The use of capacitive sensors in the automotive context opens new possibilities in the development of new interfaces for machine interaction with the vehicle occupants. Large smart surfaces with gesture recognition will possibly be part of such new interfaces. However, the data processing cost of such new sensors should be maintained at a minimum while increasing the complexity of their gesture recognition accuracy by using modern deep-learning approaches. In this paper, we introduce the use of Bayesian optimization with execution platform constraints to implement accurate gesture recognition sensors based on 1D capacitive sensor arrays. Various RNN-based designs are implemented and optimized for their execution on embedded automotive microcontrollers. We show that LSTM and GRU-based designs are especially adequate achieving an average recall of the gesture classes over 95% in less than 100 optimization steps.

在汽车环境中使用电容式传感器为开发新的界面以与乘员进行机器交互开辟了新的可能性。具有手势识别功能的大型智能表面可能会成为此类新界面的一部分。然而，这种新传感器的数据处理成本应保持在最低水平，同时通过使用现代深度学习方法来增加其手势识别精度的复杂性。在本文中，我们介绍了在执行平台约束下使用贝叶斯优化的方法，以基于一维电容传感器阵列实现精确的手势识别传感器。各种基于RNN的设计均已实现和优化，可在嵌入式汽车微控制器上执行。