Search results for: Autonomous vehicles

Authors: Alessandro Preziosi, Antonio Prioletti, Luca Castangia

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Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations.

Keywords: Autonomous vehicles, deformable part model, dpm, pedestrian recognition.

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Authors: Thach D. Do, Juhum Kwon, Chang-Joo Moon

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A Ground Control System (GCS), which controls Unmanned Aerial Vehicles (UAVs) and monitors their missionrelated data, is one of the major components of UAVs. In fact, some traditional GCSs were built on an expensive, complicated hardware infrastructure with workstations and PCs. In contrast, a GCS on a portable device – such as an Android phone or tablet – takes advantage of its light-weight hardware and the rich User Interface supported by the Android Operating System. We implemented that kind of GCS and called it Ground System Software (GSS) in this paper. In operation, our GSS communicates with UAVs or other GSS via TCP/IP connection to get mission-related data, visualizes it on the device-s screen, and saves the data in its own database. Our study showed that this kind of system will become a potential instrument in UAV-related systems and this kind of topic will appear in many research studies in the near future.

Keywords: Android Operating System, Ground Control System, Mobile Device, Unmanned Aerial Vehicle.

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Authors: David Tena-Gago, Jose M. Alcaraz Calero, Qi Wang

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The evolution and integration of automated vehicles have become more and more tangible in recent years. State-of-the-art technological advances in the field of camera-based Artificial Intelligence (AI) and computer vision greatly favor the performance and reliability of Advanced Driver Assistance System (ADAS), leading to a greater knowledge of vehicular operation and resembling the human behaviour. However, the exclusive use of this technology still seems insufficient to control the vehicular operation at 100%. To reveal the degree of accuracy of the current camera-based automated driving AI modules, this paper studies the structure and behavior of one of the main solutions in a controlled testing environment. The results obtained clearly outline the lack of reliability when using exclusively the AI model in the perception stage, thereby entailing using additional complementary sensors to improve its safety and performance.

Keywords: Accuracy assessment, AI-Driven Mobility, Artificial Intelligence, automated vehicles.

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Authors: Eshagh Rasouli Sarabi, Mir Saeed Moosavi

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In Iran, due to abundance of energy resources, energy consumption is extraordinarily higher than international standards and transportation sector is considered to be one of the major consumers of energy. Moreover, air pollution in urban areas as a result of high dependence on private vehicle and lower standards of vehicles, high subsidies spent on fuel and time waste due to traffic congestion in urban areas all have led to speculations on new strategies and policies in order to control energy consumption in transportation sector. These strategies and policies will be introduced in this paper and their consequences will be analyzed with consideration to socio-economic factors affecting the urban society of Iran. Besides, the intention is to suggest and analyze new approaches such as broader application of public transportation system, demand management in transport sector, replacement of deteriorated vehicles, quality improvement in car manufacture and introduction of substitute fuels.

Keywords: Consumption, energy, fuel, transportation.

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Authors: Jieh-Shian Young, Chan Wei Hsu

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This paper mainly studies the analyses of parameters in the intersection collision avoidance (ICA) system based on the radar sensors. The parameters include the positioning errors, the repeat period of the radar sensor, the conditions of potential collisions of two cross-path vehicles, etc. The analyses of the parameters can provide the requirements, limitations, or specifications of this ICA system. In these analyses, the positioning errors will be increased as the measured vehicle approach the intersection. In addition, it is not necessary to implement the radar sensor in higher position since the positioning sensitivities become serious as the height of the radar sensor increases. A concept of the safety buffer distances for front and rear of the measured vehicle is also proposed. The conditions for potential collisions of two cross-path vehicles are also presented to facilitate the computation algorithm.

Keywords: Intersection Collision Avoidance (ICA), Positioning Errors, Radar Sensors, Sensitivity of Positioning.

