标题： 基于神经模糊系统和加权主成分投影的驾驶员行为软测量方法 显示英文标题

标题： Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components

摘要： 本研究的主要目标是开发一种软传感器，用于实时分类驾驶员在操控车辆时的行为。 仅利用车辆已有的传感器测量数据（而无需添加额外的硬件，如智能手机、摄像头等）来高效分类驾驶行为，这是一个挑战。 仅使用现代车辆的中心信号数据的主要优势在于经济性。 在不需要向车辆添加额外硬件（及其软件）的情况下对驾驶行为进行分类并向驾驶员发出危险行为警告，这将允许这些分类器直接集成到当前车辆中，且不会增加车辆制造成本，因此具有附加价值。 本研究中，分类仅基于速度、加速度和惯性测量值，这些数据在许多现代车辆中已经存在。 所提出的算法基于由多个神经模糊系统组成的结构，并结合了主成分分析（PCA）投影数据的各个分量。 与多种经典分类算法进行了比较。 显示英文摘要

摘要： This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers' behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smart phones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in components of various Principal Component Analysis. A comparison with several types of classical classifying algorithms has been made.