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.

翻译：本文提出了一种基于机器学习的手部检测算法概念。该检测在硬件层面采用电容式方法实现：当驾驶员的手靠近时，方向盘中的传感器垫会检测到电容变化。关于手握或离手状态的评估与最终判断则通过机器学习完成。为寻找合适的机器学习模型，我们实现并评估了多种不同模型。基于准确率、内存消耗和计算开销等指标，选定了最具前景的模型，并将其移植至微控制器上。最后，从可靠性和响应时间两方面对整体系统进行了评估。