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Authors: Toru Murayama, Akinori Nagano, Zhi-Wei Luo

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In this research, we study a control method of a multivehicle system while considering the limitation of communication range for each vehicles. When we control networked vehicles with limitation of communication range, it is important to control the communication network structure of a multi-vehicle system in order to keep the network-s connectivity. From this, we especially aim to control the network structure to the target structure. We formulate the networked multi-vehicle system with some disturbance and the communication constraints as a hybrid dynamical system, and then we study the optimal control problems of the system. It is shown that the system converge to the objective network structure in finite time when the system is controlled by the receding horizon method. Additionally, the optimal control probrems are convertible into the mixed integer problems and these problems are solvable by some branch and bound algorithm.

Keywords: Hybrid system, multi-vehicle system, receding horizon control, topology control.

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Authors: Jayanth Jacob, C. V. Hariharakrishnan, Suganthi L.

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The rapid urbanization of cities has a bane in the form road accidents that cause extensive damage to life and limbs. A number of location based factors are enablers of road accidents in the city. The speed of travel of vehicles is non-uniform among locations within a city. In this study, the perception of vehicle users is captured on a 10-point rating scale regarding the degree of variation in speed of travel at chosen locations in the city. The average rating is used to cluster locations using fuzzy c-means clustering and classify them as low, moderate and high speed of travel locations. The high speed of travel locations can be classified proactively to ensure that accidents do not occur due to the speeding of vehicles at such locations. The advantage of fuzzy c-means clustering is that a location may be a part of more than one cluster to a varying degree and this gives a better picture about the location with respect to the characteristic (speed of travel) being studied.

Keywords: C-means clustering, Location Specific, Road Accidents.

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: Multiclass classification, convolution neural network, OpenCV, Data Augmentation.

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Authors: Taylan Kabbani, Ekrem Duman

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Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining  the financial assets price ”prediction” step and the ”allocation” step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solved the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and achieved a 2.68 Sharpe ratio on the test dataset. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making.

Keywords: Autonomous agent, deep reinforcement learning, MDP, sentiment analysis, stock market, technical indicators, twin delayed deep deterministic policy gradient.

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Authors: Abdel Ilah Alshabtat, Liang Dong

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In this paper, we proposed a new routing protocol for Unmanned Aerial Vehicles (UAVs) that equipped with directional antenna. We named this protocol Directional Optimized Link State Routing Protocol (DOLSR). This protocol is based on the well known protocol that is called Optimized Link State Routing Protocol (OLSR). We focused in our protocol on the multipoint relay (MPR) concept which is the most important feature of this protocol. We developed a heuristic that allows DOLSR protocol to minimize the number of the multipoint relays. With this new protocol the number of overhead packets will be reduced and the End-to-End delay of the network will also be minimized. We showed through simulation that our protocol outperformed Optimized Link State Routing Protocol, Dynamic Source Routing (DSR) protocol and Ad- Hoc On demand Distance Vector (AODV) routing protocol in reducing the End-to-End delay and enhancing the overall throughput. Our evaluation of the previous protocols was based on the OPNET network simulation tool.

Keywords: Mobile Ad-Hoc Networks, Ad-Hoc RoutingProtocols, Optimized link State Routing Protocol, Unmanned AerialVehicles, Directional Antenna.

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Authors: Ahmad Aljaafreh

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This paper proposes a hierarchical hidden Markov model (HHMM) to model the detection of M vehicles in a wireless sensor network (WSN). The HHMM model contains an extra level of hidden Markov model to model the temporal transitions of each state of the first HMM. By modeling the temporal transitions, only those hypothesis with nonzero transition probabilities needs to be tested. Thus, this method efficiently reduces the computation load, which is preferable in WSN applications.This paper integrates several techniques to optimize the detection performance. The output of the states of the first HMM is modeled as Gaussian Mixture Model (GMM), where the number of states and the number of Gaussians are experimentally determined, while the other parameters are estimated using Expectation Maximization (EM). HHMM is used to model the sequence of the local decisions which are based on multiple hypothesis testing with maximum likelihood approach. The states in the HHMM represent various combinations of vehicles of different types. Due to the statistical advantages of multisensor data fusion, we propose a heuristic based on fuzzy weighted majority voting to enhance cooperative classification of moving vehicles within a region that is monitored by a wireless sensor network. A fuzzy inference system weighs each local decision based on the signal to noise ratio of the acoustic signal for target detection and the signal to noise ratio of the radio signal for sensor communication. The spatial correlation among the observations of neighboring sensor nodes is efficiently utilized as well as the temporal correlation. Simulation results demonstrate the efficiency of this scheme.

