scholarly journals An Empirical Study on V2X Enhanced Low-Cost GNSS Cooperative Positioning in Urban Environments

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5201 ◽  
Author(s):  
Paul Schwarzbach ◽  
Albrecht Michler ◽  
Paula Tauscher ◽  
Oliver Michler
Keyword(s):  
Empirical Study ◽  
Low Cost ◽  
Transportation Systems ◽  
Urban Environments ◽  
Position Estimation ◽  
Sensor Data ◽  
Ieee 802.11P ◽  
Traffic Information ◽  
Automated Driving ◽  
Cooperative Positioning

High-precision and lane selective position estimation is of fundamental importance for prospective advanced driver assistance systems (ADAS) and automated driving functions, as well as for traffic information and management processes in intelligent transportation systems (ITS). User and vehicle positioning is usually based on Global Navigation Satellite System (GNSS), which, as stand-alone positioning, does not meet the necessary requirements in terms of accuracy. Furthermore, the rise of connected driving offers various possibilities to enhance GNSS positioning by applying cooperative positioning (CP) methods. Utilizing only low-cost sensors, especially in urban environments, GNSS CP faces several demanding challenges. Therefore, this contribution presents an empirical study on how Vehicle-to-Everything (V2X) technologies can aid GNSS position estimation in urban environments, with the focus being solely on positioning performance instead of multi-sensor data fusion. The performance of CP utilizing common positioning approaches as well as CP integration in state-of-the-art Vehicular Ad-hoc Networks (VANET) is displayed and discussed. Additionally, a measurement campaign, providing a representational foundation for validating multiple CP methods using only consumer level and low-cost GNSS receivers, as well as commercially available IEEE 802.11p V2X communication modules in a typical urban environment is presented. Evaluating the algorithm’s performance, it is shown that CP approaches are less accurate compared to single positioning in the given environment. In order to investigate error influences, a skyview modelling seeking to identify non-line-of-sight (NLoS) effects using a 3D building model was performed. We found the position estimates to be less accurate in areas which are affected by NLoS effects such as multipath reception. Due to covariance propagation, the accuracy of CP approaches is decreased, calling for strategies for multipath detection and mitigation. In summary, this contribution will provide insights on integration, implementation strategies and accuracy performances, as well as drawbacks for local area, low-cost GNSS CP in urban environments.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

scholarly journals Sensing Urban Transportation Events from Multi-Channel Social Signals with the Word2vec Fusion Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4093 ◽  
Author(s):  
Hao Lu ◽  
Kaize Shi ◽  
Yifan Zhu ◽  
Yisheng Lv ◽  
Zhendong Niu
Keyword(s):  
Single Channel ◽  
Low Cost ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Traffic Information ◽  
Social Signals ◽  
Fusion Model ◽  
Online Data ◽  
Traffic Incidents ◽  
Traffic Incident

Social sensors perceive the real world through social media and online web services, which have the advantages of low cost and large coverage over traditional physical sensors. In intelligent transportation researches, sensing and analyzing such social signals provide a new path to monitor, control and optimize transportation systems. However, current research is largely focused on using single channel online social signals to extract and sense traffic information. Clearly, sensing and exploiting multi-channel social signals could effectively provide deeper understanding of traffic incidents. In this paper, we utilize cross-platform online data, i.e., Sina Weibo and News, as multi-channel social signals, then we propose a word2vec-based event fusion (WBEF) model for sensing, detecting, representing, linking and fusing urban traffic incidents. Thus, each traffic incident can be comprehensively described from multiple aspects, and finally the whole picture of unban traffic events can be obtained and visualized. The proposed WBEF architecture was trained by about 1.15 million multi-channel online data from Qingdao (a coastal city in China), and the experiments show our method surpasses the baseline model, achieving an 88.1% F1 score in urban traffic incident detection. The model also demonstrates its effectiveness in the open scenario test.

scholarly journals Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2452 ◽  
Author(s):  
Lakshay Narula ◽  
Michael Wooten ◽  
Matthew Murrian ◽  
Daniel LaChapelle ◽  
Todd Humphreys
Keyword(s):  
Low Cost ◽  
Vertical Direction ◽  
Satellite System ◽  
Sensor Data ◽  
Limiting Factor ◽  
Automated Driving ◽  
Digital Maps ◽  
Digital Mapping ◽  
Structure And Motion ◽  
Localization And Mapping

