Inference of driver behavior using correlated IoT data from the vehicle telemetry and the driver mobile phone
Daniel Alves da Silva, José Alberto Sousa Torres, Alexandre Pinheiro, Francisco L. de Caldas Filho, Fabio L. L. Mendonça, Bruno J. G Praciano, Guilherme Oliveira Kfouri, Rafael T. de Sousa Jr
Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 487–491 (2019)
Abstract. The drivers' behavior in traffic is a determining factor for the rate of accidents on roads and highways. This paper presents the design an intelligent IoT system capable of inferring and warning about road traffic risks and danger zones, based on data obtained from the vehicles and their drivers mobile phones, thus helping to avoid accidents and seeking preserve the lives of the passengers. The proposed approach is to collect vehicle telemetry data and mobile phone sensors data through an IoT network and then to analyze the drivers' behavior while driving, along with data from the environment. The results of the inference serve to alert the drivers about incidents in their trajectory as well as to provide feedback on how they are driving. The proposal is validated using a developed prototype to test its data collection and inference features in a small scale experiment.
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