Inference of driver behavior using correlated IoT data from the vehicle telemetry and the driver mobile phone

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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1. This research work is supported by the Brazilian research and innovation Agencies CAPES (Grants 23038.007604/2014-69 FORTE and 88887.115692/2016-00), CNPq (Grant 465741/2014-2 INCT-CyberSecurity), and FAPDF (Grants 0193.001366/2016 UIoT and 0193.001365/2016 SSDDC), as well as the LATITUDE/UnB Laboratory (Grant 23106.099441/2016-43 SDN), the Ministry of the Economy (Grants 005/2016DIPLA, 011/2016 SEST and 083/2016 ENAP), the Institutional Security Office of the Presidency of the Republic of Brazil (Grant 002/2017), and the IEEE VTS Centro-Norte Brasil Chapter.

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2. Track 3: Network Systems and Applications

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3. Technical Session: 3rd Workshop on Internet of Things - Enablers, Challenges and Applications

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4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).