Search results for: unmanned autonomous air vehicle

Authors: Mohamed Gazzeh, Bradley Null, Fethi Tlili, Hichem Besbes

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The burgeoning global ownership of personal vehicles has posed a significant strain on urban infrastructure, notably parking facilities, leading to traffic congestion and environmental concerns. Effective parking management systems (PMS) are indispensable for optimizing urban traffic flow and reducing emissions. The most commonly deployed systems nowadays rely on computer vision technology. This paper explores the integration of radar sensors and simulation in the context of smart parking management. We concentrate on radar sensors due to their versatility and utility in automotive applications, which extends to PMS. Additionally, radar sensors play a crucial role in driver assistance systems and autonomous vehicle development. However, the resource-intensive nature of radar data collection for algorithm development and testing necessitates innovative solutions. Simulation, particularly the monoDrive simulator, an internal development tool used by NI the Test and Measurement division of Emerson, offers a practical means to overcome this challenge. The primary objectives of this study encompass simulating radar sensors to generate a substantial dataset for algorithm development, testing, and, critically, assessing the transferability of models between simulated and real radar data. We focus on occupancy detection in parking as a practical use case, categorizing each parking space as vacant or occupied. The simulation approach using monoDrive enables algorithm validation and reliability assessment for virtual radar sensors. It meticulously designed various parking scenarios, involving manual measurements of parking spot coordinates, orientations, and the utilization of TI AWR1843 radar. To create a diverse dataset, we generated 4950 scenarios, comprising a total of 455,400 parking spots. This extensive dataset encompasses radar configuration details, ground truth occupancy information, radar detections, and associated object attributes such as range, azimuth, elevation, radar cross-section, and velocity data. The paper also addresses the intricacies and challenges of real-world radar data collection, highlighting the advantages of simulation in producing radar data for parking lot applications. We developed classification models based on Support Vector Machines (SVM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), exclusively trained and evaluated on simulated data. Subsequently, we applied these models to real-world data, comparing their performance against the monoDrive dataset. The study demonstrates the feasibility of transferring models from a simulated environment to real-world applications, achieving an impressive accuracy score of 92% using only one radar sensor. This finding underscores the potential of radar sensors and simulation in the development of smart parking management systems, offering significant benefits for improving urban mobility and reducing environmental impact. The integration of radar sensors and simulation represents a promising avenue for enhancing smart parking management systems, addressing the challenges posed by the exponential growth in personal vehicle ownership. This research contributes valuable insights into the practicality of using simulated radar data in real-world applications and underscores the role of radar technology in advancing urban sustainability.

Keywords: autonomous vehicle simulator, FMCW radar sensors, occupancy detection, smart parking management, transferability of models

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Authors: Pradeep Jawale, Sharad Supare, Sachin Kumar Jain, Nagesh Walke

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One can witness the constant development and redevelopment of cities throughout the world. Construction equipment vehicles (CEVs) are commonly used on the construction site. However, noise pollution from construction sites due to the use of CEV has become a major problem for many cities. The construction equipment employed, which includes excavators and bulldozers, is one of the main causes of these elevated noise levels. The construction workers possibly will face a potential risk to their auditory health and well-being due to the noise levels they are exposed to. Different countries have imposed exterior and operator noise limits for construction equipment vehicles, enabling them to control noise pollution from CEVs. In this study, the operator ear level noise of the identified vehicle is higher than the benchmark vehicle by 8 dB(A). It was a tough time for the NVH engineer to beat the interior noise level of the benchmark vehicle. Initially, the noise source identification technique was used to identify the dominant sources for increasing the interior noise of the test vehicle. It was observed that the transfer of structure-borne and air-borne noise to the cabin was the major issue with the vehicle. It was foremost required to address the issue without compromising the overall performance of the vehicle. Surprisingly, the steering pump and radiator fan were identified as the major dominant sources than typical conventional sources like powertrain, intake, and exhaust. Individual sources of noise were analyzed in detail, and optimizations were made to minimize the noise at the source. As a result, the significant noise reduction achieved inside the vehicle and the overall in-cab noise level for the vehicle became a new benchmark in the market.

Keywords: interior noise, noise reduction, CEV, noise source identification

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Authors: B. Kriheli, E. Levner, T. C. E. Cheng, C. T. Ng

