A Platform for Multi-Modal Driver Behaviour Data Collection

Simone Bianco, Luigi Celona, Giovanni Donato Gallo, Paolo Napoletano

Abstract: This article describes an open-source platform for collecting multi-modal driver behavior data. The presented platform encompasses all essential components for efficient data collection, including hardware, software, and a rigorous simulation protocol. The hardware aspect involves the creation of a simulated driver environment including a realistic driving cockpit and position with a real seat, and non-invasive sensors to ensure non-intrusive data collection. The software component of our platform leverages the power of the Internet of Things (IoT) to enable seamless communication between various sensors in an atomic and scalable manner. Through the implementation of a thoughtfully designed simulation protocol, driver data is systematically gathered in controlled and replicable scenarios featuring varying stress levels. The presented work therefore offers a comprehensive and effective solution for gathering essential data to advance the development of Drive Status Monitoring (DSM) systems.