Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones
This work addresses driver safety systems for transportation, though it appears incremental as it applies existing MSVAR methods to new smartphone data.
The authors tackled driver behavior prediction by implementing Multiple Markov Switching Variable Auto-Regression models validated with smartphone-collected data, achieving more accurate predictions for behavior and driving situations.
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.