School of Engineering and Science, Tecnologico de Monterrey, Av. E Garza Sada 2501, Monterrey 64849, Mexico.
School of Engineering and Technologies, Universidad de Monterrey, Av. I Morones Prieto 4500 Pte., San Pedro Garza Garcia 66238, Mexico.
Sensors (Basel). 2023 Jan 10;23(2):814. doi: 10.3390/s23020814.
This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver's health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle's central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver's heart condition and vehicular traffic have been found in this analysis.
本文提出了一种集成的驾驶员和车辆监测系统，仅通过单一案例研究即可实现易于配置和复制。车载传感器和远程传感器相结合，为估算污染排放、燃料消耗、驾驶风格和驾驶员健康状况的算法建模。本文的主要贡献在于分析上述监测特征之间的相互作用，突出了驾驶员对车辆性能的影响，反之亦然。该分析使用具有不同驾驶员和路线的一辆车进行了实验，并在移动应用程序上实现。与商业驾驶员和车辆监测系统相比，这种方法不是定制的，使用经典的传感器测量，并且基于已经证明但不在与其他算法交互的环境中的简单算法。在这个全局车辆和驾驶员监测系统的设计过程中，进行了主成分分析，以减少用于训练/测试算法的变量，目的是减少通过蓝牙在使用的设备（生物识别腕带、智能手机和车辆中央计算机）之间传输的数据。实验结果表明，所提出的车辆和驾驶员监测系统可以正确预测 84%的燃料消耗指数、89%的污染排放和 89%的驾驶风格。实际上，在这项分析中发现了驾驶员心脏状况和车辆交通之间有趣的相关性结果。
