Towards Comfortable Cycling: A Practical Approach to Monitor the Conditions in Cycling Paths

This is a no brainer. Using bicycles to commute is the most sustainable form of transport, is the least expensive to use and are pollution-free. Towns and cities have to be made bicycle-friendly to encourage their wide usage. Therefore, cycling paths should be more convenient, comfortable, and safe to ride. This paper investigates a smartphone application, which passively monitors the road conditions during cyclists ride. To overcome the problems of monitoring roads, we present novel algorithms that sense the rough cycling paths and locate road bumps. Each event is detected in real time to improve the user friendliness of the application. Cyclists may keep their smartphones at any random orientation and placement. Moreover, different smartphones sense the same incident dissimilarly and hence report discrepant sensor values. We further address the aforementioned difficulties that limit such crowd-sourcing application. We evaluate our sensing application on cycling paths in Singapore, and show that it can successfully detect such bad road conditions.Comment: 6 pages, 5 figures, Accepted by IEEE 4th World Forum on Internet of Things (WF-IoT) 201

Road Surface Quality Detection Using Accelerometer Data

Nutiseadmed on muutunud igapäevaelu lahutamatuks osaks. Suurenev populaarsus, arvutusvõimsus ja erinevate sensorite olemasolu on avanud uusi võimalusi nutiseadmete kasutamiseks. Lisaks nutiseadmetele on tänapäeva ühiskonnas tähtis roll ka teede võrgustikul.Kuna sõiduteid kasutab igapäevaselt suur hulk inimesi, siis on teede lagunemine paljudes kohtades endiselt probleemiks.Käesoleva töö eesmärgiks on pakkuda algoritm sõiduteekatte kvaliteedi tuvastamiseks ning Androidi rakendus aktseleromeetri andmete kogumiseks. Algoritm kasutab kogutud aktseleromeetri andmeid erinevatelt sõiduteedelt, eesmärgiga treenida klassifitseerija teekatte kvaliteedi tuvastamiseks. Aktseleromeetri andmeid kogutakse eelnevalt mainitud Androidi rakendusega, mis valmib käesoleva töö raames. Töö lõpptulemuseks on proof of concept lahendus, mis võimaldab tuvastada ning klassifitseerida sõiduteid teekatte kvaliteedi järgi kolme defineeritud kategooriasse.Smartphones and tablets have become an integral part of modern society. Increasing popularity, computing power and availability of different sensors have opened new ways to utilize these smart devices. In addition to smartphones and tablets, another important aspect of modern society is the road network. Since roads are used by a large number of people every day, deterioration remains a problem in many places. The aim of this thesis is to provide an algorithm for road surface quality detection and an Android application for accelerometer data collection.The algorithm uses accelerometer data, collected from different roads, in order to train a classifier for road surface quality detection. The accelerometer data is collected with the aforementioned Android application, which is developed as a result of this thesis. The overall result of this thesis is a proof of concept solution, that can be used to detect and classify the surface quality of roads into three road quality categories

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

Machine Learning Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor

The walkability of a neighborhood impacts public health and leads to economic and environmental benefits. The condition of sidewalks is a significant indicator of a walkable neighborhood as it supports and encourages pedestrian travel and physical activity. However, common sidewalk assessment practices are subjective, inefficient, and ineffective. Current alternate methods for objective and automated assessment of sidewalk surfaces do not consider pedestrians’ physiological responses. We developed a novel classification framework for the detection of irregular walking surfaces that uses a machine learning approach to analyze gait parameters extracted from a single wearable accelerometer. We also identified the most suitable location for sensor placement. Experiments were conducted on 12 subjects walking on good and irregular walking surfaces with sensors attached at three different locations: right ankle, lower back, and back of the head. The most suitable location for sensor placement was at the ankle. Among the five classifiers trained with gait features from the ankle sensor, Support Vector Machine (SVM) was found to be the most effective model since it was the most robust to subject differences. The model’s performance was improved with post-processing. This demonstrates that the SVM model trained with accelerometer-based gait features can be used as an objective tool for the assessment of sidewalk walking surface conditions

Road monitoring system based on iot technology for SmartCity

This work is devoted to the problem of automatic road quality control, which can be used by both road repair services and common drivers. This paper provides a survey of different known techniques and algorithms of finding potholes on the road and describe our own method, using accelerometer. It will be shown our device for detecting potholes on the road, which can be used, as a part of the IoT system of SmartCity. It uses data from an accelerometer for finding road bumps

Evaluating and choosing a machine learning algorithm for classifying road surface quality data

