The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

A critical review on unmanned aerial vehicles power supply and energy management: Solutions, strategies, and prospects

Recently, the availability of unmanned aerial vehicle (UAV) opens up new opportunities for smart transportation applications, such as automatic traffic data collection. In such a trend, detecting vehicles and extracting traffic parameters from UAV video in a fast and accurate manner is becoming crucial in many prospective applications. However, from the methodological perspective, several limitations have to be addressed before the actual implementation of UAV. This paper proposes a new and complete analysis framework for traffic flow parameter estimation from UAV video. This framework addresses the well-concerned issues on UAV's irregular ego-motion, low estimation accuracy in dense traffic situation, and high computational complexity by designing and integrating four stages. In the first two stages an ensemble classifier (Haar cascade + convolutional neural network) is developed for vehicle detection, and in the last two stages a robust traffic flow parameter estimation method is developed based on optical flow and traffic flow theory. The proposed ensemble classifier is demonstrated to outperform the state-of-the-art vehicle detectors that designed for UAV-based vehicle detection. Traffic flow parameter estimations in both free flow and congested traffic conditions are evaluated, and the results turn out to be very encouraging. The dataset with 20,000 image samples used in this study is publicly accessible for benchmarking at http://www.uwstarlab.org/research.html.

Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow

Race Car Vehicle Dynamics

The new era of sharing information and “big data” has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the existing traditional ways of collecting traffic data that are based either on fixed-location sensors or GPS devices with low spatial coverage or penetration rates and significant measurement errors, especially in congested urban areas. Unmanned Aerial Systems (UAS) or simply “drones” have been proposed as a pioneering tool of the Intelligent Transportation Systems (ITS) infrastructure due to their unique characteristics, but various challenges have kept these efforts only at a small size. This paper describes the system architecture and preliminary results of a first-of-its-kind experiment, nicknamed pNEUMA, to create the most complete urban dataset to study congestion. A swarm of 10 drones hovering over the central business district of Athens over multiple days to record traffic streams in a congested area of a 1.3 km2 area with more than 100 km-lanes of road network, around 100 busy intersections (signalized or not), many bus stops and close to half a million trajectories. The aim of the experiment is to record traffic streams in a multi-modal congested environment over an urban setting using UAS that can allow the deep investigation of critical traffic phenomena. The pNEUMA experiment develops a prototype system that offers immense opportunities for researchers many of which are beyond the interests and expertise of the authors. This open science initiative creates a unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was available till now, that researchers from different disciplines around the globe can use to develop and test their own models.

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On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment

This paper presents a methodology for tracking moving vehicles that integrates Unmanned Aerial Vehicles with video processing techniques. The authors investigated the usefulness of Unmanned Aerial Vehicles to capture reliable individual vehicle data by using GPS technology as a benchmark. A video processing algorithm for vehicles trajectory acquisition is introduced. The algorithm is based on OpenCV libraries. In order to assess the accuracy of the proposed video processing algorithm an instrumented vehicle was equipped with a high precision GPS. The video capture experiments were performed in two case studies. From the field, about 24,000 positioning data were acquired for the analysis. The results of these experiments highlight the versatility of the Unmanned Aerial Vehicles technology combined with video processing technique in monitoring real traffic data.

Evaluating the Accuracy of Vehicle Tracking Data Obtained from Unmanned Aerial Vehicles

Before smartphone probes can be used to obtain instantaneous vehicle speeds and other dynamic characteristics, the accuracy of these estimates needs to be established under varying degrees of satellite signal interruption commonly found with varying road and traffic conditions. This paper presents the results of several vehicle tracking tests comparing smartphone speeds to benchmark values obtained for three types of routes. Benchmark values were obtained using a high frequency calibrated V-Box mounted on the test vehicle with four Android OS smartphone units. A relationship is established linking error in smartphone instantaneous speeds to the corresponding signal Circular Error Probable (CEP) range for different road and traffic conditions. This relationship is used to provide speed adjustment factors for the smartphone probe estimates subject to varying satellite signal strength. The CEP test is a reported GPS unit indicator of precision based on a known ground control benchmark. Smartphone speeds (adjusted and unadjusted) are compared to aggregate speed profiles from a stationary radar detector placed at a given location along SS106 in southern Italy. The smartphone devices were found to replicate closely the observed speed profiles obtained from the fixed detector station. Simple t-tests suggest that the means of the smartphone speeds for the unadjusted case differed significantly from the means obtained from the radar detector, when the smartphone estimates were adjusted for uncertainty (CEP range related), however, the difference in mean speeds between the smartphone probes and the radar detector profile was not found to be statistically significant.

Treating uncertainty in the estimation of speed from smartphone traffic probes

Road network management under critical conditions is achievable by adopting technologies that trace vehicles and capture unsafety events to provide users with real time traffic information. Most common approaches used to acquire vehicle tracking data are based on video image processing algorithms and satellite navigation systems. However, many studies are increasingly focused on the emerging smartphone technologies for tracking vehicles. The aim of this study is to present a procedure for acquiring vehicle tracking data from smartphone sensors, supporting managers of transportation systems to take effective decisions on their networks, especially in conjunction with special events and/or critical road safety issues.

Using Smartphones as a Tool to Capture Road Traffic Attributes

Big Data are having a great impact on the cities morphology and urban planning; actually, big data are used, for example, to predict the needs of the public transport system, by targeting investment and mobility policies within the main metropolis. Based on this assumption, this paper presents a Decision Support System (DSS) framework aimed at proposing travel strategies alternative to car use by elaborating a large amount of transportation systems data coming from different devices. The paper focalizes on the use of Big Data in public transportation and introduces a methodological framework for collecting, integrating, aggregating, fusing, managing and disseminating data coming from different sources. Data mining approaches are applied to allow the analysis of both open Big Data, that conforms to established standards such as SIRI and GTFS, and unstructured freely available Big Data. Experimental data was collected from both a sample of smartphones and AVL systems in a case study area. The application of the proposed DSS allowed analyzing spatial and temporal coverage of the public transport services, and suggesting policies to improve the modal split.

Daniele Rogano

Papers

Evaluating the Accuracy of Vehicle Tracking Data Obtained from Unmanned Aerial Vehicles

Treating uncertainty in the estimation of speed from smartphone traffic probes

Using Smartphones as a Tool to Capture Road Traffic Attributes

Big data for public transportation: A DSS framework

A Decision Support System based on smartphone probes as a tool to promote public transport