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During the last decades, research interest in planning, scheduling, and control of emergency response operations, especially people rescue and evacuation from the dangerous zone of marine accidents, has increased dramatically. Until the survivors (called ‘targets’) are found and saved, it may cause loss or damage whose extent depends on the location of the targets and the search duration. The problem is to efficiently search for and detect/rescue the targets as soon as possible with the help of intelligent mobile robots so as to maximize the number of saved people and/or minimize the search cost under restrictions on the amount of saved people within the allowable response time. We consider a special situation when the autonomous mobile robots (AMR), e.g., unmanned aerial vehicles and remote-controlled robo-ships have no operator on board as they are guided and completely controlled by on-board sensors and computer programs. We construct a mathematical model for the search process in an uncertain environment and provide a new fast algorithm for scheduling the activities of the autonomous robots during the search-and rescue missions after an accident at sea. We presume that in the unknown environments, the AMR’s search-and-rescue activity is subject to two types of error: (i) a 'false-negative' detection error where a target object is not discovered (‘overlooked') by the AMR’s sensors in spite that the AMR is in a close neighborhood of the latter and (ii) a 'false-positive' detection error, also known as ‘a false alarm’, in which a clean place or area is wrongly classified by the AMR’s sensors as a correct target. As the general resource-constrained discrete search problem is NP-hard, we restrict our study to finding local-optimal strategies. A specificity of the considered operational research problem in comparison with the traditional Kadane-De Groot-Stone search models is that in our model the probability of the successful search outcome depends not only on cost/time/probability parameters assigned to each individual location but, as well, on parameters characterizing the entire history of (unsuccessful) search before selecting any next location. We provide a fast approximation algorithm for finding the AMR route adopting a greedy search strategy in which, in each step, the on-board computer computes a current search effectiveness value for each location in the zone and sequentially searches for a location with the highest search effectiveness value. Extensive experiments with random and real-life data provide strong evidence in favor of the suggested operations research model and corresponding algorithm.

Keywords: disaster management, intelligent robots, scheduling algorithm, search-and-rescue at sea

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Authors: Josua K. Junias, Fillemon N. Nangolo, Petrina T. Johaness

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The deterioration of pavement can lead to the formation of potholes, which cause the wheels of a vehicle to experience unusual and uneven movement. In addition, improper loading practices of heavy vehicles can result in dynamic loading of the pavement due to the vehicle's response to the irregular movement caused by the potholes. Previous studies have only focused on the effects of either the road's uneven surface or the asymmetrical loading of the vehicle, but not both. This study aimed to model the pavement's dynamic response to heavy vehicles under different loading configurations and wheel movements. A sample of 225 cases with symmetrical and asymmetrical loading and kinematic movements was used, and 27 validated 3D pavement-vehicle interactive models were developed using SIMWISE 4D. The study found that the type of kinematic movement experienced by the heavy vehicle affects the pavement's dynamic loading, with eccentrically loaded, asymmetrically kinematic heavy vehicles having a statistically significant impact. The study also suggests that the mass of the vehicle's suspension system plays a role in the pavement's dynamic loading.

Keywords: eccentricities, pavement dynamic loading, vertical displacement dynamic response, heavy vehicles

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Authors: M. Adamski, J. Cwiklak

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The contemporary battlefield creates a demand for more costly and highly advanced munitions. Training personnel responsible for operations, as well as an immediate execution of combat tasks, which engage real assets, is unrealistic and economically not feasible. Owing to a wide array of exploited simulators and various types of imitators, it is possible to reduce the costs. One of the effective elements of training, which can be applied in the training of all service branches, are imitators of aerial targets. This research serves as an introduction to the commencement of design analysis over a real aerial target imitator. Within the project, the basic aerodynamic calculations were made, which enabled to determine its geometry, design layout, performance, as well as the mass balance of individual components. The conducted calculations of the parameters of flight characteristics come closer to the real performance of such unmanned aerial vehicles.

Keywords: aerial target, aerodynamics, imitator, performance

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Authors: Dustin Schöder

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The potential benefits of the utilization of highly automated and autonomous trucks in logistics operations are the subject of interest to the entire logistics industry. The benefits of the use of these new technologies were scientifically investigated and implemented in roadmaps. So far, reliable data and experiences from real life use cases are still limited. A German research consortium of both academics and industry developed a highly automated (SAE level 4) vehicle for yard operations at railports and container depots. After development and testing, a several month field test at the DUSS Terminal in Ulm-Dornstadt (Germany) and the nearby DB Intermodal Services Container Depot in Ulm-Dornstadt was conducted. The truck was piloted in a shuttle service between both sites. In a holistic automation approach, the vehicle was integrated into a digital communication platform so that the truck could move autonomously without a driver and his manual interactions with a wide variety of stakeholders. The main goal is to investigate the effects of highly automated trucks in the key processes of container loading, unloading and container relocation on holistic railport yard operation. The field test data were used to investigate changes in process efficiency of key processes of railport and container yard operations. Moreover, effects on the capacity utilization and potentials for smothering peak workloads were analyzed. The results state that process efficiency in the piloted use case was significantly higher. The reason for that could be found in the digitalized data exchange and automated dispatch. However, the field test has shown that the effect is greatly varying depending on the ratio of highly automated and manual trucks in the yard as well as on the congestion level in the loading area. Furthermore, the data confirmed that under the right conditions, the capacity utilization of highly automated trucks could be increased. In regard to the potential for smothering peak workloads, no significant findings could be made based on the limited requirements and regulations of railway operation in Germany. In addition, an empirical survey among railport managers, operational supervisors, innovation managers and strategists (n=15) within the logistics industry in Germany was conducted. The goal was to identify key characteristics of future railports and terminals as well as requirements that railports will have to meet in the future. Furthermore, the railport processes where automation and autonomization make the greatest impact, as well as hurdles and challenges in the introduction of new technologies, have been surveyed. Hence, further potential use cases of highly automated and autonomous applications could be identified, and expectations have been mapped. As a result, a highly detailed and practice-based roadmap towards a ‘terminal 4.0’ was developed.

