An eco-driving approach for ride comfort improvement

[EN] New challenges on transport systems are emerging due to the advances that the current paradigm is experiencing. The breakthrough of the autonomous car brings concerns about ride comfort, while the pollution concerns have arisen in recent years. In the model of automated automobiles, drivers are expected to become passengers, so, they will be more prone to suffer from ride discomfort or motion sickness. Conversely, the eco-driving implications should not be set aside because of the influence of pollution on climate and people's health. For that reason, a joint assessment of the aforementioned points would have a positive impact. Thus, this work presents a self-organised map-based solution to assess ride comfort features of individuals considering their driving style from the viewpoint of eco-driving. For this purpose, a previously acquired dataset from an instrumented car was used to classify drivers regarding the causes of their lack of ride comfort and eco-friendliness. Once drivers are classified regarding their driving style, natural-language-based recommendations are proposed to increase the engagement with the system. Hence, potential improvements of up to the 57.7% for ride comfort evaluation parameters, as well as up to the 47.1% in greenhouse-gasses emissions are expected to be reached.University of the Basque Country UPV/EHU, Grant/Award Number: GIU18/122; European Commission, Grant/Award Number: TEC201677618-R; Spanish AEI, Grant/Award Number: TEC2016-77618-R; Basque Government, Grant/Award Number: KK-2019-00035-AUTOLIB (ELKARTEK

Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques

Ride comfort improvement in driving scenarios is gaining traction as a research topic. This work presents a direct methodology that utilizes measured car signals and combines data processing techniques and machine learning algorithms in order to identify driver actions that negatively affect passenger motion sickness. The obtained clustering models identify distinct driving patterns and associate them with the motion sickness levels suffered by the passenger, allowing a comfort-based driving recommendation system that reduces it. The designed and validated methodology shows satisfactory results, achieving (from a real datasheet) trained models that identify diverse interpretable clusters, while also shedding light on driving pattern differences. Therefore, a recommendation system to improve passenger motion sickness is proposed.This research was funded by the Basque Government; partial support of this work was received from the project KK-2021/00123 Autoeval and the University of the Basque Country UPV/EHU, grant GIU21/007

Analysis of the Motion Sickness and the Lack of Comfort in Car Passengers

Advanced driving assistance systems (ADAS) are primarily designed to increase driving safety and reduce traffic congestion without paying too much attention to passenger comfort or motion sickness. However, in view of autonomous cars, and taking into account that the lack of comfort and motion sickness increase in passengers, analysis from a comfort perspective is essential in the future car investigation. The aim of this work is to study in detail how passenger’s comfort evaluation parameters vary depending on the driving style, car or road. The database used has been developed by compiling the accelerations suffered by passengers when three drivers cruise two different vehicles on different types of routes. In order to evaluate both comfort and motion sickness, first, the numerical values of the main comfort evaluation variables reported in the literature have been analyzed. Moreover, a complementary statistical analysis of probability density and a power spectral analysis are performed. Finally, quantitative results are compared with passenger qualitative feedback. The results show the high dependence of comfort evaluation variables’ value with the road type. In addition, it has been demonstrated that the driving style and vehicle dynamics amplify or attenuate those values. Additionally, it has been demonstrated that contributions from longitudinal and lateral accelerations have a much greater effect in the lack of comfort than vertical ones. Finally, based on the concrete results obtained, a new experimental campaign is proposed.This research was funded by Basque Government for partial support of this work through the project KK-2021/00123 Autoeval and the University of the Basque Country UPV/EHU under Grant GIU18/122

Diffusional Behavior of New Insulating Gas Mixtures as Alternatives to the SF6-Use in Medium Voltage Switchgear