Keywords: Classification, decision fusion, fuzzy logic, hidden Markov model

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Authors: Sergey N. Sayapin, Anatoly P. Karpenko, Suan H. Dang

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Comparative analysis of robotic vehicles for pipe inspection is presented in this paper. The promising concept of self-propelled intelligent robotic vehicle (SPIRV) based on octahedral dodekapod for inspection and operation in active branched pipelines with variable cross-sections is reasoned. SPIRV is able to move in pipeline, regardless of its spatial orientation. SPIRV can also be used to move along the outside of the pipelines as well as in space between surfaces of annular tubes. Every one of faces of the octahedral dodekapod can clamp/unclamp a thing with a closed loop surface of various forms as well as put pressure on environmental surface of contact. These properties open new possibilities for its applications in SPIRV. We examine design principles of octahedral dodekapod as future intelligent building blocks for various robotic vehicles that can self-move and self-reconfigure.

Keywords: Modular robot, octahedral dodekapod, pipe inspection robot, spatial parallel structure.

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Authors: Rania Alshikhe, Vinita Jindal

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Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from trav-eling vehicles, such as taxis through installed global positioning sys-tem (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

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Authors: H. Sayyaadi, N. Shokouhi

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In rail vehicles, air springs are very important isolating component, which guarantee good ride comfort for passengers during their trip. In the most new rail–vehicle models, developed by researchers, the thermo–dynamical effects of air springs are ignored and secondary suspension is modeled by simple springs and dampers. As the performance of suspension components have significant effects on rail–vehicle dynamics and ride comfort of passengers, a complete nonlinear thermo–dynamical air spring model, which is a combination of two different models, is introduced. Result from field test shows remarkable agreement between proposed model and experimental data. Effects of air suspension parameters on the system performances are investigated here and then these parameters are tuned to minimize Sperling ride comfort index during the trip. Results showed that by modification of air suspension parameters, passengers comfort is improved and ride comfort index is reduced about 10%.

Keywords: Air spring, Ride comfort improvement, Thermo– dynamical effects.

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Authors: Hsu-Yung Cheng, Chih-Chang Yu

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In this work, we present an automatic vehicle detection system for airborne videos using combined features. We propose a pixel-wise classification method for vehicle detection using Dynamic Bayesian Networks. In spite of performing pixel-wise classification, relations among neighboring pixels in a region are preserved in the feature extraction process. The main novelty of the detection scheme is that the extracted combined features comprise not only pixel-level information but also region-level information. Afterwards, tracking is performed on the detected vehicles. Tracking is performed using efficient Kalman filter with dynamic particle sampling. Experiments were conducted on a wide variety of airborne videos. We do not assume prior information of camera heights, orientation, and target object sizes in the proposed framework. The results demonstrate flexibility and good generalization abilities of the proposed method on a challenging dataset.

