Showing papers by "Vassilis Gikas published in 2021"

Monitoring ground deformation of cultural heritage sites using SAR and geodetic techniques: the case study of Choirokoitia, Cyprus

Abstract: Nowadays, assessing geo-hazards in cultural heritage sites in most cases takes place after the hazard has occurred. Monitoring structural and ground deformation resulting from geo-hazards facilitates the early recognition of potential risks and encourages effective conservation planning. This paper presents an integrated ground deformation monitoring approach based on the combined use of satellite SAR data, campaign-based GPS/GNSS observations, and aerial images from UAVs within the Choirokoitia UNESCO World Heritage Site in Cyprus. The Neolithic settlement of Choirokoitia is one of the most important prehistoric sites in the Eastern Mediterranean. The site is located on a steep hill, which makes it vulnerable to rock falls and landslides. As part of the PROTHEGO project, a series of field measurements were collected at the Choirokoitia site and compared against satellite SAR data to verify kinematic behavior of the broader area and to assist in monitoring potential geo-hazards over time. The results obtained indicate displacement rates of the order of 0.03 m/year. These results indicate that ground deformation should be monitored in the area surrounding the Choirokoitia using long-term, low-impact monitoring systems such as SAR images and UAV-based and geodetic techniques. The combination of such monitoring technologies can be compared to monitor and assess potential geo-hazards on archeological sites with increased accuracy.

Classification of pedestrian behavior using real trajectory data

Abstract: Pedestrian behavior is influenced by various factors and is thus characterized by heterogeneity. The aim of this research is to explore the impact of various parameters, such as pedestrians’ characteristics, mobile phone use and walking pace on pedestrians’ behavior. Classification of pedestrian behavior contributes into understanding how different factors affect pedestrian behavior and allows a finer perception of pedestrian movement, as it helps us distinguish and interpret the way that pedestrians react to different situations. In this research a simple methodology for pedestrian behavior classification is proposed taking into account pedestrians’ heterogeneity. The methodology employs random forest algorithms in order to analyze and classify trajectory data, as well as to estimate the pedestrian behavior state in real time. The analysis utilizes real pedestrian trajectory data collected via an experiment conducted in the Campus of National Technical University of Athens under various conditions (normal and fast pace, distraction from mobile phone). Distributions of these data are explored and a clustering analysis follows, yielding satisfactory results.

A case study of pedestrian positioning with uwb and uav cameras

Abstract: . The development and availability on consumer devices of the global navigation satellite systems (GNSS) have dramatically changed the everyday-life of most of the human population, enabling real time navigation on almost any smart device produced in the last years. However, such strong dependence on the availability of the GNSS limits the further spread of location based services to the areas where GNSS is not available or reliable. Motivated by these considerations, several research groups recently considered the problem of developing alternative positioning systems able to compensate the unavailability of GNSS in certain areas. Similarly, this paper also investigates the performance of certain alternative methods, which aim at partially substitute GNSS. In particular, the positioning performance of an affordable Ultra Wide-Band system is compared with that of a vision approach, based on the use of visual information acquired by an Unmanned Aerial Vehicle (UAV) flying over the area of interest. In accordance with the results obtained in our dataset, the drone-based vision system usually allow to obtain better positioning results when the pedestrians are visible in the drone video frames (median 2D positioning error less than 25 cm). Nevertheless, the combination of such strategies shall also be investigated in order to obtain a more robust positioning system.

Addressing the Potential of GNSS Moving Base Station Technique for Vehicular C-ITS Applications: Preliminary Tests and Results

Abstract: Effective transportation of people and goods requires an efficient positioning service. This service currently relies to a certain extent on Global Navigation Satellite Systems (GNSS). However, the problems associated with obtaining an accurate and reliable position solution in challenging environments are often underestimated and need to be dealt in greater detail. This paper discusses the concept of GNSS moving baseline station in critical ITS applications and provides preliminary results from the testing of the technique in variant observation scenarios.

Uav uwb positioning close to building facades: a case study

Abstract: . Nowadays, Unmanned Aerial Vehicles represent a very popular tool used in dramatically wide range of applications: indeed, their high flexibility, ease of use, and in certain cases quite affordable price make them a very attractive solutions in a number of applications, including surveying and mapping. Despite such a wide range of uses, their usage in automatic/autonomous mode is still restricted by the requirement of the availability of a reliable positioning and navigation system, which in practically all the commercial solutions is represented by the Global Navigation Satellite System (GNSS). Unfortunately, the availability and reliability of GNSS cannot be ensured in all the working conditions of interest. In particular, such condition may not hold downtown, close to high buildings. Since this can also be an operative condition of wide interest, this paper aims at investigating the use of an alternative positioning method that can be integrated with GNSS in order to compensate its unavailability. To be more specific, this paper investigates the positioning performance of an Ultra Wide-Band (UWB) system when an UWB rover is attached to a drone flying close to a building facade, whereas a set of UWB anchors are on the ground, close to the facade. The results obtained in the case study of a building of the University of Padua show that the UWB system positioning performance is quite good (quite less than 1 meter error for most of the time) up to approximately 15–20 meters of distance from the anchors. Close to the top of the building the error significantly increases when using an Extended Kalman filter (EKF) positioning approach, probably mostly due to the low UWB measurement success rate at such heights and to the poor geometric configuration of the UWB network. Nevertheless, a Gauss-Newton-based positioning strategy outperforms the EKF in such critical case, still ensuring errors at 1 meter level.

Development of a prototype, self-trained truck driver coaching system based on geolocation, IoT and BI: Preliminary results and analyses

Abstract: The volume of freight transport on roads has grown steadily over the years and is expected to increase significantly over the next decade. Truck driver coaching systems have proved a key element in boosting efficiency, profitability and safety of the haulage sector whilst contributing to infrastructure asset management objectives. Notwithstanding new, commercial driver coaching systems have shown up recently in the market, still they operate as "black boxes", whilst their functionality and automation level is very limited. Given the enormous numbers of truck fleets currently operating worldwide, the necessity for an adaptable driver coaching system, capable of providing real-time functionality is evident. This study reports the design characteristics and first results from PEGASUS research project aiming at developing a self-trained, unified, driver-coaching system based on geolocation, IoT (Internet of Things) and BI (Business Intelligence) techniques. The system by design aims at reducing operational costs for the truck fleet, improving road safety, as well as contributing to green transportation. In effect, the system has been designed to produce user-friendly, two-way advice to lorry drivers tailored to the road conditions (e.g., geometry, road markings, neighboring vehicles) and operational (e.g., weather, traffic) conditions in real time and in a dynamic manner.

Design and Testing of a Fuel Consumption Eco-Driving Coach System for Truck Drivers based on Geolocation and BI Technologies

Abstract: Minimizing fuel consumption while maximizing driving performance (i.e., less travel time, increased safety and less gas emissions) is a key factor for the successful implementation of any transport system. In this regard, monitoring driving behaviour and converting this information into real-time advice to drivers can benefit seriously the performance of transport and mobility services. This paper presents the design and implementation of a prototype system and a BI (Business Intelligence) model for fuel consumption optimization for heavy-duty vehicles. The system consists of an OBDH prototype, various external sensors (including GNSS/INS and environmental ones), and an on-purpose built FMS (Fleet Management System) bus logger. The paper revisits the concept of BI and attempts knowledge transfer from the field of data analytics for business information to a pure engineering problem. Preliminary analyses using real truck trajectory data reveals and quantifies the effect of driving behaviour on fuel consumption while it indicates the potential of the system to transform into an integrated, self-trained driver coaching system.