A Fuzzy set-based method to identify the car position in a road lane at intersections by smartphone GPS data

Abstract Intelligent transportation systems (ITS) work by collections of data in real time. Average speed, travel time and delay at intersections are some of the most important measures, often used for monitoring the performance of transportation systems, and useful for system management and planning. In urban transportation planning, intersections are usually considered critical points, acting as bottlenecks and clog points for urban traffic. Thus, detecting the travel time at intersections in different turning directions is an activity useful to improve the urban transport efficiency. Smartphones represent a low-cost technology, with which is possible to obtain information about traffic state. However, smartphone GPS data suffer for low precision, mainly in urban areas. In this paper, we present a fuzzy set-based method for car positioning identification within road lanes near intersections using GPS data coming from smartphones. We have introduced the fuzzy sets to take into account uncertainty embedded in GPS data when trying to identify the position of cars within the road lanes. Moreover, we introduced a Genetic Algorithm to calibrate the fuzzy parameters in order to obtain a novel supervised clustering technique. We applied the proposed method to one intersection in the urban road network of Bari (Italy). First results reveal the effectiveness of the proposed methodology when comparing the outcomes of the proposed method with two well-known clustering techniques (Fuzzy C-means, K-means)

Satellite Navigation for the Age of Autonomy

Global Navigation Satellite Systems (GNSS) brought navigation to the masses. Coupled with smartphones, the blue dot in the palm of our hands has forever changed the way we interact with the world. Looking forward, cyber-physical systems such as self-driving cars and aerial mobility are pushing the limits of what localization technologies including GNSS can provide. This autonomous revolution requires a solution that supports safety-critical operation, centimeter positioning, and cyber-security for millions of users. To meet these demands, we propose a navigation service from Low Earth Orbiting (LEO) satellites which deliver precision in-part through faster motion, higher power signals for added robustness to interference, constellation autonomous integrity monitoring for integrity, and encryption / authentication for resistance to spoofing attacks. This paradigm is enabled by the 'New Space' movement, where highly capable satellites and components are now built on assembly lines and launch costs have decreased by more than tenfold. Such a ubiquitous positioning service enables a consistent and secure standard where trustworthy information can be validated and shared, extending the electronic horizon from sensor line of sight to an entire city. This enables the situational awareness needed for true safe operation to support autonomy at scale.Comment: 11 pages, 8 figures, 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS

A stake-out prototype system based on GNSS-RTK technology for implementing accurate vehicle reliability and performance tests

none4noThere are many car tests regulated by European and international standards and carried out on tracks to assess vehicle performance. The test preparation phase usually consists of placing road cones on the track with a specific configuration defined by the considered standard; this phase is performed by human operators using imprecise and slow methods, mainly due to the huge required distances. In this paper, a new geolocation stake-out system based on GNSS RTK technology has been realized and tested, supported by a Matlab-based software application to allow the user to quickly and precisely locate the on-track points on which to position the road cones. The realized stake-out system, innovative and very simple to use, produces negligible average errors (i.e. 2.4-2.9 cm) on the distance between the staked-out points according to the reference standards (distance percentage error 0.29-0.47%). Furthermore, the measured average angular error is also very low, in the range 0.04-0.18°. Finally, ISO 3888-1 and ISO 3888-2 test configurations were re-produced on the proving ground of the Porsche Technical Center by utilizing the realized stake-out system to perform a double lane-change manoeuvre on car prototypes.Special Issue "Electronic Systems and Energy Harvesting Methods for Automation, Mechatronics and Automotive 2021" Article Number: 4885openP. Visconti; F. Iaia, R. de Fazio, I. GiannoccaroVisconti, P.; Iaia, F.; de Fazio, R.; Giannoccaro, I

Leveraging a Publish/Subscribe Fog System to Provide Collision Warnings in Vehicular Networks