Keywords: Vehicle Detection, Airborne Video, Tracking, Dynamic Bayesian Networks

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Authors: Tao Zhao, Xianshuo Xu

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Low-carbon public passenger transport is an important part of low carbon city. The CO₂ emissions of public passenger transport in Tianjin from 1995 to 2010 are estimated with IPCC CO₂ counting method, which shows that the total CO₂ emissions of Tianjin public passenger transport have gradually become stable at 1,425.1 thousand tons. And then the CO₂ emissions of the buses, taxies, and rail transits are calculated respectively. A CO₂ emission of 829.9 thousand tons makes taxies become the largest CO₂ emissions source among the public passenger transport in Tianjin. Combining with passenger volume, this paper analyzes the CO₂ emissions proportion of the buses, taxies, and rail transits compare the passenger transport rate with the proportion of CO₂ emissions, as well as the CO₂ emissions change of per 10,000 people. The passenger volume proportion of bus among the three public means of transport is 72.62% which is much higher than its CO₂ emissions proportion of 36.01%, with the minimum number of CO₂ emissions per 10,000 people of 4.90 tons. The countermeasures to reduce CO₂ emissions of public passenger transport in Tianjin are to develop rail transit, update vehicles and use alternative fuel vehicles.

Keywords: Public passenger transport, carbon emissions, countermeasures.

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Authors: Maged A. Mossallam

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The objective from this paper is to design a solar thermal engine for space vehicles orbital control and electricity generation. A computational model is developed for the prediction of the solar thermal engine performance for different design parameters and conditions in order to enhance the engine efficiency. The engine is divided into two main subsystems. First, the concentrator dish which receives solar energy from the sun and reflects them to the cavity receiver. The second one is the cavity receiver which receives the heat flux reflected from the concentrator and transfers heat to the fluid passing over. Other subsystems depend on the application required from the engine. For thrust application, a nozzle is introduced to the system for the fluid to expand and produce thrust. Hydrogen is preferred as a working fluid in the thruster application. Results model developed is used to determine the thrust for a concentrator dish 4 meters in diameter (provides 10 kW of energy), focusing solar energy to a 10 cm aperture diameter cavity receiver. The cavity receiver outer length is 50 cm and the internal cavity is 47 cm in length. The suggested design material of the internal cavity is tungsten to withstand high temperature. The thermal model and analysis shows that the hydrogen temperature at the plenum reaches 2000oK after about 250 seconds for hot start operation for a flow rate of 0.1 g/sec.Using solar thermal engine as an electricity generation device on earth is also discussed. In this case a compressor and turbine are used to convert the heat gained by the working fluid (air) into mechanical power. This mechanical power can be converted into electrical power by using a generator.

Keywords: Concentrated Solar Energy, Orbital Control, Power Generation, Solar Thermal Engine, Space Vehicles Propulsion

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Authors: Mona Lisa M. Oliveira, Nara A. Policarpo, Ana Luiza B. P. Barros, Carla A. Silva

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Selective catalytic reduction systems for nitrogen oxides reduction by ammonia has been the chosen technology by most of diesel vehicle (i.e. bus and truck) manufacturers in Brazil, as also in Europe. Furthermore, at some conditions, over-stoichiometric ammonia availability is also needed that increases the NH₃ slips even more. Ammonia (NH₃) by this vehicle exhaust aftertreatment system provides a maximum efficiency of NOx removal if a significant amount of NH₃ is stored on its catalyst surface. In the other words, the practice shows that slightly less than 100% of the NOx conversion is usually targeted, so that the aqueous urea solution hydrolyzes to NH₃ via other species formation, under relatively low temperatures. This paper presents a model based on neural networks integrated with a road vehicle simulator that allows to estimate NH₃-slip emission factors for different driving conditions and patterns. The proposed model generates high NH₃slips which are not also limited in Brazil, but more efforts needed to be made to elucidate the contribution of vehicle-emitted NH₃ to the urban atmosphere.

Keywords: Ammonia slip, neural-network, vehicles emissions, SCR-NOx.

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Authors: Mahmud Fotuhi-Firuzabad, Ali Abbaspour, Mohsen Mazidi, Mohamamd Rastegar

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To tackle the air pollution issues, Plug-in Hybrid Electric Vehicles (PHEVs) are proposed as an appropriate solution. Charging a large amount of PHEV batteries, if not controlled, would have negative impacts on the distribution system. The control process of charging of these vehicles can be centralized in parking lots that may provide a chance for better coordination than the individual charging in houses. In this paper, an optimization-based approach is proposed to determine the optimum PHEV parking capacities in candidate nodes of the distribution system. In so doing, a profile for charging and discharging of PHEVs is developed in order to flatten the network load profile. Then, this profile is used in solving an optimization problem to minimize the distribution system losses. The outputs of the proposed method are the proper place for PHEV parking lots and optimum capacity for each parking. The application of the proposed method on the IEEE-34 node test feeder verifies the effectiveness of the method.

