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AUTOCAN-ID

Modern vehicles already contain a host of sensors that are required for its operation. This data is typically circulated within an automobile via the CAN bus and can in principle be shared with the outside world considering the privacy aspects of data sharing. The main problem is, however, the difficulty in interpreting this data. This is mainly because the configuration of this data varies between manufacturers and vehicle models and have not been standardized by the manufacturers. AUTOCAN-ID investigates if an intelligent tool or specific test procedures could be developed to extract CAN messages and their composition efficiently irrespective of vehicle brand and type. This would lay the foundations that are required to generate big data-sets from in-vehicle data efficiently for cooperative applications.

           

The maximum capacity of the road infrastructure is being reached due to the number of vehicles that are being introduced on Dutch roads each day. One of the plausible solutions to tackle congestion could be efficient and effective use of road infrastructure using modern technologies such as cooperative mobility. Cooperative mobility relies majorly on big data that is generated potentially by millions of vehicles that are travelling on the road. But how can this data be generated?

Though several organizations around the globe can read CAN bus signals and develop services such as fleet management systems that are based on this data, reverse engineering/interpreting the CAN signals has always been a time consuming and a labour-intensive process. Thus, this work only aims to develop an efficient method to interpret already extracted signals and not the extraction process itself. The central research question of AUTOCAN-ID is:

Can we develop an intelligent tool or specific test procedures to efficiently interpret CAN signals irrespective of the vehicle brand/type?

Given the vast amount of vehicle brands/types combinations available and an extensive list of signals that could be extracted from an automobile, a substantial amount of research would be required in the follow-up projects. This project aims at developing a proof-of-concept and will consider three-vehicle samples of which two would be used for testing and one would be used for validation. On the interests of the project partners, this work will focus on vehicle speed and brake status signals which could also be used in the fleet management systems of the project partners. The project partners also expressed the complexities involved in interpreting yaw rate and lateral acceleration signals, these variables will be mainly investigated in this work. The guidelines that are developed for the variables considered in this project could be extended for research on other variables during the follow-up projects.

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