Response-based methods to measure road surface irregularity: a state-of-the-art review

"jats:sec" "jats:title"Purpose"/jats:title" "jats:p"With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the state-of-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research."/jats:p" "/jats:sec" "jats:sec" "jats:title"Methods"/jats:title" "jats:p"Available articles regarding response-based methods to measure road surface condition were collected mainly from “Scopus” database and partially from “Google Scholar”. The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation."/jats:p" "/jats:sec" "jats:sec" "jats:title"Results"/jats:title" "jats:p"The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking."/jats:p" "/jats:sec Document type: Articl

“Uso de algoritmos para la identificación de imperfecciones en la calzada: Un mapeo sistemático”

La gran mayoría de los accidentes de tránsito son provocados por las imperfecciones en la calzada, por este motivo se ha ido adaptando diferentes algoritmos de inteligencia artificial para su detección. El propósito de este trabajo se centra en el desarrollo de un análisis de la literatura del periodo comprendido entre las dos últimas décadas que incluye temas relacionados con el uso de algoritmos de inteligencia artificial para la identificación de imperfecciones en la calzada. La metodología empleada en este trabajo se basa en técnicas de Mapeo Sistemático, un proceso que consta de tres etapas: Definiciones de Protocolo, Ejecuciones de Búsqueda y Discusión de Resultados. Como resultado de este análisis, se obtuvieron 74 artículos relevantes de acuerdo a los criterios de inclusión donde se proponen 41 algoritmos y tres enfoques de identificación de imperfecciones en la calzada, con porcentajes de exactitud desde el 95.45% hasta el 99.8%. Mismos que fueron obtenidos de repositorios como SciencieDirect, IEEE y Scopus.The vast majority of traffic accidents are caused by imperfections in the road, for this reason different artificial intelligence algorithms have been adapted for their detection. The purpose of this work is focused on the development of an analysis of the literature of the period between the last two decades that includes topics related to the use of artificial intelligence algorithms for the identification of imperfections in the road. The methodology used in this work is based on Systematic Mapping techniques, a process that consists of three stages: Protocol Definitions, Search Executions and Results Discussion. As a result of this analysis, 74 relevant articles were obtained according to the inclusion criteria where 41 algorithms and three approaches to identify imperfections in the road are proposed, with percentages of accuracy from 95.45% to 99.8%. The same ones that were obtained from repositories such as SciencieDirect, IEEE and Scopus

Pyöräilyn ympäristötekijöiden mittaaminen esineiden internetin sovelluksia varten