Keywords: highly automated driving, autonomous driving, SAE level 4, railport operations, container depot, intermodal logistics, potentials of autonomization

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Authors: Shoeb Ahmed Adeel, Vivek Paul, K. Prajwal, Michael Fenelon

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Techniques have been used to tether and stabilize a multi-rotor MAV but carrying out the same process to a fixed wing MAV is a novel method which can be utilized in order to reduce damage occurring to the fixed wing MAVs while conducting flight test trials and PID tuning. A few sensors and on board controller is required to carry out this experiment in horizontal and vertical plane of the vehicle. Here we will be discussing issues such as sensitivity of the air vehicle, endurance and external load of the string acting on the vehicle.

Keywords: MAV, PID tuning, tethered flight, UAV

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Authors: Miguel Arlenzo Duran Sarmiento, Luis Alfonso Del Portillo Valdés, Carlos Borras Pinilla

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Brake energy regeneration systems have the capacity to transform part of the vehicle's kinetic energy during deceleration into useful energy. These systems can be implemented in hybrid vehicles, which can be electric or hydraulic in type, and contribute to reducing the energy required to propel the vehicle thanks to the accumulation of energy. This paper presents the modeling and simulation of a braking energy regeneration system applied in hydraulic hybrid vehicles configured in parallel, the modeling and simulation were performed in Simulink of Matlab, where a performance comparison of the regenerated torque as a function of vehicle load, the displacement of the hydraulic regeneration device and the vehicle speed profile. The speed profiles used in the simulation are standard profiles such as the NEDC and WLTP profiles. The vehicle loads range from 1500 kg to 12000 kg. The results show the comparison of the torque required by the vehicle, the torque regenerated by the system subjected to the different speed and load conditions.

Keywords: braking energy, energy regeneration, hybrid vehicles, kinetic energy, torque

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Authors: Prabhsharan Singh, Rahul Sindhu, Piyush Sikka

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The steering system is an integral part and medium through which the driver communicates with the vehicle and terrain, hence the most suitable steering geometry as per requirements must be chosen. The function of the chosen steering geometry of an All-Terrain Vehicle (ATV) is to provide the desired understeer gradient, minimum tire slippage, expected weight transfer during turning as these are requirements for a good steering geometry of a BAJA ATV. This research paper focuses on choosing the best suitable steering geometry for BAJA ATV tracks by reasoning the working principle and using fundamental trigonometric functions for obtaining these geometries on the same vehicle itself, namely Ackermann, Anti- Ackermann, Parallel Ackermann. Full vehicle analysis was carried out on Adams Car Analysis software, and graphical results were obtained for various parameters. Steering geometries were achieved by using a single versatile knuckle for frontward and rearward tie-rod placement and were practically tested with the help of data acquisition systems set up on the ATV. Each was having certain characteristics, setup, and parameters were observed for the BAJA ATV, and correlations were created between analytical and practical values.

Keywords: all-terrain vehicle, Ackermann, Adams car, Baja Sae, steering geometry, steering system, tire slip, traction, understeer gradient

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Authors: Lydia Wahid, Mona F. Ahmed, Nevin Darwish

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The vehicle routing problem (VRP) consists of a group of customers that needs to be served. Each customer has a certain demand of goods. A central depot having a fleet of vehicles is responsible for supplying the customers with their demands. The problem is composed of two subproblems: The first subproblem is an assignment problem where the number of vehicles that will be used as well as the customers assigned to each vehicle are determined. The second subproblem is the routing problem in which for each vehicle having a number of customers assigned to it, the order of visits of the customers is determined. Optimal number of vehicles, as well as optimal total distance, should be achieved. In this paper, an approach for solving the first subproblem (the assignment problem) is presented. In the approach, a clustering algorithm is proposed for finding the optimal number of vehicles by grouping the customers into clusters where each cluster is visited by one vehicle. Finding the optimal number of clusters is NP-hard. This work presents a polynomial time clustering algorithm for finding the optimal number of clusters and solving the assignment problem.

