Driver Identification

The growing penetration of telematics systems and connectivity in vehicles has enabled a large variety of possible value-added services for drivers and service providers. In this project we focus on utilizing this abundance of data to model how drivers behave in various driving contexts. We use the state of the art Machine Learning algorithms to build models that can serve different applications with the focus on only using vehicle sensors while avoiding the usage of intrusive sensors such as in-car cameras. For example by modelling distinct driving patterns of each individual we can identify the driver, which has applications in detection of stolen vehicles. In another use-case we work on distraction detection as an safety application. This time the model captures changes between attentive and distracted driving therefore we can detect such events and possibly issue a warning to the driver. Lastly in a more general sense more comprehensive driver models can augment current simulation tools which play a crucial role in development of advanced driver assistance systems (ADAS) and autonomous vehicles.

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Sasan Jafarnejad
Doctoral researcher | data scientist

My research interests include time series classification and privacy preserving machine learning.

Publications

A major issue in driver identification is lack of enough data from the target group of drivers. In this work I use Triplet loss to …

This is one of my latest works in which I apply deep learning to problem of driver identification using GPS data collected from …

The increasing penetration of connected vehicles nowadays has enabled driving data collection at a very large scale. Many telematics …

Nowadays Internet-enabled phones have become ubiquitous, and we all witness the flood of information that often arrives with a …

The growing penetration of telematics systems and connectivity in vehicles has enabled a large variety of possible value-added services …