[EN] Fog computing, an extension of the Cloud Computing paradigm where routers themselves may provide the virtualisation infrastructure, aims at achieving fluidity when distributing in-network functions, in addition to allowing fast and scalable processing, and exchange of information. In this paper we present a fog computing architecture based on a content island which interconnects sets of things to exchange and process data among themselves or with other content islands. We then present a use case that focuses on a smartphone-based forward collision warning application for a connected vehicle scenario. This application makes use of the optical sensor of smartphones to estimate the distance between the device itself and other vehicles in its field of view. The vehicle travelling directly ahead is identified relying on the information from the GPS, camera, and inter-island communication. Warnings are generated at both content islands, if the driver does not maintain a predefined safe distance towards the vehicle ahead. Experiments performed with the application show that with the developed method, we are able to estimate the distance between vehicles, and the inter-island communication has a very low overhead, resulting in improved performance. On comparing our proposed solution based on edge/fog computing with a cloud-based api, it was observed that our solution outperformed the cloud-based api, thus making us optimistic of the utility of the proposed architectureThis work was partially funding by the Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018 , Spain, under Grant RTI2018-096384-B-I00Patra, S.; Manzoni, P.; Tavares De Araujo Cesariny Calafate, CM.; Zamora-Mero, WJ.; Cano, J. (2019). Leveraging a Publish/Subscribe Fog System to Provide Collision Warnings in Vehicular Networks. Sensors. 19(18):1-22. https://doi.org/10.3390/s19183852S1221918Vaquero, L. M., & Rodero-Merino, L. (2014). Finding your Way in the Fog. ACM SIGCOMM Computer Communication Review, 44(5), 27-32. doi:10.1145/2677046.2677052MQTT Version 3.1.1 http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/os/mqtt-v3.1.1-os.docSultana, T., & Wahid, K. A. (2019). Choice of Application Layer Protocols for Next Generation Video Surveillance Using Internet of Video Things. IEEE Access, 7, 41607-41624. doi:10.1109/access.2019.2907525Mehmood, F., Ullah, I., Ahmad, S., & Kim, D. (2019). Object detection mechanism based on deep learning algorithm using embedded IoT devices for smart home appliances control in CoT. Journal of Ambient Intelligence and Humanized Computing. doi:10.1007/s12652-019-01272-8https://tools.ietf.org/html/rfc2616https://tools.ietf.org/html/rfc7252Volvo Official Website https://www.volvocars.com/Chang, B. R., Tsai, H. F., & Young, C.-P. (2010). Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing. Expert Systems with Applications, 37(3), 2439-2450. doi:10.1016/j.eswa.2009.07.036Tan, H.-S., & Huang, J. (2006). DGPS-Based Vehicle-to-Vehicle Cooperative Collision Warning: Engineering Feasibility Viewpoints. IEEE Transactions on Intelligent Transportation Systems, 7(4), 415-428. doi:10.1109/tits.2006.883938Gelernter, D. (1985). Generative communication in Linda. ACM Transactions on Programming Languages and Systems, 7(1), 80-112. doi:10.1145/2363.2433Raspberry Pi Official Website https://www.raspberrypi.org/https://tools.ietf.org/html/rfc768Wallace, G. K. (1991). The JPEG still picture compression standard. Communications of the ACM, 34(4), 30-44. doi:10.1145/103085.103089Sauvola, J., & Pietikäinen, M. (2000). Adaptive document image binarization. Pattern Recognition, 33(2), 225-236. doi:10.1016/s0031-3203(99)00055-2Road Safety Authority of Ireland Suggest the Use of Two Second Rule http://www.rotr.ie/Rules_of_the_road.pdfOpenALPR Cloud-API Website https://www.openalpr.com/cloud-api.htmlPatra, S., Calafate, C. T., Cano, J.-C., & Manzoni, P. (2015). An ITS solution providing real-time visual overtaking assistance using smartphones. 2015 IEEE 40th Conference on Local Computer Networks (LCN). doi:10.1109/lcn.2015.736632

