Ciencias Exactas y Ciencias de la Salud

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Human-vehicle interaction analysis
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06-05) Campos Ferreira, Andrés Eduardo; MORALES MENENDEZ, RUBEN; 30452; Morales Menéndez, Rubén; ilquio, emipsanchez; Vargas Martínez, Adriana; Ramírez Mendoza, Ricardo Ambrocio; School of Engineering and Sciences; Campus Monterrey; Lozoya Santos, Jorge de Jesús
The present work is the Thesis work of Vehicle assessment comparison from a smartphone reference with different approaches, to pursue the Master on Science on Manufacturing Systems. The automotive industry is continuously evolving by implementing top-edge technologies to improve comfort, safety, and driving experience to the users. In the context of Industry 4.0 and the Smart Cities paradigm, the concept of Intelligent Transportation System has become a research topic in the last few years. In the race for autonomous driving, researchers and industry have stressed the importance of monitoring drivers and passengers to determine the driving style, safety, and fuel efficiency, among other essential features. Despite all the work that has been done to monitor drivers, some approaches consist of vehicle-fixed devices or personalized devices that do not allow for the reproduction of experimentation to other vehicles. This instrumentation limits enormously the possibility to monitor any type of vehicle and collect information to develop intelligent algorithms that can predict driver and vehicle features such as driving behavior, energy consumption, fatigue, or vehicle’s element prognosis. Current researches focus on analyzing the interaction as a system from the vehicle’s point of view or driver’s point of view. Nevertheless, they have not been observed on both sides. To overcome these issues, an experimental setup is proposed on this work. The importance of this project is the easiness and replicability of the experimental setup; it is then validated by analyzing the logged data and the correlations between variables. Besides, state-of-the-art algorithms are compared to validate and select the best performance. This thesis integrates an experimental setup easy to use and implement with available commercial devices. Then, to validate the setup, a selection of algorithms based on a literature review were replicated and fed with the data logged from the experimental setup. A set of analyses of the resulting dataset is done to observe the interaction of vehicle and driver signals’ performance on how these signals are correlated. The first part of this work is devoted to the experimental setup definition and testing. Here the process was iteratively done by generating a procedure. Then, the next step consisted of exploring the logged data with a statistical tool to determine a possible correlation between signals and to reduce the dataset order but preserving most of the information. Later, state-of-the-art algorithms and data-driven identification models were identified and validated for specific key performances of vehicles and drivers. The vehicle’s key performances boarded on this thesis are the driving style, energy consumption, and emissions. Besides, the driver’s key performances are the heart condition, temperature, electrodermal activity, and heart rate. These features are highly studied when evaluating vehicle’s or driver’s state. The result of evaluating these performances with the selected algorithms shown that the driving style had 77% of correct classification, energy consumption, and emissions had around 16% and 11% of relative error, respectively. The results of this project show how vehicle and driver interact by analyzing its key performances. The Principal Component Analysis technique helped to find correlation among the raw data and also reduced the features from 57 to 27 without significant losses on the information. Besides, it demonstrated the correlation between vehicle’s and driver’s key performances by analyzing PCA plots and the covariance matrix.
Definition of bio-physics framework for advanced driver assistance systems’ design and development
(2017-12-05) Ledezma-Zavala, Edgar; Ramírez Mendoza, Ricardo; Bustamante-Bello, Rogelio; Soto Rodríguez, Rogelio
Most recent efforts made in the industry to the path for a full autonomous vehicle have been focusing on the automation of the vehicles’ maneuvers, and the understanding of the surroundings. While a great advance has been achieved, the most advanced implementations of such systems may be only at the scale defined by the US National Highway Traffic Safety Administration as a level 2: “automation of at least two primary control functions”. Such systems require the driver to keep their hands on the steering wheel at all the time. One popular example for this is Tesla Motors´ “Autopilot” feature, which is in fact just a diver-assistant feature rather than a fully autonomous driving system. Given an NHTSA level 2 can still drive on itself by hundreds of miles in the highway, it is easy for driver to misinterpret the real capabilities of current systems and get comfortable on letting the machine take its decisions alone, wandering around visually or mentally, believing they are using a “limited self-diving” NHTSA level 3 of automation, or even a fully autonomous level 4. Current systems have evolved to process a great amount of information coming from the environment, but they may be leaving out the most important character involved in the vehicle: the driver. This project focuses on that forgotten element in the vehicle framework and intends to stablish a robust yet flexible representation for such a concept system as a driving environment, considering the driver itself, the internal mechanics of the vehicle, and external elements such as the driveway, other vehicles or pedestrians and traffic signals both passive and possible active signals with intelligent capabilities of Intelligent Transportations Systems (ITS)

Ciencias Exactas y Ciencias de la Salud

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