Abstract
This work aims to assist the diagnostics of car defects by relating the data obtained through the vehicle telemetry with the driver’s perceptions on a problem when it is perceived. Including the driver in the diagnostic process allows the engineers to identify features to be improved in the car design, even though a possible error/mistake is not apparent. Thus, the driver is seen as a new “sensor” capable of reporting his perceptions. For that, we propose an approach that includes data mining on the automaker knowledge base, the car’s telemetry data obtained through an OBD device, the drivers’ perception captured by a mobile device such as a Smartphone or a Tablet. The proposed Interactive Diagnostic approach enables a more complete preventive diagnostics in comparison with the traditional diagnostic based only on the telemetry data. In addition, the automaker receives the gathered data allowing their engineers to analyze the error/defect and fix the problem or improve the car design. The proposed approach was evaluated through some case studies. Diagnostic engineers answered a questionnaire that shows how the proposed approach influences the diagnostic process, i.e. the solution of the problem was found in fewer steps compared to the current diagnostics process. Therefore, this work advances both the state-of-the-art and the state-of-the-practice in automotive diagnostics by (i) exploring the vehicles’ connectivity in the diagnostics process in an efficient way, and (ii) allowing the automobile industries to obtain more concrete information on the products they offer.
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Notes
- 1.
I.e. the automaker does not have access to the source code, and hence, it is difficult to adapt the software to specific needs.
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de Oliveira, L.P., Wehrmeister, M.A., de Oliveira, A.S. (2023). Using Data Mining and Mobile Devices to Assist the Interactive Automotive Diagnostics. In: Wehrmeister, M.A., Kreutz, M., Götz, M., Henkler, S., Pimentel, A.D., Rettberg, A. (eds) Analysis, Estimations, and Applications of Embedded Systems. IESS 2019. IFIP Advances in Information and Communication Technology, vol 576. Springer, Cham. https://doi.org/10.1007/978-3-031-26500-6_9
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