Driver and Passenger Identification From Smartphone Data
Bashar I. Ahmad, Patrick Langdon, Jiaming Liang, Simon Godsill, Mauricio Delgado, Thomas Popham · IEEE Transactions on Intelligent Transportation Systems · 2018
The objective of this paper is twofold. First, it presents a brief overview of existing driver and passenger identification or recognition approaches, which rely on smartphone data. This includes listing the typically available sensory measurements and highlighting a few key practical considerations for automotive settings. Second, a simple identification method that utilizes the smartphone inertial measurements and, possibly, doors signal is proposed. It is based on analyzing the user behavior during entry, namely, the direction of turning, and extracting relevant salient features, which are distinctive depending on the side of entry to the vehicle. This is followed by applying a suitable classifier and decision criterion. Experimental data is shown to demonstrate the usefulness and effectiveness of the introduced probabilistic, low-complexity, identification technique.