Implementation of convolutional neural networks in accelerating units for real-time image analysis

Matomojo šviesos spektro vaizdų analizė įgalina intelektualiąsias sistemas gauti informaciją taip, kaip žmogus rega. Daugelyje sričių taikoma sąsūkos dirbtinių neuronų tinklais (SDNT) grįsta vaizdų analizė. Tačiau dėl didelės skaičiavimų apimties kyla sunkumų įgyvendinant įterptinėse sistemose lokaliai vykdomus SDNT grįstus algoritmus vaizdų analizei realiuoju laiku. Šiuo metu vaizdų analizei įterptinėse sistemose galima taikyti spartinančiuosius įrenginius, tačiau trūksta suderinamų SDNT elementų sąrašų ir SDNT pritaikymo spartinantiesiems įrenginiams įterptinėse sistemose metodikos. Darbo tyrimų objektas – sąsūkos dirbtinių neuronų tinklai vaizdams analizuoti realiuoju laiku. Disertacijoje tiriami šie, su tiriamuoju objektų susiję dalykai: įgyvendinimas spartinančiuosiuose įrenginiuose ir projektavimo metodika. Disertacijoje pateikiami vaizdams apdoroti skirtų SDNT įgyvendinimo tyrimai, kuriais remiantis sudarytas SDNT elementų sąrašas ir metodika, skirta SDNT pritaikymui įgyvendinti spartinančiuosiuose įrenginiuose pasirinktiems vaizdų analizės uždaviniams spręsti. Taip pat pateikiami dviejų taikymo sričių vaizdų analizės algoritmų, sukurtų taikant pasiūlytą elementų sąrašą ir metodiką, aprašymai. Viena iš sričių – žmogaus akies tinklainės vaizdų požymių aprašymas. Kita sritis – kelio dangos tipo ir būklės įvertinimas, analizuojant vaizdus iš automobilio priekyje sumontuotos vaizdo kameros. Pagrindiniai disertacijos rezultatai yra paskelbti septyniuose moksliniuose straipsniuose recenzuojamuose mokslo leidiniuose: vienas straipsnis mokslo žurnale, referuojamame Clarivate Analytics Web of Science duomenų bazėje, turintis citavimo indeksą 1,524, vienas straipsnis mokslo žurnale, referuojamame Clarivate Analytics Web of Science duomenų bazėje, vienas straipsnis mokslo žurnale, referuojamame Index Copernicus duomenų bazėje, trys straipsniai tarptautinių konferencijų medžiagose, referuojamose Clarivate Analytics Web of Science Proceedings duomenų bazėje, vienas straipsnis konferencijos medžiagoje, referuojamoje kitose duomenų bazėse. Disertacijoje atliktų tyrimų rezultatai buvo pristatyti devyniose mokslinėse konferencijose Lietuvoje ir užsienyje. Disertaciją sudaro įžanga, trys skyriai, bendrosios išvados, literatūros šaltinių sąrašas ir trys priedai.Disertacij

Technological measures of forefront road identification for vehicle comfort and safety improvement

This paper presents the technological measures currently being developed at institutes and vehicle research centres dealing with forefront road identification. In this case, road identification corresponds with the surface irregularities and road surface type, which are evaluated by laser scanning and image analysis. Real-time adaptation, adaptation in advance and system external informing are stated as sequential generations of vehicle suspension and active braking systems where road identification is significantly important. Active and semi-active suspensions with their adaptation technologies for comfort and road holding characteristics are analysed. Also, an active braking system such as Anti-lock Braking System (ABS) and Autonomous Emergency Braking (AEB) have been considered as very sensitive to the road friction state. Artificial intelligence methods of deep learning have been presented as a promising image analysis method for classification of 12 different road surface types. Concluding the achieved benefit of road identification for traffic safety improvement is presented with reference to analysed research reports and assumptions made after the initial evaluation

Variability of gravel pavement roughness: an analysis of the impact on vehicle dynamic response and driving comfort

Gravel pavement has lower construction costs but poorer performance than asphalt surfaces on roads. It also emits dust and deforms under the impact of vehicle loads and ambient air factors; the resulting ripples and ruts constantly deepen, and therefore increase vehicle vibrations and fuel consumption, and reduce safe driving speed and comfort. In this study, existing pavement quality evaluation indexes are analysed, and a methodology for adapting them for roads with gravel pavement is proposed. We report the measured wave depth and length of gravel pavement profile using the straightedge method on a 160 m long road section at three stages of road utilization. The measured pavement elevation was processed according to ISO 8608, and the frequency response of a vehicle was investigated using simulations in MATLAB/Simulink. The international roughness index (IRI) analysis showed that a speed of 30-45 km/h instead of 80 km/h provided the objective results of the IRI calculation on the flexible pavement due to the decreasing velocity of a vehicle’s unsprung mass on a more deteriorated road pavement state. The influence of the corrugation phenomenon of gravel pavement was explored, identifying specific driving safety and comfort cases. Finally, an increase in the dynamic load coefficient (DLC) at a low speed of 30 km/h on the most deteriorated pavement and a high speed of 90 km/h on the middle-quality pavement demonstrated the demand for timely gravel pavement maintenance and the complicated prediction of a safe driving speed for drivers. The main relevant objectives of this study are the adaptation of a road roughness indicator to gravel pavement, including the evaluation of vehicle dynamic responses at different speeds and pavement deterioration states