Regarding the use of SF6 in medium voltage switchgear (MVS), a review of alternatives was encouraged by the European Parliament in Regulation No 517/2014. This is aimed at a new regulatory change, that is expected soon, which will include its prohibition, similar to what has happened with other fluorinated greenhouse gases in other fields, like refrigeration. Therefore, there is an urgent need to study the physical and chemical properties of alternative gas mixtures to determine if they are suitable to replace SF6. In this context, this work addresses the difusional analysis of new gases. Binary and ternary mixtures made of 1,3,3,3-tetrafluoropropene (C3F4H2) and heptafluoroisopropyl trifluoromethyl ketone (C5F10O), using dry air as a carrier gas, were studied. The mixtures were analyzed using original equipment, composed of UV-Vis spectroscopy technology in a sealed gas chamber, which is similar to MVS. Consequently, an experimental equipment that monitors the concentration of a gas mixture online and a model that predicts the mixing process were designed and tested. The concentration profiles were obtained concerning both the time and position in the gas chamber, and the diffusional and convectional parameters were numerically calculated and optimized in an algorithm created in Scilab.This research was funded by the Basque Government, grant numbers KK-2017/00090 and KK-2019/00017

New Generation Compact Linear Accelerator for Low-Current, Low-Energy Multiple Applications

A new compact linear proton accelerator project (named LINAC 7) for multiple low-current applications, designed and built in-house at the Beam Laboratory of the University of the Basque Country (UPV/EHU) is described. The project combines the University, a research technology center and a private company with the aim of designing and building a compact, low-current proton accelerator capable of accelerating particles up to 7 MeV. In this paper, we present an overview of the accelerator design, summarize the progress and testing of the components that have been built, and describe the components that are being designed that will allow us to achieve the final desired energy of 7 MeV.This research was funded by the Basque Government, Department of Economic Development, Sustainability and Environment under codes Elkartek KK-2020/00003 and KK-2021/00029, and by the University of the Basque Country UPV/EHU Research Group ref. GIU18/196

Driving-Style Assessment from a Motion Sickness Perspective Based on Machine Learning Techniques

Ride comfort improvement in driving scenarios is gaining traction as a research topic. This work presents a direct methodology that utilizes measured car signals and combines data processing techniques and machine learning algorithms in order to identify driver actions that negatively affect passenger motion sickness. The obtained clustering models identify distinct driving patterns and associate them with the motion sickness levels suffered by the passenger, allowing a comfort-based driving recommendation system that reduces it. The designed and validated methodology shows satisfactory results, achieving (from a real datasheet) trained models that identify diverse interpretable clusters, while also shedding light on driving pattern differences. Therefore, a recommendation system to improve passenger motion sickness is proposed

A Novel Micro- and Nano-Scale Positioning Sensor Based on Radio Frequency Resonant Cavities

In many micro- and nano-scale technological applications high sensitivity displacement sensors are needed, especially in ultraprecision metrology and manufacturing. In this work a new way of sensing displacement based on radio frequency resonant cavities is presented and experimentally demonstrated using a first laboratory prototype. The principle of operation of the new transducer is summarized and tested. Furthermore, an electronic interface that can be used together with the displacement transducer is designed and proved. It has been experimentally demonstrated that very high and linear sensitivity characteristic curves, in the range of some kHz/nm; are easily obtainable using this kind of transducer when it is combined with a laboratory network analyzer. In order to replace a network analyzer and provide a more affordable, self-contained, compact solution, an electronic interface has been designed, preserving as much as possible the excellent performance of the transducer, and turning it into a true standalone positioning sensor. The results obtained using the transducer together with a first prototype of the electronic interface built with cheap discrete elements show that positioning accuracies in the micrometer range are obtainable using this cost-effective solution. Better accuracies would also be attainable but using more involved and costly electronics interfaces.The authors are grateful to CICYT and to the Basque Government for partial support of this work though projects DPI2011-24821 and IT-381-10, respectively

Interface electronics for an RF resonance-based displacement sensor

6 p. Paper of the 17th Conference on Sensors and Their Applications held in Dubrovnik, Croatia. Sep 16-18, 2013We propose, design, and test an electronic interface for a new standalone, affordable and compact displacement transducer based on resonant cavities. The operation of the interface establishes a self-resonance in the cavities and detects the resonance frequency (which is directly related to the position to be measured) by analyzing the attenuation produced by a low pass filter. The results obtained in a first prototype of the interface built with discrete elements show that the obtainable positioning accuracy using this cost-effective solution is about 5 micrometers.The authors are grateful to CICYT and to the Basque Government for partial support of this work though projects DPI2011-24821 and IT-381-10, respectively