Hands-on detection for steering wheels with neural networks
This addresses safety in automotive systems for drivers, but it is incremental as it applies existing methods to a specific hardware setup.
The paper tackled the problem of detecting whether a driver's hands are on a steering wheel by implementing a capacitive sensor and machine learning models, achieving a system evaluated for reliability and response time on a microcontroller.
In this paper the concept of a machine learning based hands-on detection algorithm is proposed. The hand detection is implemented on the hardware side using a capacitive method. A sensor mat in the steering wheel detects a change in capacity as soon as the driver's hands come closer. The evaluation and final decision about hands-on or hands-off situations is done using machine learning. In order to find a suitable machine learning model, different models are implemented and evaluated. Based on accuracy, memory consumption and computational effort the most promising one is selected and ported on a micro controller. The entire system is then evaluated in terms of reliability and response time.