Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones

TL;DR

This paper introduces a Markov switching model validated with smartphone-collected data to predict driver behavior accurately over short periods, aiding in accident prevention and safety systems.

Contribution

It presents a novel driver behavior prediction framework using low-cost smartphone data and advanced Markov switching models, improving prediction accuracy for driving situations.

Findings

High prediction accuracy for driver behavior using MSVAR models

Effective use of smartphone data for real-time behavior analysis

Potential applications in accident prediction and safety systems

Abstract

Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed model is validated using a real dataset to predict the driver behavior in short duration periods. A literature survey on motion detection (specifically driving behavior detection using smartphones) is presented. Multiple Markov Switching Variable Auto-Regression (MSVAR) models are implemented to achieve a sophisticated fitting with the collected driver behavior data. This yields more accurate predictions not only for driver behavior but also for the entire driving situation. The performance of the presented models together with a suitable model selection criteria is also presented. The proposed driver behavior prediction framework can potentially be used in accident prediction and driver safety systems.