Keywords: vehicle routing problems, clustering algorithms, Clarke and Wright Saving Method, agglomerative hierarchical clustering

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Wael Ata

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Tracked vehicles are exposed to severe operating conditions during their battlefield. The suspension system of such vehicles plays a crucial role in the mitigation of vibration transmitted from unevenness to vehicle hull and consequently to the crew. When the vehicles are crossing the road with high speeds, the driver is subjected to a high magnitude of vibration dose. This is because of the passive suspension system of the tracked vehicle lack the effectiveness to withstand induced vibration from irregular terrains. This paper presents vibration control of a semi-active seat suspension incorporating Magnetorheological (MR) damper fitted to a driver seat of an amphibious tracked vehicle (BMP-1). A half vehicle model featuring the proposed semi-active seat suspension is developed and its governing equations are derived. Two controllers namely; skyhook and fuzzy logic skyhook based to suppress the vibration dose at driver’s seat are formulated. The results show that the controlled MR suspension seat along with the vehicle model has substantially suppressed vibration levels at the driver’s seat under bump and sinusoidal excitations

Keywords: Tracked Vehicles, MR dampers, Skyhook controller, fuzzy logic controller

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Authors: Shiming Li, Qingwang Liu, Honggan Wu, Jianbing Zhang

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Plantation forests are useful for timber production, recreation, environmental protection and social development. Stands height is an important parameter for the estimation of forest volume and carbon stocks. Although lidar is suitable technology for the vertical parameters extraction of forests, but high costs make it not suitable for operational inventory. With the development of computer vision and photogrammetry, aerial photos from unmanned aerial system can be used as an alternative solution for height mapping. Structure-from-motion (SfM) photogrammetry technique can be used to extract DSM and DEM information. Canopy height model (CHM) can be achieved by subtraction DEM from DSM. Our result shows that overlapping aerial photos is a potential solution for plantation forests height mapping.

Keywords: forest height mapping, plantation forests, structure-from-motion photogrammetry, UAS

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Authors: Qiaosi Li, Frankie Kwan Kit Wong, Tung Fung

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Lightweight unmanned aerial vehicle (UAV) loading with novel sensors offers a low cost approach for data acquisition in complex environment. This study established a framework for applying UAV system in complex environment quick mapping and assessed the performance of UAV-based hyperspectral image and digital surface model (DSM) derived from photogrammetric point clouds for 13 species classification in wetland area Mai Po Inner Deep Bay Ramsar Site, Hong Kong. The study area was part of shallow bay with flat terrain and the major species including reedbed and four mangroves: Kandelia obovata, Aegiceras corniculatum, Acrostichum auerum and Acanthus ilicifolius. Other species involved in various graminaceous plants, tarbor, shrub and invasive species Mikania micrantha. In particular, invasive species climbed up to the mangrove canopy caused damage and morphology change which might increase species distinguishing difficulty. Hyperspectral images were acquired by Headwall Nano sensor with spectral range from 400nm to 1000nm and 0.06m spatial resolution image. A sequence of multi-view RGB images was captured with 0.02m spatial resolution and 75% overlap. Hyperspectral image was corrected for radiative and geometric distortion while high resolution RGB images were matched to generate maximum dense point clouds. Furtherly, a 5 cm grid digital surface model (DSM) was derived from dense point clouds. Multiple feature reduction methods were compared to identify the efficient method and to explore the significant spectral bands in distinguishing different species. Examined methods including stepwise discriminant analysis (DA), support vector machine (SVM) and minimum noise fraction (MNF) transformation. Subsequently, spectral subsets composed of the first 20 most importance bands extracted by SVM, DA and MNF, and multi-source subsets adding extra DSM to 20 spectrum bands were served as input in maximum likelihood classifier (MLC) and SVM classifier to compare the classification result. Classification results showed that feature reduction methods from best to worst are MNF transformation, DA and SVM. MNF transformation accuracy was even higher than all bands input result. Selected bands frequently laid along the green peak, red edge and near infrared. Additionally, DA found that chlorophyll absorption red band and yellow band were also important for species classification. In terms of 3D data, DSM enhanced the discriminant capacity among low plants, arbor and mangrove. Meanwhile, DSM largely reduced misclassification due to the shadow effect and morphological variation of inter-species. In respect to classifier, nonparametric SVM outperformed than MLC for high dimension and multi-source data in this study. SVM classifier tended to produce higher overall accuracy and reduce scattered patches although it costs more time than MLC. The best result was obtained by combining MNF components and DSM in SVM classifier. This study offered a precision species distribution survey solution for inaccessible wetland area with low cost of time and labour. In addition, findings relevant to the positive effect of DSM as well as spectral feature identification indicated that the utility of UAV-borne hyperspectral and photogrammetry deriving 3D data is promising in further research on wetland species such as bio-parameters modelling and biological invasion monitoring.

Keywords: digital surface model (DSM), feature reduction, hyperspectral, photogrammetric point cloud, species mapping, unmanned aerial vehicle (UAV)

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Authors: Carolina Senabre, Sergio Valero, Emilio Velasco

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The brake systems of vehicles are tested periodically by a “brake tester” at Ministry of Transport (MOT) stations. This tester measures the effectiveness of vehicle. This parameter is established by the International Committee of Vehicle Inspection (CITA). In this paper, we present an investigation of the influence of the tire size on the measurements of brake force on three MOT brake testers. We performed an analysis of the vehicle braking capacity test at MOT stations. The influence of varying wheel diameter and width on the measurement of braking at MOT stations has been analyzed. Thereby, the MOT brake tester as a verification system for a vehicle has been evaluated.