Increasing population in cities creates increasing amount of traffic, which leads to emissions and traffic congestion. Smart Cities set out to solve the challenges urban cities face due to the increased population, using Internet of Things as means to monitor the assets as it allows non-traditional devices to connect as a part of global information network. At the same time, cycling has increased its popularity as an environmentally friendly as well as healthy transportation method. To further its usage, infrastructure in cities must support cycling as a serious transportation method. For this purpose, it is important to include bicycles to Smart City with measurements of cycling and its environment. This thesis studies if it is possible to measure factors affecting cycling environment and assess route quality without using sensors built in bicycle frame. Decision to avoid sensors embedded in frame stemmed from incentive to have easily available and inexpensive measuring device, which does not bind the cyclists to use bicycles from specific brand or require them to purchase new bike if they are interested in participating in measuring. For evaluating the feasibility of cycling environment measuring, prototype called BikeBox was built and used during test drives. In addition, an online survey was held, which received answers from 97 cyclists. The survey queried about their cycling habits and preferences to better understand what kind of data they would be interested in. The prototype included accelerometer for measuring road quality, photoresistor to identify poorly lit areas and GPS module for location and timestamps, which are needed for other measurements as well as finding possible stopping points and slow areas on the route. Based on the test drives it is possible to identify quality changes on road surface as well as changes in lighting. Inaccurate GPS positioning does pose a challenge for pinpointing exact locations, though. Using location and timestamps it is possible to calculate the speed along different parts of the route, including areas which cause interruptions for the cyclists. This thesis presents results from 7 different example drives, though during testing phase more test driving was done. To get comprehensive coverage, crowdsourcing should be considered as the data gathering method. Based on the survey fastness and length of the route, amount of stops and interruptions and road condition are one of the most important factors for the cyclists. When queried what kind of information cyclists would like to receive, the road condition related factors were most commonly mentioned.Kaupungistumisen seurauksena väkimäärät kaupungeissa kasvavat, mikä tuo mukanaan kasvavat liikennemäärät, ruuhkat ja liikennepäästöt. Älykkäät kaupungit ovat reaktio kaupungistumisesta seuraaviin haasteisiin. Älykkäät kaupungit pyrkivät seuraamaan ja kontrolloimaan kaupungin infrastruktuuria, apunaan esineiden internet. Esineiden internet mahdollistaa epäperinteisten laitteiden yhdistämisen maailmanlaajuiseen tietoverkkoon. Samaan aikaan pyöräilyn suosio on kasvanut ympäristöystävällisenä ja terveellisenä liikennemuotona. Jos pyöräilyn määrää halutaan jatkossakin kasvattaa, kaupungin infrastruktuurin täytyy tukea pyöräilyä vakavasti otettavana liikennemuotona. Jotta tämä voidaan saavuttaa, on pyöräilijöiden pyöräily-ympäristön ja pyöräilytapojen ymmärtäminen tärkeää. Tässä työssä tutkitaan, onko pyöräily-ympäristöön vaikuttavia tekijöitä mahdollista mitata sensoreilla, joita ei ole istutettu polkupyörän runkoon. Runkoon upotettuja sensoreita haluttiin välttää, jotta mittauslaitteet voisivat olla mahdollisimman suuren joukon saatavilla, eikä pyöräilijä olisi sidottu käyttämään tietyn valmistajan polkupyörää. Lisäksi pyritään selvittämään, minkälaisesta pyöräily-ympäristöön liittyvästä datasta pyöräilijät olisivat kiinnostuneita. Tähän tarkoitukseen rakennettiin prototyyppi PyöräPurkista (BikeBox). Lisäksi toteutettiin internet-kysely, johon vastasi 97 polkupyöräilijää. Kyselyllä selvitettiin pyöräilijöiden pyöräilytapoja ja -mieltymyksiä ja sitä, millainen pyöräily-ympäristöstä kertova data kiinnostaisi heitä. Prototyyppiin sisällytettiin kiihtyvyysanturi tien pinnan laadun mittaamiseen, valoanturi heikosti valaistujen alueiden tunnistamiseen ja GPS-moduuli, jolla saadaan sijantitieto ja kellonaika muita mittauksia varten. Lisäksi sijaintitiedosta ja kellonajasta voidaan laskea ajonopeus ja paikat, missä pyöräilijä on joutunut keskeyttämään ajonsa. Testiajojen perusteella on mahdollista havaita tien pinnanlaadun muutos sekä muutos valaistusolosuhteissa. Epätarkkuudet GPS-paikannuksessa vaikeuttavat kuitenkin ongelmakohtien tarkkaa paikallistamista. Tämä työ käsittelee aiheita 7 erillisen testiajon kautta, vaikka testausvaiheessa ajettiinkin useampia testiajoja. Kattavien mittausten saamiseksi joukkoistamista kannattaisi harkita datankeräysmetodina. Tehdyn kyselyn perusteella reitin nopeus, pituus, reitillä olevien keskeytysten määrä ja tien kunto ovat tärkeimpiä reitin laatuun vaikuttavia tekijöitä. Erilaiset pyöräilyreitin kuntoon liittyvät asiat kiinnostivat eniten kun kysyttiin, minkälaista dataa pyöräilijät haluaisivat saada

Demand-driven data acquisition for large scale fleets

Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker’s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Optimization for Deep Learning Systems Applied to Computer Vision

149 p.Since the DL revolution and especially over the last years (2010-2022), DNNs have become an essentialpart of the CV field, and they are present in all its sub-fields (video-surveillance, industrialmanufacturing, autonomous driving, ...) and in almost every new state-of-the-art application that isdeveloped. However, DNNs are very complex and the architecture needs to be carefully selected andadapted in order to maximize its efficiency. In many cases, networks are not specifically designed for theconsidered use case, they are simply recycled from other applications and slightly adapted, without takinginto account the particularities of the use case or the interaction with the rest of the system components,which usually results in a performance drop.This research work aims at providing knowledge and tools for the optimization of systems based on DeepLearning applied to different real use cases within the field of Computer Vision, in order to maximizetheir effectiveness and efficiency