Applied project submitted to the Department of Computer Science, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2018Considering the importance of roads to a community, stakeholders (Governments, Motoristsetc) needup-to-dateinformation about the state of roads for decisionmaking. This problem inspired Vorgbe’s (2014) work in implementing a machine learning classifier that could accurately classify roads as “good”, “fair” or “bad”. This information can then be visualised on Google Maps. However, with his algorithm failing to accurately classify some roads, this project seeks to evaluate five classification algorithms to determine which one is best for classifying road surface quality data. To do this, we collected x, y, z acceleration and location data, extracted the desired features from it, performed a 10-fold cross-validation trainingonthedatatochoosethe best model and then tested on a new set of examples to determine the model that accurately classifies the data. Fromthedataavailable,thedecisiontreemodelproduced the best performance with true positives of 97% accuracy for bad roads,81%accuracy for fair roads and 93% accuracy for good roads. The overall accuracy on the test set is 92% with a precision of 92% and recall of 90%. This means that, this model is more likely to accurately predict a new data point as belonging to its true class. The other algorithms (Logistic Regression, Random Forests, Support Vector Machines and Nearest Neighbour) performed well when classifying the “good” and “bad” road data but instead classified the “fair” road data as “good” road.Ashesi Universit

A Driver, Vehicle and Road Safety System Using Smartphones

As vehicle manufacturers continue to increase their emphasis on safety with advanced driver assistance systems (ADAS), I propose a ubiquitous device that is able to analyze and advise on safety conditions. Mobile smartphones are increasing in popularity among younger generations with an estimated 64% of 25-34 year olds already using one in their daily lives. with over 10 million car accidents reported in the United States each year, car manufacturers have shifted their focus of a passive approach (airbags) to more active by adding features associated with ADAS (lane departure warnings). However, vehicles manufactured with these sensors are not economically priced while older vehicles might only have passive safety features. Given its accessibility and portability, I target a mobile smartphone as a device to compliment ADAS that can bring a driver assist to any vehicle without regards for any on-vehicle communication system requirements. I use the 3-axis accelerometer of multiple Android based smartphone to record and analyze various safety factors which can influence a driver while operating a vehicle. These influences with respect to the driver, vehicle and road are lane change maneuvers, vehicular comfort and road conditions. Each factor could potentially be hazardous to the health of the driver, neighboring public, and automobile and is therefore analyzed thoroughly achieving 85.60% and 89.89% classification accuracy for identifying road anomalies and lane changes, respectively. Effective use of this data can educate a potentially dangerous driver on how to operate a vehicle safely and efficiently. with real time analysis and auditory alerts of these factors, I hope to increase a driver's overall awareness to maximize safety

Impact detection using a machine learning approach and experimental road roughness classification

First, this publication presents the experimental validation of a road roughness classification method. Second, an impact detection strategy for two-wheeled vehicles is proposed including a classification of service loads, mild special events, and severe special events. The methods presented utilise the vehicle’s onboard signals to gather field data. The modular road roughness classification system operates with the vehicle’s transfer functions, and continuously classifies the road profile, according to ISO 8608. The method was successfully validated on test tracks with known road profiles. The impact detection strategy was developed using a supervised machine learning technique. Six road obstacles were ridden over using different velocities to invoke mild and severe special events. The most popular classifiers were trained for comparison and prediction of future observations. The developed impact detection strategy shows a high accuracy and was successfully validated using a k-fold cross-validation. The combination of the road roughness classification system and the impact detection strategy, enables a holistic field data acquisition of customer usage profiles, in the context of durability engineering. The collection of customer usage profiles improves vehicle design targets and enables a virtual load acquisition

