Driving Signature Analysis for Auto-Theft Recovery

Abstract

Autotheft is a crime that can be mitigated using artificial intelligence as a scientific approach. In this case, we assess the drivers driving pattern using both deep neural network and swarm intelligence algorithms. From the analysis we are able to obtain the driving signature of the driver which can be associated with the vehicle. The vehicle is then tracked and monitored. Next, a deviation from the usual driving signature of the owner or assigned driver would signify a possible instance of autotheft. Subsequently, the vehicle can be traced and reclaimed by the owner. The algorithms are evaluated based on their performance in analysing the datasets bearing variable features. The variations in features enable us to verify the efficacy and accuracy levels of the various algorithms that are used in the study. The metrics used for evaluation are the Mean Squared Error and the F1 Score for precision, accuracy and recall functionality.