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

Abstract

Drivers' behavior in traffic is a determining factor for the rate of accidents on roads and highways. This paper presents the design of 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 to 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 driver's behavior while driving, along with data from the environment. The results of the inference serve to alert 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.

Map Matching Algorithm Based on Dynamic Programming Approach

“…Map matching algorithms are applied to identify the correct path as a sequence of road segments by a series of location measurements (GPS records) and road network data. Processed trajectories are an important data source for intelligent transportation systems that can be in such applications as traffic estimation and prediction [1], [2], traffic modelling [3], developing navigation services, user preferences elicitation and training of transportation recommendation systems [4], [5], and so on.…”