devices. It is also possible to tap into, and read/write data from/to the CAN bus, as data transmitted on the CAN bus is not encrypted. This naturally generates concerns about automotive cybersecurity. One commonality among most vehicular security attacks reported to date is that they ultimately require write access to the CAN bus. In order to cause targeted and intentional changes in vehicle behavior, malicious CAN injection attacks require knowledge of the CAN message format. However, since this format is proprietary to OEMs and can differ even among different models of a single make of vehicle, one must manually reverse-engineer the CAN message format of each vehicle they target --- a time-consuming and tedious process that does not scale. To mitigate this difficulty, we develop LibreCAN, which can translate most CAN messages with minimal effort. Our extensive evaluation on multiple vehicles demonstrates LibreCAN's efficiency in terms of accuracy, coverage, required manual effort and scalability to any vehicle.