Monitoring of Occupant States in Autonomous Vehicles Using Capacitance-Sensing Imaging

Abstract

The increasing popularity of autonomous vehicles has heightened the need to deploy robust systems that guarantee the safety and comfort of vehicle occupants. Such systems are required to monitor the presence and posture of occupants in real-time. This paper proposes a novel system that combines capacitive sensing with machine learning to achieve real-time occupant detection and posture recognition. The main constituent of the system is a capacitance-sensing mat that spans both the seat base (SB) and the backrest (BR) of a typical vehicle seat. The mat is connected to a sensing circuitry that continuously monitors variations in capacitance inside the mat to generate grayscale capacitance-sensing images (CSIs). The CSIs are real-time pictorial representations of the variations in capacitance caused within the mat due to changes in the position and the posture of occupants. The real-time CSIs then serve as inputs to a k-nearest-neighbors-based (k-NN) classifier that is capable of identifying different posture classes. The classifier is pre-trained using a previously acquired database consisting of a collection of CSIs belonging to known posture classes. Subsequently, the performance of the classifier is validated, and the system is deployed for real-time occupant detection and posture recognition.