An Orientation & Mobility Aid for People with Visual Impairments

Orientierung&Mobilität (O&M) umfasst eine Reihe von Techniken für Menschen mit Sehschädigungen, die ihnen helfen, sich im Alltag zurechtzufinden. Dennoch benötigen sie einen umfangreichen und sehr aufwendigen Einzelunterricht mit O&M Lehrern, um diese Techniken in ihre täglichen Abläufe zu integrieren. Während einige dieser Techniken assistive Technologien benutzen, wie zum Beispiel den Blinden-Langstock, Points of Interest Datenbanken oder ein Kompass gestütztes Orientierungssystem, existiert eine unscheinbare Kommunikationslücke zwischen verfügbaren Hilfsmitteln und Navigationssystemen. In den letzten Jahren sind mobile Rechensysteme, insbesondere Smartphones, allgegenwärtig geworden. Dies eröffnet modernen Techniken des maschinellen Sehens die Möglichkeit, den menschlichen Sehsinn bei Problemen im Alltag zu unterstützen, die durch ein nicht barrierefreies Design entstanden sind. Dennoch muss mit besonderer Sorgfalt vorgegangen werden, um dabei nicht mit den speziellen persönlichen Kompetenzen und antrainierten Verhaltensweisen zu kollidieren, oder schlimmstenfalls O&M Techniken sogar zu widersprechen. In dieser Dissertation identifizieren wir eine räumliche und systembedingte Lücke zwischen Orientierungshilfen und Navigationssystemen für Menschen mit Sehschädigung. Die räumliche Lücke existiert hauptsächlich, da assistive Orientierungshilfen, wie zum Beispiel der Blinden-Langstock, nur dabei helfen können, die Umgebung in einem limitierten Bereich wahrzunehmen, während Navigationsinformationen nur sehr weitläufig gehalten sind. Zusätzlich entsteht diese Lücke auch systembedingt zwischen diesen beiden Komponenten — der Blinden-Langstock kennt die Route nicht, während ein Navigationssystem nahegelegene Hindernisse oder O&M Techniken nicht weiter betrachtet. Daher schlagen wir verschiedene Ansätze zum Schließen dieser Lücke vor, um die Verbindung und Kommunikation zwischen Orientierungshilfen und Navigationsinformationen zu verbessern und betrachten das Problem dabei aus beiden Richtungen. Um nützliche relevante Informationen bereitzustellen, identifizieren wir zuerst die bedeutendsten Anforderungen an assistive Systeme und erstellen einige Schlüsselkonzepte, die wir bei unseren Algorithmen und Prototypen beachten. Existierende assistive Systeme zur Orientierung basieren hauptsächlich auf globalen Navigationssatellitensystemen. Wir versuchen, diese zu verbessern, indem wir einen auf Leitlinien basierenden Routing Algorithmus erstellen, der auf individuelle Bedürfnisse anpassbar ist und diese berücksichtigt. Generierte Routen sind zwar unmerklich länger, aber auch viel sicherer, gemäß den in Zusammenarbeit mit O&M Lehrern erstellten objektiven Kriterien. Außerdem verbessern wir die Verfügbarkeit von relevanten georeferenzierten Datenbanken, die für ein derartiges bedarfsgerechtes Routing benötigt werden. Zu diesem Zweck erstellen wir einen maschinellen Lernansatz, mit dem wir Zebrastreifen in Luftbildern erkennen, was auch über Ländergrenzen hinweg funktioniert, und verbessern dabei den Stand der Technik. Um den Nutzen von Mobilitätsassistenz durch maschinelles Sehen zu optimieren, erstellen wir O&M Techniken nachempfundene Ansätze, um die räumliche Wahrnehmung der unmittelbaren Umgebung zu erhöhen. Zuerst betrachten wir dazu die verfügbare Freifläche und informieren auch über mögliche Hindernisse. Weiterhin erstellen wir einen neuartigen Ansatz, um die verfügbaren Leitlinien zu erkennen und genau zu lokalisieren, und erzeugen virtuelle Leitlinien, welche Unterbrechungen überbrücken und bereits frühzeitig Informationen über die nächste Leitlinie bereitstellen. Abschließend verbessern wir die Zugänglichkeit von Fußgängerübergängen, insbesondere Zebrastreifen und Fußgängerampeln, mit einem Deep Learning Ansatz. Um zu analysieren, ob unsere erstellten Ansätze und Algorithmen einen tatsächlichen Mehrwert für Menschen mit Sehschädigung erzeugen, vollziehen wir ein kleines Wizard-of-Oz-Experiment zu unserem bedarfsgerechten Routing — mit einem sehr ermutigendem Ergebnis. Weiterhin führen wir eine umfangreichere Studie mit verschiedenen Komponenten und dem Fokus auf Fußgängerübergänge durch. Obwohl unsere statistischen Auswertungen nur eine geringfügige Verbesserung aufzeigen, beeinflußt durch technische Probleme mit dem ersten Prototypen und einer zu geringen Eingewöhnungszeit der Probanden an das System, bekommen wir viel versprechende Kommentare von fast allen Studienteilnehmern. Dies zeigt, daß wir bereits einen wichtigen ersten Schritt zum Schließen der identifizierten Lücke geleistet haben und Orientierung&Mobilität für Menschen mit Sehschädigung damit verbessern konnten