Hands-on detection for steering wheels with neural networks
Michael Hollmer Andreas Fischer
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.