Exchange of location and sensor data among connected and automated vehicles will demand accurate global referencing of the digital maps currently being developed to aid positioning for automated driving. This paper explores the limit of such maps’ globally-referenced position accuracy when the mapping agents are equipped with low-cost Global Navigation Satellite System (GNSS) receivers performing standard code-phase-based navigation, and presents a globally-referenced electro-optical simultaneous localization and mapping pipeline, called GEOSLAM, designed to achieve this limit. The key accuracy-limiting factor is shown to be the asymptotic average of the error sources that impair standard GNSS positioning. Asymptotic statistics of each GNSS error source are analyzed through both simulation and empirical data to show that sub-50-cm accurate digital mapping is feasible in the horizontal plane after multiple mapping sessions with standard GNSS, but larger biases persist in the vertical direction. GEOSLAM achieves this accuracy by (i) incorporating standard GNSS position estimates in the visual SLAM framework, (ii) merging digital maps from multiple mapping sessions, and (iii) jointly optimizing structure and motion with respect to time-separated GNSS measurements.

scholarly journals City Wide Participatory Sensing of Air Quality

2021 ◽  
Vol 9 ◽  
Author(s):  
Andrew Rebeiro-Hargrave ◽  
Pak Lun Fung ◽  
Samu Varjonen ◽  
Andres Huertas ◽  
Salla Sillanpää ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Low Cost ◽  
Age Groups ◽  
Personal Exposure ◽  
Urban Environments ◽  
Sensor Data ◽  
Automated System ◽  
Participatory Sensing ◽  
Good Correspondence

Air pollution is a contributor to approximately one in every nine deaths annually. Air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality stations are expensive to maintain resulting in sparse coverage and data is not readily available to citizens. This can be resolved by city-wide participatory sensing of air quality fluctuations using low-cost sensors. We introduce new concepts for participatory sensing: a voluntary community-based monitoring data forum for stakeholders to manage air pollution interventions; an automated system (cyber-physical system) for monitoring outdoor air quality and indoor air quality; programmable platform for calibration and generating virtual sensors using data from low-cost sensors and city monitoring stations. To test our concepts, we developed a low-cost sensor to measure particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) with GPS. We validated our approach in Helsinki, Finland, with participants carrying the sensor for 3 months during six data campaigns between 2019 and 2021. We demonstrate good correspondence between the calibrated low-cost sensor data and city’s monitoring station measurements. Data analysis of their personal exposure was made available to the participants and stored as historical data for later use. Combining the location of low cost sensor data with participants public profile, we generate proxy concentrations for black carbon and lung deposition of particles between districts, by age groups and by the weekday.

scholarly journals Lane Discovery in Traffic Video

1969 ◽  
Vol 2 (1) ◽  
Author(s):  
Nicholas Miller ◽  
David M. Swart ◽  
Akshaya Mishra ◽  
Andrew Achkar
Keyword(s):  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Transportation Systems ◽  
Traffic Information ◽  
Surveillance Video ◽  
Counting Problem ◽  
Moving Vehicle ◽  
Non Invasive ◽  
Vehicle Trajectories ◽  
Reduced Space

Video sensing has become very important in Intelligent Transportation Systems (ITS) due to its relative low cost and non-invasive deployment. An effective ITS requires detailed traffic information, including vehicle volume counts for each lane in surveillance video of a highway or an intersection. The multiple-target, vehicle-tracking and counting problem is most reliably solved in a reduced space defined by the constraints of the vehicles driving within lanes. This requires lanes to be pre-specified. An off-line pre-processing method is presented which automatically discovers traffic lanes from vehicle motion in uncalibrated video from a stationary camera. A moving vehicle density map is constructed, then multiple lane curves are fitted. Traffic lanes are found without relying on possibly noisy tracked vehicle trajectories.

scholarly journals Modeling and Analysis on Minimum Safe Distance for Platooning Vehicles Based on Field Test of Communication Delay

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mengyan Hu ◽  
Xiangmo Zhao ◽  
Fei Hui ◽  
Bin Tian ◽  
Zhigang Xu ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Field Tests ◽  
Transportation Systems ◽  
Communication Delay ◽  
Ieee 802.11P ◽  
Safe Distance ◽  
Automated Driving ◽  
Emergency Braking ◽  
The Impact ◽  
Minimum Safe Distance