Keywords: brake tester, ministry of transport facilities, wheel diameter, efficiency

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Authors: Jiayuan Wu. Lu Hu

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With the development of the sharing economy and mobile technology, carsharing becomes more popular. In this paper, we focus on the joint optimization of one-way station-based carsharing systems. We model the problem as an integer linear program with six elements: station locations, station capacity, fleet size, initial vehicle allocation, vehicle relocation, and demand selection. A greedy-based heuristic is proposed to address the model. Firstly, initialization based on the location variables relaxation using Gurobi solver is conducted. Then, according to the profit margin and demand satisfaction of each station, the number of stations is downsized iteratively. This method is applied to real data from Chengdu, Sichuan taxi data, and it’s efficient when dealing with a large scale of candidate stations. The result shows that with vehicle relocation and demand selection, the profit and demand satisfaction of carsharing systems are increased.

Keywords: one-way carsharing, location, vehicle relocation, demand selection, greedy algorithm

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Authors: Loha Hashimy, Isabella Castillo

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In today's rapidly evolving technological landscape, the importance of precise navigation and mapping systems cannot be understated. As various sectors undergo transformative changes, the market potential for Advanced Mapping and Management Systems (AMMS) emerges as a critical focus area. The Galileo/GNSS-Based Autonomous Mobile Mapping System (GAMMS) project, specifically targeted toward high-definition mapping (HDM), endeavours to provide insights into this market within the broader context of the geomatics and navigation fields. With the growing integration of Autonomous Vehicles (AVs) into our transportation systems, the relevance and demand for sophisticated mapping solutions like HDM have become increasingly pertinent. The research employed a meticulous, lean, stepwise, and interconnected methodology to ensure a comprehensive assessment. Beginning with the identification of pivotal project results, the study progressed into a systematic market screening. This was complemented by an exhaustive desk research phase that delved into existing literature, data, and trends. To ensure the holistic validity of the findings, extensive consultations were conducted. Academia and industry experts provided invaluable insights through interviews, questionnaires, and surveys. This multi-faceted approach facilitated a layered analysis, juxtaposing secondary data with primary inputs, ensuring that the conclusions were both accurate and actionable. Our investigation unearthed a plethora of drivers steering the HD maps landscape. These ranged from technological leaps, nuanced market demands, and influential economic factors to overarching socio-political shifts. The meteoric rise of Autonomous Vehicles (AVs) and the shift towards app-based transportation solutions, such as Uber, stood out as significant market pull factors. A nuanced PESTEL analysis further enriched our understanding, shedding light on political, economic, social, technological, environmental, and legal facets influencing the HD maps market trajectory. Simultaneously, potential roadblocks were identified. Notable among these were barriers related to high initial costs, concerns around data quality, and the challenges posed by a fragmented and evolving regulatory landscape. The GAMMS project serves as a beacon, illuminating the vast opportunities that lie ahead for the HD mapping sector. It underscores the indispensable role of HDM in enhancing navigation, ensuring safety, and providing pinpoint, accurate location services. As our world becomes more interconnected and reliant on technology, HD maps emerge as a linchpin, bridging gaps and enabling seamless experiences. The research findings accentuate the imperative for stakeholders across industries to recognize and harness the potential of HD mapping, especially as we stand on the cusp of a transportation revolution heralded by Autonomous Vehicles and advanced geomatic solutions.

Keywords: high-definition mapping (HDM), autonomous vehicles, PESTEL analysis, market drivers

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Authors: E. T. L. Cöuras Ford, V. A. C. Vale, J. U. L. Mendes

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Two of the objective principal in the study of the aerodynamics of vehicles are the safety and the acting. Those objectives can be reached through the development of devices modify the drainage of air about of the vehicle and also through alterations in the way of the external surfaces. The front lowest profile of the vehicle, for instance, has great influence on the coefficient of aerodynamic penetration (Cx) and later on great part of the pressure distribution along the surface of the vehicle. The objective of this work was of analyzing the aerodynamic behavior that it happens on some types the trucks of vehicles, based on experimentation in aerodynamic tunnel, seeking to determine the aerodynamic efficiency of each one of them.

Keywords: aerodynamic, vehicles, wind tunnel, safety, acting

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Authors: Fatemeh Behbahani, Rubiyah Yusof

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To avoid collision between following vehicles and vehicles in front, it is vital to keep appropriate, safe spacing between both vehicles over all speeds. Therefore, the following vehicle needs to have exact information regarding the speed and spacing between vehicles. This project is conducted to simulate the tuning of controller gain for a vehicle-following system through the selected control strategy, spacing control policy and fixed-time headway policy. In addition, the paper simulates and designs an adaptive gain controller for a road-vehicle-following system which uses information on the spacing, velocity and also acceleration of a preceding vehicle in the proposed one-vehicle look-ahead strategy. The mathematical model is implemented using Kirchhoff and Newton’s Laws, and stability simulated. The trial-error method was used to obtain a suitable value of controller gain. However, the adaptive-based controller system was able to optimize the gain value automatically. Model Reference Adaptive Control (MRAC) is designed and utilized and based on firstly the Gradient and secondly the Lyapunov approach. The Lyapunov approach considers stability. The Gradient approach was found to improve the best value of gain in the controller system with fixed-time headway.