Keywords: Plug-in Hybrid Electric Vehicle (PHEV), PHEV parking lot, V2G.

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Authors: Mark Schudeleit, Christian Sieg, Ferit Küçükay

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This paper describes how to dimension the electric components of a 48V hybrid system considering real customer use. Furthermore, it provides information about savings in energy and CO2 emissions by a customer-tailored 48V hybrid. Based on measured customer profiles, the electric units such as the electric motor and the energy storage are dimensioned. Furthermore, the CO2 reduction potential in real customer use is determined compared to conventional vehicles. Finally, investigations are carried out to specify the topology design and preliminary considerations in order to hybridize a conventional vehicle with a 48V hybrid system. The emission model results from an empiric approach also taking into account the effects of engine dynamics on emissions. We analyzed transient engine emissions during representative customer driving profiles and created emission meta models. The investigation showed a significant difference in emissions when simulating realistic customer driving profiles using the created verified meta models compared to static approaches which are commonly used for vehicle simulation.

Keywords: Customer use, dimensioning, hybrid electric vehicles, vehicle simulation, 48V hybrid system.

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Authors: Hyun-Koo Kim, Sagong Kuk, MinKwan Kim, Ho-Youl Jung

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This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, to effectively extract spotlight of interest, a segmentation process based on automatic multi-level threshold method is applied on the road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process based on light tracking and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with near infrared mono-camera and tested it in the urban and rural roads. Through the test, classification performances are above 97% of true positive rate evaluated on real-time environment. Our method also has good performance in the case of clear, fog and rain weather.

Keywords: Assistance Driving System, Multi-level Threshold Method, Near Infrared Mono Camera, Nighttime Vehicle Detection.

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Authors: Jyong Lin, Shih-Chang Chen, Yu-Tsen Shih, Shi-Huang Chen

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This paper presents a remote on-line diagnostic system for vehicles via the use of On-Board Diagnostic (OBD), GPS, and 3G techniques. The main parts of the proposed system are on-board computer, vehicle monitor server, and vehicle status browser. First, the on-board computer can obtain the location of deriver and vehicle status from GPS receiver and OBD interface, respectively. Then on-board computer will connect with the vehicle monitor server through 3G network to transmit the real time vehicle system status. Finally, vehicle status browser could show the remote vehicle status including vehicle speed, engine rpm, battery voltage, engine coolant temperature, and diagnostic trouble codes. According to the experimental results, the proposed system can help fleet managers and car knockers to understand the remote vehicle status. Therefore this system can decrease the time of fleet management and vehicle repair due to the fleet managers and car knockers who find the diagnostic trouble messages in time.

Keywords: Diagnostic Trouble Code (DTC), Electronic Control Unit (ECU), Global Position System (GPS), On-Board Diagnostic (OBD).

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Authors: M.M. Al Emran Hasan, M.Ektesabi, A.Kapoor

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Recently electric vehicles are becoming popular as an alternative of conventional fossil fuel vehicles. Conventional Internal Combustion Engine (ICE) vehicle uses fossil fuel which contributing a major part of overall carbon emission in the environment. Carbon and other green house gas emission are responsible for global warming and resulting climate change. It becomes vital to evaluate performance of vehicle based on emission. In this paper an effort has been made to depict the picture of emission caused by vehicle and scenario of Australia has taken into account. Effort has been made to compare the fossil based vehicle with electric vehicle in phases. The study also evaluates advancement in electric vehicle technology, required infrastructure for sustainability and future scope of developments. This paper also includes the evaluation of electric vehicle concept for pollution control and sustainable transport systems in future. This study can be a benchmark for development of electric vehicle as low carbon emission alternative for the cities of tomorrow.

