Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving

Muñoz-Organero, Mario; Blázquez, Ramona Ruiz; Sánchez-Fernandez, Luis

doi:10.1016/j.compenvurbsys.2017.09.005

Cited by 70 publications

(34 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DBNs can be also be trained greedily layer by layer. DBNs are also used in [24], in order to assist digital map creation by automatic street elements detection such as traffic lights, roundabouts, etc. The input data for the system are obtained only by users GPS data.…”

Section: Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

View full text Add to dashboard Cite

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications. Additionally, route optimization, parking, and accident/detection tend to be the most popular ITS applications among researchers.

show abstract

Section: Deep Learningmentioning

confidence: 99%

“…All the classifiers presented an accuracy over 99% based on the CCR measurements. Finally, in [24], the problem of the automated detection of street elements dealt with the application of a DBN is shown. At the end of the DBN a classifier is used for the final element classification.…”

Section: Instance Basedmentioning

confidence: 99%

A Review of Machine Learning and IoT in Smart Transportation

et al. 2019

View full text Add to dashboard Cite

show abstract

“…TR is detected only by 10% of studies and less popular regulators are PS/YS (5%). Here we should note that some studies, except for those on traffic regulators, detect other map/street elements, such as street crossings and roundabouts in Reference [81] or underpasses, stairs, escalators, footbridges, crosswalks, elevators, ramps, and so forth, as described in Reference [77]. Since this SLR focuses on intersection controlling categories, we ignored these elements from the study.…”

Section: Regulators: Categories and Diversionmentioning

confidence: 99%

“…The other 27.27% used both publicly and non-publicly datasets. In the latter case, all but one used data acquired from OSM and only one study [81] reports the (non OSM) data source, which one can use to download data from. Obviously, even using open data sources like OSM, unless details are given regarding the data acquisition, one cannot get access to the exact dataset that other studies had previously used.…”

Section: Dataset Typementioning

confidence: 99%

“…In another case [52], open data were used only for testing algorithms but without verifying their results, most probably due to ground truth map unavailability. Last, the third study that uses both types of datasets [81], applied the proposed methods separately to each dataset, since for the public dataset the ground truth map was also provided.…”

Section: Dataset Typementioning

confidence: 99%

See 1 more Smart Citation

Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review

Zourlidou¹,

Sester²

2019

IJGI

View full text Add to dashboard Cite

Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies.

show abstract

Deep learning support for intelligent transportation systems

Ibáñez

Contreras-Castillo

Zeadally

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Intelligent Transportation Systems (ITS) help improve the ever-increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in-depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

show abstract

Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving

Abstract: Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving.

Cited by 70 publications

References 22 publications

A Review of Machine Learning and IoT in Smart Transportation

A Review of Machine Learning and IoT in Smart Transportation

Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review

Deep learning support for intelligent transportation systems

Contact Info

Product

Resources

About