Keywords: one-vehicle look-ahead, model reference adaptive, stability, tuning gain controller, MRAC

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Authors: Emily J. Morey, Kevin Galvin, Thomas Riley, R. Eddie Wilson

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The key challenges faced by integrating autonomous rail operations into the existing mainline railway environment have been identified through the understanding and framing of the problem space and stakeholder analysis. This was achieved through the completion of the first four steps of Soft Systems Methodology, where the problem space has been expressed via conceptual models. Having identified these challenges, we investigated one of them, namely capacity, via the use of models and simulation. This paper examines the approach used to move from the conceptual models to a simulation which can determine whether the integration of autonomous trains can plausibly increase capacity. Within this approach, we developed an architecture and converted logical models into physical resource models and associated design features which were used to build a simulator. From this simulator, we are able to analyse mixtures of legacy-autonomous operations and produce fundamental diagrams and trajectory plots to describe the dynamic behaviour of mixed mainline railway operations.

Keywords: autonomy, executable architecture, modelling and simulation, railway capacity

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Authors: Marcus Walter, Norbert Nitzsche, Dirk Odenthal, Steffen Müller

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This contribution presents a friction estimator for industrial purposes which identifies Coulomb friction in a steering system. The estimator only needs a few, usually known, steering system parameters. Friction occurs on almost every mechanical system and has a negative influence on high-precision position control. This is demonstrated on a steering angle controller for highly automated driving. In this steering system the friction induces limit cycles which cause oscillating vehicle movement when the vehicle follows a given reference trajectory. When compensating the friction with the introduced estimator, limit cycles can be suppressed. This is demonstrated by measurements in a series vehicle.

Keywords: friction estimation, friction compensation, steering system, lateral vehicle guidance

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Authors: Fan Yang, Ye-Lu Wang, Yang Zhao

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The dynamic load allowance, as an application result of the vehicle-bridge coupled vibration theory, is an important parameter for bridge design and evaluation. Based on the coupled vehicle-bridge vibration theory, the current work establishes a full girder model of a dynamic load allowance, selects a planar five-degree-of-freedom three-axis vehicle model, solves the coupled vehicle-bridge dynamic response using the APDL language in the spatial finite element program ANSYS, selects the pivot point 2 sections as the representative of the negative moment section, and analyzes the effects of parameters such as travel speed, unevenness, vehicle frequency, span diameter, span number and forced displacement of the support on the negative moment dynamic load allowance through orthogonal tests. The influence of parameters such as vehicle speed, unevenness, vehicle frequency, span diameter, span number, and forced displacement of the support on the negative moment dynamic load allowance is analyzed by orthogonal tests, and the influence law of each influencing parameter is summarized. It is found that the effects of vehicle frequency, unevenness, and speed on the negative moment dynamic load allowance are significant, among which vehicle frequency has the greatest effect on the negative moment dynamic load allowance; the effects of span number and span diameter on the negative moment dynamic load allowance are relatively small; the effects of forced displacement of the support on the negative moment dynamic load allowance are negligible.

Keywords: continuous T-girder bridge, dynamic load allowance, sensitivity analysis, vehicle-bridge coupling

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

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Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle

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Authors: Maxime A. J. Kuitche, Ruxandra M. Botez, Arthur Guillemin

Abstract:

As the Unmanned Aerial Systems have found diverse utilities in both military and civil aviation, the necessity to obtain an accurate aerodynamic model has shown an enormous growth of interest. Recent modeling techniques are procedures using optimization algorithms and statistics that require many flight tests and are therefore extremely demanding in terms of costs. This paper presents a procedure to estimate the aerodynamic behavior of an unmanned aerial system from a numerical approach using computational fluid dynamic analysis. The study was performed using an unstructured mesh obtained from a grid convergence analysis at a Mach number of 0.14, and at an angle of attack of 0°. The flow around the aircraft was described using a standard k-ω turbulence model. Thus, the Reynold Averaged Navier-Stokes (RANS) equations were solved using ANSYS FLUENT software. The method was applied on the UAS-S45 designed and manufactured by Hydra Technologies in Mexico. The lift, the drag, and the pitching moment coefficients were obtained at different angles of attack for several flight conditions defined in terms of altitudes and Mach numbers. The results obtained from the Computational Fluid Dynamics analysis were compared with the results obtained by using the DATCOM semi-empirical procedure. This comparison has indicated that our approach is highly accurate and that the aerodynamic model obtained could be useful to estimate the flight dynamics of the UAS-S45.