Keywords: Electric Vehicle, Fossil Fuel, Internal CombustionEngine, Green House Gas, In wheel motor, Smart grid

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Authors: Wei Lei, Hui Chen, Lin Lu

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Microscopic emission and fuel consumption models have been widely recognized as an effective method to quantify real traffic emission and energy consumption when they are applied with microscopic traffic simulation models. This paper presents a framework for developing the Microscopic Emission (HC, CO, NOx, and CO2) and Fuel consumption (MEF) models for light-duty vehicles. The variable of composite acceleration is introduced into the MEF model with the purpose of capturing the effects of historical accelerations interacting with current speed on emission and fuel consumption. The MEF model is calibrated by multivariate least-squares method for two types of light-duty vehicle using on-board data collected in Beijing, China by a Portable Emission Measurement System (PEMS). The instantaneous validation results shows the MEF model performs better with lower Mean Absolute Percentage Error (MAPE) compared to other two models. Moreover, the aggregate validation results tells the MEF model produces reasonable estimations compared to actual measurements with prediction errors within 12%, 10%, 19%, and 9% for HC, CO, NOx emissions and fuel consumption, respectively.

Keywords: Emission, Fuel consumption, Light-duty vehicle, Microscopic, Modeling.

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Authors: Zh. Kumganbayev, U. Ahatov, A. Darkenbayev

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This article refers to the action of Kazakh intelligentsia towards the formation of national state and their attempt for reconstruction of national independence and building the way to nowadays- independence through reviewing the history of our national ideology.

Keywords: Kazakh intelligentsia's activity, Kazakh intellectuals, independence of Kazakhstan, Kazakh nation, Alashorda.

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Authors: Robson C. Santos, Julio C. P. Ribeiro, Iorran M. de Castro, Luan C. F. Rodrigues, Sandro R. L. Silva, Diego M. Quesada

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The work aims to develop a robot in the form of autonomous vehicle to detect, inspection and mapping of underground pipelines through the ATmega328 Arduino platform. Hardware prototyping is very similar to C / C ++ language that facilitates its use in robotics open source, resembles PLC used in large industrial processes. The robot will traverse the surface independently of direct human action, in order to automate the process of detecting buried pipes, guided by electromagnetic induction. The induction comes from coils that send the signal to the Arduino microcontroller contained in that will make the difference in intensity and the treatment of the information, and then this determines actions to electrical components such as relays and motors, allowing the prototype to move on the surface and getting the necessary information. This change of direction is performed by a stepper motor with a servo motor. The robot was developed by electrical and electronic assemblies that allowed test your application. The assembly is made up of metal detector coils, circuit boards and microprocessor, which interconnected circuits previously developed can determine, process control and mechanical actions for a robot (autonomous car) that will make the detection and mapping of buried pipelines plates. This type of prototype can prevent and identifies possible landslides and they can prevent the buried pipelines suffer an external pressure on the walls with the possibility of oil leakage and thus pollute the environment.

Keywords: Robotic, metal detector, embedded system, pipeline.

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Authors: Batin Latif Aylak, Bernd Noche, M. Baran Cantepe, Aydin Karakaya

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Ultra-light overhead conveyor systems are rope-based conveying systems with individually driven vehicles. The vehicles can move automatically on the rope and this can be realized by energy and signals. The ultra-light overhead conveyor systems always must be integrated with a logistical process by finding a best way for a cheaper material flow in order to guarantee precise and fast workflows. This paper analyzes the process of an ultra-light overhead conveyor system using necessary assumptions. The analysis consists of three scenarios. These scenarios are based on raising the vehicle speeds with equal increments at each case. The correlation between the vehicle speed and system throughput is investigated. A discrete-event simulation model of an ultra-light overhead conveyor system is constructed using DOSIMIS-3 software to implement three scenarios. According to simulation results; the optimal scenario, hence the optimal vehicle speed, is found out among three scenarios. This simulation model demonstrates the effect of increased speed on the system throughput.

