Drowsiness Classification for Internal Driving Situation Awareness on Mobile Platform

the sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without glasses, and light-dark background. This work aims to classify drowsiness, warn, and inform drivers, helping them to stop falling asleep at the wheel. The integrated CNN-based method is used to create a high accuracy and simple-to-use real-time driver drowsiness monitoring framework for embedded devices and Android phones

Towards Autonomous Driving Using Vision Based Intelligent Systems

Vision Based systems have become an integral part when it comes to autonomous driving. The autonomous industry has seen a made large progress in the perception of environment as a result of the improvements done towards vision based systems. As the industry moves up the ladder of automation, safety features are coming more and more into the focus. Different safety measurements have to be taken into consideration based on different driving situations. One of the major concerns of the highest level of autonomy is to obtain the ability of understanding both internal and external situations. Most of the research made on vision based systems are focused on image processing and artificial intelligence systems like machine learning and deep learning. Due to the current generation of technology being the generation of “Connected World”, there is no lack of data any more. As a result of the introduction of internet of things, most of these connected devices are able to share and transfer data. Vision based techniques are techniques that are hugely depended on these vision based data.

Traffic Light and Back-light Recognition using Deep Learning and Image Processing with Raspberry Pi

Traffic light detection and back-light recognition are essential research topics in the area of intelligent vehicles because they avoid vehicle collision and provide driver safety. Improved detection and semantic clarity may aid in the prevention of traffic accidents by self-driving cars at crowded junctions, thus improving overall driving safety. Complex traffic situations, on the other hand, make it more difficult for algorithms to identify and recognize objects. The latest state-of-the-art algorithms based on Deep Learning and Computer Vision are successfully addressing the majority of real-time problems for autonomous driving, such as detecting traffic signals, traffic signs, and pedestrians. We propose a combination of deep learning and image processing methods while using the MobileNetSSD (deep neural network architecture) model with transfer learning for real-time detection and identification of traffic lights and back-light. This inference model is obtained from frameworks such as Tensor-Flow and Tensor-Flow Lite which is trained on the COCO data. This study investigates the feasibility of executing object detection on the Raspberry Pi 3B+, a widely used embedded computing board. The algorithm’s performance is measured in terms of frames per second (FPS), accuracy, and inference time.

Dataset Evaluation for Multi Vehicle Detection using Vision Based Techniques

Vehicle detection is one of the primal challenges of modern driver-assistance systems owing to the numerous factors, for instance, complicated surroundings, diverse types of vehicles with varied appearance and magnitude, low-resolution videos, fast-moving vehicles. It is utilized for multitudinous applications including traffic surveillance and collision prevention. This paper suggests a Vehicle Detection algorithm developed on Image Processing and Machine Learning. The presented algorithm is predicated on a Support Vector Machine(SVM) Classifier which employs feature vectors extracted via Histogram of Gradients(HOG) approach conducted on a semi-real time basis. A comparison study is presented stating the performance metrics of the algorithm on different datasets.

Automated Evaluation of Smart City Data from Cloud-System

Smart city data processing is an important taskfor the promotion and development of smart cities. The articledescribes and presents the types of smart city data, discusses theexisting modern methods and approaches to the processing ofsmart city data, such as pre-processing, assessment and analysis,and their tasks. This article contains architectural solutions andmethods used in the developed automated smart city dataevaluation system. There is also a detailed description of theintegration of the developed system with the DriveCloud cloudserver for receiving and storing smart city data.

Analysis of Machine Learning Approach for the mode model in SWC Mapping in Automotive Systems

Automotive technologies are ever-increasinglybecoming digital. Highly autonomous driving together withdigital E/E control mechanisms include thousands of softwareapplications which are called as software components.Together with the industry requirements, and rigoroussoftware development processes, mapping of components as asoftware pool becomes very difficult. This article analyses anddiscusses the integration possibilities of machine learningapproaches to our previously introduced concept of mappingof software components through a common software pool

Classification for Quality Assessment of the User Interface and its Application in the Development of Web-applications

The article considers a classification for validation and quality assessment of the user interface (UI) from the point of view of the main aspects of design and its application in the development of web-applications. The problem with inaccurately crafted user interface requirements is relevant and as a result, developers often have to redesign the interface and architecture of the application. The article analyzes the role and place of UI in the architecture of client-server applications, analyzes aspects of UI design, on the basis of which the classification is formed. The classification is used to analyze UI design oversights of the developed web-applications for BPMS “Fireproof Corporation” company. Based on the results of UI validation, a set of typical UI design oversights has been added.

EEG representing on brain surface using volume rendering

Brain research is challenging. One of the standard research methods is electroencephalography (EEG). As a rule, this study is presented in the form of graphs. This article describes an approach in which this data is mapped onto a brain model generated from a magnetic resonance imaging (MRI) scan. This allows you to look at the EEG study from a different point of view. An MRI scan will also allow you to take into account some of the features of the brain. This is an advantage over mapping just to a brain template. This non-invasive system can be implemented to monitor the patient in real-time, for example, during space flight.

Towards Collection of Smart City Data for Cloud Storage Using UAVs

The article describes the methodology and process of collecting smart city data using drones for cities that do not have a sufficiently developed infrastructure. For storage and subsequent analysis of data, a cloud server is required; TUC DriveCloud is presented as an example of such a server in the article. Traffic analysis and building inspection are described as examples of drone data collection tasks. The advantages and disadvantages of collecting data using a thermal imaging camera are also discussed using the example of the problem of detecting and tracking the movement of people.

Intelligent Approaches for Smart Cities

The Embedded Self-Organizing System Journal (ESSJ) is calling for contributions to an issue on the subject of "Smart Cities". This issue focuses on the wide variety of application areas for the sustainable development of smart cities.