Vehicular Networks and Outdoor Pedestrian Localization

This thesis focuses on vehicular networks and outdoor pedestrian localization. In particular, it targets secure positioning in vehicular networks and pedestrian localization for safety services in outdoor environments. The former research topic must cope with three major challenges, concerning users’ privacy, computational costs of security and the system trust on user correctness. This thesis addresses those issues by proposing a new lightweight privacy-preserving framework for continuous tracking of vehicles. The proposed solution is evaluated in both dense and sparse vehicular settings through simulation and experiments in real-world testbeds. In addition, this thesis explores the benefit given by the use of low frequency bands for the transmission of control messages in vehicular networks. The latter topic is motivated by a significant number of traffic accidents with pedestrians distracted by their smartphones. This thesis proposes two different localization solutions specifically for pedestrian safety: a GPS-based approach and a shoe-mounted inertial sensor method. The GPS-based solution is more suitable for rural and suburban areas while it is not applicable in dense urban environments, due to large positioning errors. Instead the inertial sensor approach overcomes the limitations of previous technique in urban environments. Indeed, by exploiting accelerometer data, this architecture is able to precisely detect the transitions from safe to potentially unsafe walking locations without the need of any absolute positioning systems

Road Surface Feature Extraction and Reconstruction of Laser Point Clouds for Urban Environment

Automakers are developing end-to-end three-dimensional (3D) mapping system for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles (AVs). Using geomatics, artificial intelligence, and SLAM (Simultaneous Localization and Mapping) systems to handle all stages of map creation, sensor calibration and alignment. It is crucial to have a system highly accurate and efficient as it is an essential part of vehicle controls. Such mapping requires significant resources to acquire geographic information (GIS and GPS), optical laser and radar spectroscopy, Lidar, and 3D modeling applications in order to extract roadway features (e.g., lane markings, traffic signs, road-edges) detailed enough to construct a “base map”. To keep this map current, it is necessary to update changes due to occurring events such as construction changes, traffic patterns, or growth of vegetation. The information of the road play a very important factor in road traffic safety and it is essential for for guiding autonomous vehicles (AVs), and prediction of upcoming road situations within AVs. The data size of the map is extensive due to the level of information provided with different sensor modalities for that reason a data optimization and extraction from three-dimensional (3D) mobile laser scanning (MLS) point clouds is presented in this thesis. The research shows the proposed hybrid filter configuration together with the dynamic developed mechanism provides significant reduction of the point cloud data with reduced computational or size constraints. The results obtained in this work are proven by a real-world system

Integrating vehicle specific power methodology and microsimulation in estimating emissions on urban roundabouts

In this study pollutant emissions were estimated from VSP modal emission rates and the distribution of time spent in each VSP mode obtained from the speed profiles both gathered in the field and simulated in AIMSUN at a sample of urban roundabouts. The versatility of the micro-simulation model for a calibration aimed at improving accuracy of emissions estimates was tested in order to ensure that second-by-second trajectories experienced in the field by a test vehicle through the sampled roundabouts properly reflected the simulated speed profiles. The first results which the thesis will refer, confirmed the feasibility of the smart approach that integrates the use of field-observed and simulated data to estimate emissions at urban roundabouts. It is also revealed friendly in collecting information via smartphone and in the subsequent data analysis and provided suggestions for large-scale data collection through a digital community. Another goal of this research is to investigate about the environmental performance after a conversion of a traditional existing roundabout into a turbo-roundabout. This aspect has been considered a positive approach for a novel attitude in the performance evaluation of road networks to align the infrastructural design with the aim of sustainable and low-emission mobility. The main finding provided from this study is referred to the positive potential of a novel attitude in the conceptualization and performance evaluation of road units in order to align urban infrastructural projects with the worldwide shared long-term ambitions for a low-emission mobility