Our roads the preparation of mobility studies has become an essential element when planning and regulating traffic. These studies have been carried out with all kinds of systems, such as cameras, on-track counting systems and, lately, with embedded devices. However, until now there have been no studies able to take into account how traffic situations affect drivers and vice versa. Nor has it been analysed if what happens inside the vehicles affects the circulation and in what way. For this, it’s necessary to have a low-cost system, installable in a significant number of vehicles that can not only detect the position of the vehicle and how is its displacement, but to control what happens in the set composed of the driver, the cabin and the vehicle. This article presents a system capable of collection biometric information of the driver, from sensors installed in the cabin and from the vehicle itself, pre-processing the information on the device itself, following an edge-computing philosophy, and sending it to a datalake in the cloud to be processed in the studies that you wish to perform. The system has been successfully tested in different types of routes and situations. Its main advantage over previous systems is the ability to make an exhaustive analysis of risk situations and its dependence on the driver’s status with a low economic cost of production.

中文翻译：

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用于实现基于驾驶员、驾驶室和车辆监控的高级移动性研究的系统


我们的道路流动性研究的准备已成为规划和管理交通时的一个基本要素。这些研究已经在各种系统上进行，例如摄像机、轨道计数系统以及最近的嵌入式设备。然而，到目前为止，还没有研究能够考虑交通状况如何影响驾驶员，反之亦然。也没有分析车内发生的情况是否以及以何种方式影响循环。为此，需要有一个低成本的系统，可安装在大量车辆中，不仅可以检测车辆的位置及其位移，还可以控制由驾驶员、驾驶员和驾驶员组成的集合中发生的情况。驾驶室和车辆。本文提出了一种系统，能够从安装在车内的传感器和车辆本身收集驾驶员的生物识别信息，遵循边缘计算原理对设备本身的信息进行预处理，并将其发送到数据湖您希望执行的研究中要处理的云。该系统已在不同类型的路线和情况下成功进行了测试。与以前的系统相比，它的主要优点是能够以较低的生产经济成本对风险情况及其对驾驶员状态的依赖性进行详尽的分析。