Keywords: aerodynamic modelling, CFD Analysis, ANSYS FLUENT, UAS-S45

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Authors: Chun-Wei Liu, Feng-Tsai Weng

Abstract:

People faced pollution threaten in modern age although the standard of exhaust gas of vehicles has been established. The goal of this study is to investigate the effect of water atomizer in a vehicle emission system. Diluted 20% ammonia water was used in spraying system. Micro particles produced by exhausted gas from engine of vehicle which were cumulated through atomized spray in a self-development collector. In experiments, a self-designed atomization model plate and a gas tank controlled by the micro-processor using Pulse Width Modulation (PWM) logic was prepared for exhaust test. The gas from gasoline-engine of vehicle was purified with the model panel collector. A soft well named ANSYS was utilized for analyzing the distribution condition of rejected gas. Micro substance and percentage of CO, HC, CO2, NOx in exhausted gas were investigated at different engine speed, and atomizer vibration frequency. Exceptional results in the vehicle engine emissions measurement were obtained. The temperature of exhausted gas can be decreased 3oC. Micro substances PM10 can be decreased and the percentage of CO can be decreased more than 55% at 2500RPM by proposed system. Value of CO, HC, CO2 and NOX was all decreased when atomizers were used with water.

Keywords: atomizer, CO, HC, NOx, PM2.5

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Authors: Anne Schattel, Mitja Echim, Christof Büskens

Abstract:

Trajectory planning for deep space missions has become a recent topic of great interest. Flying to space objects like asteroids provides two main challenges. One is to find rare earth elements, the other to gain scientific knowledge of the origin of the world. Due to the enormous spatial distances such explorer missions have to be performed unmanned and autonomously. The mathematical field of optimization and optimal control can be used to realize autonomous missions while protecting recourses and making them safer. The resulting algorithms may be applied to other, earth-bound applications like e.g. deep sea navigation and autonomous driving as well. The project KaNaRiA ('Kognitionsbasierte, autonome Navigation am Beispiel des Ressourcenabbaus im All') investigates the possibilities of cognitive autonomous navigation on the example of an asteroid mining mission, including the cruise phase and approach as well as the asteroid rendezvous, landing and surface exploration. To verify and test all methods an interactive, real-time capable simulation using virtual reality is developed under KaNaRiA. This paper focuses on the specific challenge of the guidance during the cruise phase of the spacecraft, i.e. trajectory optimization and optimal control, including first solutions and results. In principle there exist two ways to solve optimal control problems (OCPs), the so called indirect and direct methods. The indirect methods are being studied since several decades and their usage needs advanced skills regarding optimal control theory. The main idea of direct approaches, also known as transcription techniques, is to transform the infinite-dimensional OCP into a finite-dimensional non-linear optimization problem (NLP) via discretization of states and controls. These direct methods are applied in this paper. The resulting high dimensional NLP with constraints can be solved efficiently by special NLP methods, e.g. sequential quadratic programming (SQP) or interior point methods (IP). The movement of the spacecraft due to gravitational influences of the sun and other planets, as well as the thrust commands, is described through ordinary differential equations (ODEs). The competitive mission aims like short flight times and low energy consumption are considered by using a multi-criteria objective function. The resulting non-linear high-dimensional optimization problems are solved by using the software package WORHP ('We Optimize Really Huge Problems'), a software routine combining SQP at an outer level and IP to solve underlying quadratic subproblems. An application-adapted model of impulsive thrusting, as well as a model of an electrically powered spacecraft propulsion system, is introduced. Different priorities and possibilities of a space mission regarding energy cost and flight time duration are investigated by choosing different weighting factors for the multi-criteria objective function. Varying mission trajectories are analyzed and compared, both aiming at different destination asteroids and using different propulsion systems. For the transcription, the robust method of full discretization is used. The results strengthen the need for trajectory optimization as a foundation for autonomous decision making during deep space missions. Simultaneously they show the enormous increase in possibilities for flight maneuvers by being able to consider different and opposite mission objectives.

Keywords: deep space navigation, guidance, multi-objective, non-linear optimization, optimal control, trajectory planning.

Procedia PDF Downloads 383

Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores

Abstract:

This work introduces the use of EMGs (electromyography) from muscle sensors to develop an Artificial Neural Network (ANN) for pattern recognition to control a small unmanned aerial vehicle. The objective of this endeavor exhibits interfacing drone applications beyond manual control directly. MyoWare Muscle sensor contains three EMG electrodes (dual and single type) used to collect signals from the posterior (extensor) and anterior (flexor) forearm and the bicep. Collection of raw voltages from each sensor were connected to an Arduino Uno and a data processing algorithm was developed with the purpose of interpreting the voltage signals given when performing flexing, resting, and motion of the arm. Each sensor collected eight values over a two-second period for the duration of one minute, per assessment. During each two-second interval, the movements were alternating between a resting reference class and an active motion class, resulting in controlling the motion of the drone with left and right movements. This paper further investigated adding up to three sensors to differentiate between hand gestures to control the principal motions of the drone (left, right, up, and land). The hand gestures chosen to execute these movements were: a resting position, a thumbs up, a hand swipe right motion, and a flexing position. The MATLAB software was utilized to collect, process, and analyze the signals from the sensors. The protocol (machine learning tool) was used to classify the hand gestures. To generate the input vector to the ANN, the mean, root means squared, and standard deviation was processed for every two-second interval of the hand gestures. The neuromuscular information was then trained using an artificial neural network with one hidden layer of 10 neurons to categorize the four targets, one for each hand gesture. Once the machine learning training was completed, the resulting network interpreted the processed inputs and returned the probabilities of each class. Based on the resultant probability of the application process, once an output was greater or equal to 80% of matching a specific target class, the drone would perform the motion expected. Afterward, each movement was sent from the computer to the drone through a Wi-Fi network connection. These procedures have been successfully tested and integrated into trial flights, where the drone has responded successfully in real-time to predefined command inputs with the machine learning algorithm through the MyoWare sensor interface. The full paper will describe in detail the database of the hand gestures, the details of the ANN architecture, and confusion matrices results.

