Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

Abstract

Driver identification refers to the process whose primary purpose is identifying the person behind the steering wheel using collected information about the driver him/herself. The constant monitoring of drivers through sensors generates great benefits in advanced driver assistance systems (ADAS), to learn more about the behavior of road users. Currently, there are many research works that address the subject in search of creating intelligent models that help to identify vehicle users in an efficient and objective way. However, the different methodologies proposed to create these models are based on data generated from sensors that include different vehicle brands on routes established in real environments, which, although they provide very important information for different purposes, in the case of driver identification, there may be a certain degree of bias due to the different situations in which the route environment may change. The proposed method seeks to intelligently and objectively select the most outstanding statistical features from motor activity generated in the main elements of the vehicle with genetic algorithms for driver identification, this process being newer than those established by the state-of-the-art. The results obtained from the proposal were an accuracy of 90.74% to identify two drivers and 62% for four, using a Random Forest Classifier (RFC). With this, it can be concluded that a comprehensive selection of features can greatly optimize the identification of drivers.

Towards Efficient Risky Driving Detection: A Benchmark and a Semi-Supervised Model

“…The performance of a vehicle is directly influenced by the driver’s behavior, and this impact can be assessed through the analysis of single-modal data from vehicle motion sensors. Espino-Salinas et al [ 34 ] addressed the identification of drivers through motor activity generated by the main elements of the vehicle through genetic algorithms. With the advancement of sustainable multi-sensor collection techniques, numerous studies have been undertaken to integrate and fuse data from multiple sensors.…”