Feasibility of a neural network-based virtual sensor for vehicle unsprung mass relative velocity estimation

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity

Deep neural network based data-driven virtual sensor in vehicle semi-active suspension real-time control

This research presents a data-driven Neural Network (NN)-based Virtual Sensor (VS) that estimates vehicles’ Unsprung Mass (UM) vertical velocity in real-time. UM vertical velocity is an input parameter used to control a vehicle’s semi-active suspension. The extensive simulation-based dataset covering 95 scenarios was created and used to obtain training, validation and testing data for Deep Neural Network (DNN). The simulations have been performed with an experimentally validated full vehicle model using software for advanced vehicle dynamics simulation. VS was developed and tested, taking into account the Root Mean Square (RMS) of Sprung Mass (SM) acceleration as a comfort metric. The RMS was calculated for two cases: using actual UM velocity and estimations from the VS as input to the suspension controller. The comparison shows that RMS change is less than the difference threshold that vehicle occupants could perceive. The achieved result indicates the great potential of using the proposed VS in place of the physical sensor in vehicles

Binokulinės regos skaitinis modelis horizontalių erdvės koordinačių kodavimui

Darbe pristatome binokulinės regos sistemos skaitinį modelį, atliekantį regimosios erdvės atvaizdavimą į jos subjektyvų suvokinį. Objekto, esančio horizontalioje regimosios erdvės plokštumoje, padėtis koduojama trimačio vektoriaus kryptimi. Šis trimatis vektorius yra vidinė išorinės erdvės reprezentacija. Vektoriaus kryptis dekoduojama rinkiniu neuronų detektorių, kurių kiekvienas tampa selektyvus tam tikrai objekto padėčiai plokštumoje regimoje erdvėje. Pasiūlytas modelis leidžia įvertinti subjektyvius atstumus gylyje ir kryptyje, paaiškina tokias žinomas biologinės binokulinės regos sistemos savybes kaip alelotropija, fuzija (Panumo srityje), diplopija, skirtingi gylio suvokimo jautrumai

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportatio

Visual Measurement System for Wheel–Rail Lateral Position Evaluation

Railway infrastructure must meet safety requirements concerning its construction and operation. Track geometry monitoring is one of the most important activities in maintaining the steady technical conditions of rail infrastructure. Commonly, it is performed using complex measurement equipment installed on track-recording coaches. Existing low-cost inertial sensor-based measurement systems provide reliable measurements of track geometry in vertical directions. However, solutions are needed for track geometry parameter measurement in the lateral direction. In this research, the authors developed a visual measurement system for track gauge evaluation. It involves the detection of measurement points and the visual measurement of the distance between them. The accuracy of the visual measurement system was evaluated in the laboratory and showed promising results. The initial field test was performed in the Vilnius railway station yard, driving at low velocity on the straight track section. The results show that the image point selection method developed for selecting the wheel and rail points to measure distance is stable enough for TG measurement. Recommendations for the further improvement of the developed system are presented.This article belongs to the Special Issue Data Acquisition and Processing for Fault DiagnosisThe research was funded by the EU Shift2Rail project Assets4Rail (grant number: 826250) under the Horizon 2020/Shift2Rail Framework Programme

A Robust Identification of the Protein Standard Bands in Two-Dimensional Electrophoresis Gel Images

The aim of the investigation presented in this paper was to develop a software-based assistant for the protein analysis workflow. The prior characterization of the unknown protein in two-dimensional electrophoresis gel images is performed according to the molecular weight and isoelectric point of each protein spot estimated from the gel image before further sequence analysis by mass spectrometry. The paper presents a method for automatic and robust identification of the protein standard band in a two-dimensional gel image. In addition, the method introduces the identification of the positions of the markers, prepared by using pre-selected proteins with known molecular mass. The robustness of the method was achieved by using special validation rules in the proposed original algorithms. In addition, a self-organizing map-based decision support algorithm is proposed, which takes Gabor coefficients as image features and searches for the differences in preselected vertical image bars. The experimental investigation proved the good performance of the new algorithms included into the proposed method. The detection of the protein standard markers works without modification of algorithm parameters on two-dimensional gel images obtained by using different staining and destaining procedures, which results in different average levels of intensity in the images

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

Integrated chassis control systems represent a significant advancement in the dynamics of ground vehicles, aimed at enhancing overall performance, comfort, handling, and stability. As vehicles transition from internal combustion to electric platforms, integrated chassis control systems have evolved to meet the demands of electrification and automation. This paper analyses the overall control structure of automated vehicles with integrated chassis control systems. Integration of longitudinal, lateral, and vertical systems presents complexities due to the overlapping control regions of various subsystems. The presented methodology includes a comprehensive examination of state-of-the-art technologies, focusing on algorithms to manage control actions and prevent interference between subsystems. The results underscore the importance of control allocation to exploit the additional degrees of freedom offered by over-actuated systems. This paper systematically overviews the various control methods applied in integrated chassis control and path tracking. This includes a detailed examination of perception and decision-making, parameter estimation techniques, reference generation strategies, and the hierarchy of controllers, encompassing high-level, middle-level, and low-level control components. By offering this systematic overview, this paper aims to facilitate a deeper understanding of the diverse control methods employed in automated driving with integrated chassis control, providing insights into their applications, strengths, and limitations.Intelligent Vehicle