Vehicle platooning is a perspective technique for intelligent transportation systems (ITS). Connected and automated vehicles (CAVs) use dedicated short-range communication (DSRC) to form a convoy, in which the following vehicles can receive the information from their preceding vehicles to achieve safe automated driving and maintain a short headway. Consequently, a vehicle platoon can improve traffic safety and efficiency, further reducing fuel consumption. However, emergency braking inevitably occurs when the platoon meets an accident or a sudden mechanical failure. It is more critical when the wireless communication got delays. Therefore, “how to predefine a minimum safe distance (MSD) considering communication delay” is a challenging issue. To this end, a series of field tests were carried out to measure the communication delay of IEEE 802.11p that is the underlying protocol of DSRC. Subsequently, MSD is modeled and analyzed when the platoon travels at accelerating, cruising, and decelerating states. More importantly, the results of field tests are applied in the models to investigate the impact of communication delay on MSD in practice. The simulation results verify that the proposed model can effectively maintain the platooning vehicles’ safety even if emergency braking happens with certain communication delays.

Packaging Environmental Sensors for an Internet-of-Things Solution for Urban-Microclimate Studies

2019 ◽  
Author(s):  
Shuv Dey ◽  
Yogendra Joshi ◽  
J. Michael Brown
Keyword(s):  
Rural Areas ◽  
Low Cost ◽  
Urban Environments ◽  
Environmental Data ◽  
Sensor Data ◽  
Heat Island ◽  
Environmental Sensors ◽  
Negative Effects ◽  
Air Temperatures ◽  
Urban Heat

Abstract Cities are experiencing a number of negative effects caused by increasing urbanization. For decades, the effects of pollution have been recognized and studied and steps have been taken attempting to control this problem. Many urban environments are also experiencing the effect of the Urban Heat Island (UHI). UHIs are metropolitan areas that have measurably warmer average air temperatures during several periods during the year, than their surrounding rural areas. There is a great interest in studying UHI and pollution and its effects on the environment as well as urban residents. However, in order to study these phenomena, we need more information than we currently have. Thus, an IoT based low cost sensor network can be used to collect the data necessary to study UHI and pollution. There are several key challenges associated with an IoT based solution to environmental data monitoring. This study explores these challenges by looking at what effect the packaging has on the deployed environmental sensors, and how and where to deploy sensor modules. Sensor data collected over a few months’ timeframe are analyzed and presented.

scholarly journals An Integrated Dead Reckoning with Cooperative Positioning Solution to Assist GPS NLOS Using Vehicular Communications

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2895 ◽  
Author(s):  
Pedro Nascimento ◽  
Bruno Kimura ◽  
Daniel Guidoni ◽  
Leandro Villas
Keyword(s):  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Dead Reckoning ◽  
Transportation Systems ◽  
Ieee 802.11P ◽  
Vehicular Communication ◽  
Cooperative Positioning ◽  
Wide Range

In Intelligent Transportation Systems (ITS), the Vehicular Ad Hoc Networks (VANETs) paradigm based on the WAVE IEEE 802.11p standard is the main alternative for inter-vehicle communications. Recently, many protocols, applications, and services have been developed with a wide range of objectives, ranging from comfort to security. Most of these services rely on location systems and require different levels of accuracy for their full operation. The Global Positioning System (GPS) is an off-the-shelf solution for localization in VANETs and ITS. However, GPS systems present problems regarding inaccuracy and unavailability in dense urban areas, multilevel roads, and tunnels, posing a challenge for protocols, applications, and services that rely on localization. With this motivation, we carried out a characterization of the problems of inaccuracy and unavailability of GPS systems from real datasets, and regions around tunnels were selected. Since the nodes of the vehicular network are endowed with wireless communication, processing and storage capabilities, an integrated Dead Reckoning aided Geometric Dilution of Precision (GDOP)-based Cooperative Positioning solution was developed and evaluated. Leveraging the potential of vehicular sensors, such as odometers, gyroscopes, and digital compasses, vehicles share their positions and kinematics information using vehicular communication to improve their location estimations. With the assistance of a digital map, vehicles adjust the final estimated position using the road geometry. The situations of GPS unavailability characterized in the datasets were reproduced in a simulation environment to validate the proposed localization solution. The simulation results show average gains in Root Mean Square Error (RMSE) between 97% to 98% in comparison with the stand-alone GPS solution, and 83.00% to 88.00% against the GPS and Dead Reckoning (DR) only solution. The average absolute RMSE was reduced to the range of 3 to 5 m by vehicle. In addition, the proposed solution was shown to support 100% of the GPS unavailability zones on the evaluated scenarios.

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