Keywords: artificial neural network, biosensors, electromyography, machine learning, MyoWare muscle sensors, Arduino

Procedia PDF Downloads 144

Authors: Mohammed Hmimou, Khalid Amediaz, Imane Sebari, Nabil Bounajma

Abstract:

Annual crop production is one of the most important macroeconomic indicators for the majority of countries around the world. This information is valuable, especially for exporting countries which need a yield estimation before harvest in order to correctly plan the supply chain. When it comes to estimating agricultural yield, especially for arboriculture, conventional methods are mostly applied. In the case of the citrus industry, the sale before harvest is largely practiced, which requires an estimation of the production when the fruit is on the tree. However, conventional method based on the sampling surveys of some trees within the field is always used to perform yield estimation, and the success of this process mainly depends on the expertise of the ‘estimator agent’. The present study aims to propose a methodology based on the combination of unmanned aerial vehicle (UAV) images and terrestrial laser scanner (TLS) point cloud to estimate citrus production. During data acquisition, a fixed wing and rotatory drones, as well as a terrestrial laser scanner, were tested. After that, a pre-processing step was performed in order to generate point cloud and digital surface model. At the processing stage, a machine vision workflow was implemented to extract points corresponding to fruits from the whole tree point cloud, cluster them into fruits, and model them geometrically in a 3D space. By linking the resulting geometric properties to the fruit weight, the yield can be estimated, and the statistical distribution of fruits size can be generated. This later property, which is information required by importing countries of citrus, cannot be estimated before harvest using the conventional method. Since terrestrial laser scanner is static, data gathering using this technology can be performed over only some trees. So, integration of drone data was thought in order to estimate the yield over a whole orchard. To achieve that, features derived from drone digital surface model were linked to yield estimation by laser scanner of some trees to build a regression model that predicts the yield of a tree given its features. Several missions were carried out to collect drone and laser scanner data within citrus orchards of different varieties by testing several data acquisition parameters (fly height, images overlap, fly mission plan). The accuracy of the obtained results by the proposed methodology in comparison to the yield estimation results by the conventional method varies from 65% to 94% depending mainly on the phenological stage of the studied citrus variety during the data acquisition mission. The proposed approach demonstrates its strong potential for early estimation of citrus production and the possibility of its extension to other fruit trees.

Keywords: citrus, digital surface model, point cloud, terrestrial laser scanner, UAV, yield estimation, 3D modeling

Procedia PDF Downloads 103

Authors: T. Ahilan, V. Aswin Adityan, S. Kailash

Abstract:

UAV’s are small remote operated or automated aerial surveillance systems without a human pilot aboard. UAV’s generally finds its use in military and special operation application, a recent growing trend in UAV’s finds its application in several civil and non military works such as inspection of power or pipelines. The objective of this paper is the augmentation of a UAV in order to replace the existing expensive sonar (sound navigation and ranging) based equipment amongst small scale fisherman, for whom access to sonar equipment are restricted due to limited economic resources. The surveillance equipment’s present in the UAV will relay data and GPS location onto a receiver on the fishing boat using RF signals, using which the location of the schools of fishes can be found. In addition to this, an emergency beacon system is present for rescue operations and drone recovery.

Keywords: UAV, Surveillance, RF signals, fishing, sonar, GPS, video stream, school of fish

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Authors: Mohamed Abdel-Monem, Gamal Sowilam, Omar Hegazy

Abstract:

This paper presents a technical assessment of an electric vehicle with two independent rear-wheel motor and an improved traction control system. The electric differential and the control strategy have been implemented to assure that in a straight trajectory, the two rear-wheels run exactly at the same speed, considering the same/different road conditions under the left and right side of the wheels. In case of turning to right/left, the difference between the two rear-wheels speeds assures a vehicle trajectory without sliding, thanks to a harmony between the electric differential and the control strategy. The present article demonstrates a complete model and analysis of a traction control system, considering four different traction scenarios, for two independent rear-wheels motors for electric vehicles. Furthermore, the vehicle model, including wheel dynamics, load forces, electric differential, and control strategy, is designed and verified by using MATLAB/Simulink environment.

Keywords: electric vehicle, energy saving, multi-motor, electric differential, simulation and control

Procedia PDF Downloads 315