Prolonged driving causes symptoms of fatigue in drivers and changes their physical condition during driving. The purpose of this paper is to use a force measurement system located in the driver’s seat by force-sensitive resistance pressure sensors in order to record the received information to predict fatigue by learning regression-based models. This system is designed with 16 FSR (Force Sensing Resistor) sensors mounted on the seat and its backrest that records the driver’s body’s data, based on the force exerted by the driver on the seat in standard mode and during driving at various times. Fatigue level prediction is based on the trained nonlinear autoregressive exogenous model. In this procedure, models based on multivariate regression are first trained, and then correctness is checked. In this paper, the fatigue index is divided into five parts from 0 to 100 included fully conscious, slightly tired, moderately tired, very tired, and extremely tired, so the criterion for diagnosis is crossing the 75% of fatigue index and entering the extremely tired range. The results show that nonlinear models based on exogenous autoregressive have better performance than the linear mode, and even in the nonlinear model of NARX neural network, the fatigue of one step ahead is well predictable. A flopped state will be predictable when the body is immersed in the seat due to fatigue, so is far from the standard sitting position and will be in the extremely tired warning range.

长时间驾驶会导致驾驶员疲劳症状，并在驾驶过程中改变其身体状况。本文的目的是通过力敏感的阻力压力传感器使用位于驾驶员座椅上的力测量系统，以记录接收到的信息，从而通过学习基于回归的模型来预测疲劳。该系统的设计是在座椅及其靠背上安装16个FSR（力感应电阻）传感器，该传感器基于驾驶员在标准模式下以及在不同时间驾驶过程中在座椅上施加的力来记录驾驶员的身体数据。疲劳水平预测基于训练后的非线性自回归外生模型。在此过程中，首先对基于多元回归的模型进行训练，然后检查其正确性。在本文中，疲劳指数从0到100分为五个部分，包括完全清醒，轻微疲倦，中度疲倦，非常疲倦和极度疲倦，因此诊断的标准是越过75％的疲劳指数并进入极度疲倦的范围。结果表明，基于外源自回归的非线性模型比线性模型具有更好的性能，即使在NARX神经网络的非线性模型中，提前一步的疲劳也是可以预测的。当身体由于疲劳而沉浸在座椅上时，将可以预测其翻倒状态，因此其偏离标准的坐姿将处于极度疲劳的警告范围内。因此，诊断标准是超过疲劳指数的75％并进入极度疲劳的范围。结果表明，基于外源自回归的非线性模型比线性模型具有更好的性能，即使在NARX神经网络的非线性模型中，提前一步的疲劳也是可以预测的。当身体由于疲劳而沉浸在座椅上时，将可以预测其翻倒状态，因此其偏离标准的坐姿将处于极度疲劳的警告范围内。因此，诊断标准是超过疲劳指数的75％并进入极度疲劳的范围。结果表明，基于外源自回归的非线性模型比线性模型具有更好的性能，即使在NARX神经网络的非线性模型中，提前一步的疲劳也是可以预测的。当身体由于疲劳而沉浸在座椅上时，将可以预测其翻倒状态，因此其偏离标准的坐姿将处于极度疲劳的警告范围内。