Keywords: Logistics, material flow, simulation, ultra-light overhead conveyor.

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Authors: Wong Poh Lee, Mohd. Azam Osman, Abdullah Zawawi Talib, Ahmad Izani Md. Ismail

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Traffic congestion has become a major problem in many countries. One of the main causes of traffic congestion is due to road merges. Vehicles tend to move slower when they reach the merging point. In this paper, an enhanced algorithm for traffic simulation based on the fluid-dynamic algorithm and kinematic wave theory is proposed. The enhanced algorithm is used to study traffic congestion at a road merge. This paper also describes the development of a dynamic traffic simulation tool which is used as a scenario planning and to forecast traffic congestion level in a certain time based on defined parameter values. The tool incorporates the enhanced algorithm as well as the two original algorithms. Output from the three above mentioned algorithms are measured in terms of traffic queue length, travel time and the total number of vehicles passing through the merging point. This paper also suggests an efficient way of reducing traffic congestion at a road merge by analyzing the traffic queue length and travel time.

Keywords: Dynamic, fluid-dynamic, kinematic wave theory, simulation, traffic congestion.

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Authors: Chi-Fang Huang, Yun-Shiow Chen, Yun-Kung Chung

Abstract:

In the past decade, artificial neural networks (ANNs) have been regarded as an instrument for problem-solving and decision-making; indeed, they have already done with a substantial efficiency and effectiveness improvement in industries and businesses. In this paper, the Back-Propagation neural Networks (BPNs) will be modulated to demonstrate the performance of the collaborative forecasting (CF) function of a Collaborative Planning, Forecasting and Replenishment (CPFR®) system. CPFR functions the balance between the sufficient product supply and the necessary customer demand in a Supply and Demand Chain (SDC). Several classical standard BPN will be grouped, collaborated and exploited for the easy implementation of the proposed modular ANN framework based on the topology of a SDC. Each individual BPN is applied as a modular tool to perform the task of forecasting SKUs (Stock-Keeping Units) levels that are managed and supervised at a POS (point of sale), a wholesaler, and a manufacturer in an SDC. The proposed modular BPN-based CF system will be exemplified and experimentally verified using lots of datasets of the simulated SDC. The experimental results showed that a complex CF problem can be divided into a group of simpler sub-problems based on the single independent trading partners distributed over SDC, and its SKU forecasting accuracy was satisfied when the system forecasted values compared to the original simulated SDC data. The primary task of implementing an autonomous CF involves the study of supervised ANN learning methodology which aims at making “knowledgeable" decision for the best SKU sales plan and stocks management.

Keywords: CPFR, artificial neural networks, global logistics, supply and demand chain.

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Authors: Alireza Ansariyar, Mansoureh Jeihani

Abstract:

Light Detection and Ranging (LiDAR) sensors is capable of recording traffic data including the number of passing vehicles and bicyclists, the speed of vehicles and bicyclists, and the number of conflicts among both road users. In order to collect real-time traffic data and investigate the safety of different road users, a LiDAR sensor was installed at Cold Spring Ln – Hillen Rd intersection in Baltimore city. The frequency and severity of collected real-time conflicts were analyzed and the results highlighted that 122 conflicts were recorded over a 10-month time interval from May 2022 to February 2023. By employing an image-processing algorithm, a safety Measure of Effectiveness (MOE) aims to identify critical zones for bicyclists upon entering each respective zone at the signalized intersection. Considering the trajectory of conflicts, the results of analysis demonstrated that conflicts in the northern approach (zone N) are more frequent and severe. Additionally, sunny weather is more likely to cause severe vehicle-bike conflicts.

Keywords: LiDAR sensor, Post Encroachment Time threshold, vehicle-bike conflicts, measure of effectiveness, weather condition.

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