Real-time Road Lane Boundary Monitoring System using Machine Learning

Aim: to develop directions and determine the stages of development of intelligent transport systems in Russian agglomerations, taking into account their essential features, the effects they generate and the existing problems. Materials and Methods: in order to achieve the research goal, at the first stage, systematization and comparative analysis of scientific results obtained by Russian and foreign scientists on the chosen topic are used. At the second stage, the authors use the methods of summarizing and grouping the results obtained, statistical analysis. At the third stage, the generalization method is used to formulate recommendations for the development of intelligent transport systems. The information base of the study was materials from open sources, periodicals, as well as Internet resources, including analytical reports and materials from the Federal State Statistics Service. Results: The main scientific result is to determine the directions and stages of development of intelligent transport systems in Russian agglomerations based on the tasks they solve in the field of urban passenger transport management. In addition, a list of effects generated by the development of intelligent transport systems has been defined. The problems hindering the development of intelligent transport systems in Russian agglomerations have been identified. Conclusion: as a result of the research, the authors have formed the stages of development of intelligent transport systems, suggesting their integration with the concept of "Mobility as a service" in Russian agglomerations. The results can be used by both passenger transport companies and state executive authorities in the regions of the Russian Federation.

This study presents basic concepts and applications of Artificial Intelligence System (AIS) for development of intelligent transport systems in smart cities in India. With growing urbanization the government has now realized the need for developing smart cities that can cope with the challenges of urban living and also be magnets for investment in India. Transport system in smart cities should be accessible, safe, environmentally friendly, faster, comfortable and affordable without compromising the future needs. The Indian cities largely lacks of Intelligent Transport System in India and there are various problems such as inefficient public transport system, severe congestion, increasing incidence of road accidents, inadequate parking spaces and a rapidly increasing energy cost etc. Therefore, development of Intelligent Transportation System is essential for smart cities due to concerns regarding the environmental, economic, and social equity. Artificial Intelligence is a key technology to resolve these issues. Therefore, there is an urgent need to adopt Artificial Intelligence system for development of Intelligent Transport System to better understand and control its operations in smart cities. Hence, the main objective of this study is to present some basic concepts of Artificial Intelligence and its applications for development of Intelligent Transport System in smart cities in India. This study concludes that Artificial Intelligence system needs to be adopted to develop smart public transport system, intelligent traffic management and control, smart traveller information system, smart parking management and safe mobility & emergency system in smart cities. It is expected that this study will pave the way for development of Intelligent Transport System in smart cities in India.

Developing self-driving cars is an important foundation for the development of intelligent transportation systems with advanced telecommunications network infrastructure such as 6G networks. The paper mentions two main problems, namely, lane detection and obstacle detection (road signs, traffic lights, vehicles ahead, etc.) through image processing algorithms. To solve problems such as low detection accuracy of traditional image processing methods and poor real-time performance of methods based on deep learning methods, lane and object detection algorithm barriers for smart traffic are proposed. We first convert the distorting image caused by the camera and use a threshold algorithm for the lane detection algorithm. The image with a top-down view is then determined through the extraction of a region of interest and inverse perspective transform. Finally, we implement the sliding window method to determine pixels belonging to each lane and adapt it to a quadratic equation. YOLO algorithm is suitable for identifying many types of obstacles for identification problems. Finally, we use real-time videos and the TuSimple dataset to perform simulations for the proposed algorithm. The simulation results show that the accuracy of the proposal for detecting lanes is 97.91% and the processing time is 0.0021 seconds. The accuracy of the proposal for detecting obstacles is 81.90%, and the processing time is 0.022 seconds. Compared with the traditional image processing method, the average accuracy and execution time of the proposed method are 89.90% and 0.024 seconds, which is a strong antinoise ability. The results prove that the proposed algorithm can be deployed for self-driving car systems with a high processing speed of the advanced network.

This article introduces the constitution of intelligent transport system and the development of intelligent transport system in home and abroad. At the same time it analyzes the current situation development and suggestion of intelligent transport system in national.

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird’s-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm.

No matter when, transportation construction is very important for people's production and life. However, with the development of society, various traffic problems have emerged. The traditional transportation system is unsustainable. At the same time, under the active role of information technology, intelligent transportation systems can be produced and developed. Under this background, this article specifically analyzes the reasons for the development of intelligent transportation systems in China from three aspects: people, vehicles, and roads, and then describes the current development status of China's intelligent transportation systems, and analyzes the problems faced in the development of intelligent transportation system in China, finally puts forward some suggestions for promoting the better development of intelligent transportation system in China.

The development of Intelligent Transportation System (ITS) will transition into a next level development of variously new requirements coupled with the new generation wireless communication technology. In this paper, the process-oriented structured system analysis is extended on the existing intelligent transportation architecture to establish an intelligent transportation system based on the new generation wireless communication technology, and further in-depth analysis will be performed to discuss the required additional capabilities of network, services and application support in the view of user, function, information, connection and communication. In order to comprehensively understand the technical characteristics and development of the new generation intelligent transportation system, it lays a foundation for implementation of the new generation ITS technology.

The development of Intelligent Transportation System (ITS) will transition into a next level development of variously new requirements coupled with the new generation wireless communication technology. In this paper, the process-oriented structured system analysis is extended on the existing intelligent transportation architecture to establish an intelligent transportation system based on the new generation wireless communication technology, and further in-depth analysis will be performed to discuss the required additional capabilities of network, services and application support in the view of user, function, information, connection and communication. In order to comprehensively understand the technical characteristics and development of the new generation intelligent transportation system, it lays a foundation for implementation of the new generation ITS technology.

Transportation is a driving force behind economic development and well-being of all people around the world. An urban transport system is one of the most critical infrastructures for urban development. Impacts of a poor urban transport are manifested in terms of congestion, delays, pollution, accidents, high-energy consumption, low productivity of resources, community severances and inadequate access to the service. In urban areas, despite the construction of flyovers and roads, the roads continue to face traffic congestion, environmental pollution and lack of mass transportation facility resulting in heavy shift of commuters to intermediate and private vehicle ownership. Therefore, it is necessary to make transport system attractive and user friendly in terms of safety, reliability, travel time and comfort, which can be achieved using intelligent transportation system (ITS). This study has been intended to develop a methodology for development and operation of intelligent transportation system namely Presto ITS mobile application. The Android application, Presto ITS, developed works along with the Urban Traffic Data Management Centre to help reduce the congestion on the road networks by allowing the users to make informed decisions about route choices. This helps in reducing further inflow of vehicles in an already congested road network and thus allowing distribution of vehicles among the different routes resulting in lesser overall congestion. This system when installed on an overall city wide basis will drastically help in reducing the congestion and evenly distributed traffic.

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder–decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder–decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.

Transport plays a fundamental role in the economic development for big cities. Quito, the capital of Ecuador, presents serious mobility problems, with a significant amount of traffic both in the city center and in its main access roads. The detailed proposal in this article aims to link Quito with several new generation technologies such as Internet of Things (Iot), Sensor Web Enablement (SWE) standard for heterogeneous sensor communication, and a system of real-time notifications using the communications protocol Message Queue Telemetry Transport (MQTT). These technologies will be used for the development of an Intelligent Transport System (ITS) that takes advantage of the massive deployment of smartphones in the society as main sources of information, including Arduino and Raspberry modules to check efficient communication. This ITS bases its operation on the paradigm of the Crowdsensing, which allows to reflect more accurately the reality of the object of study when more collaborators use the system; reaching up to 30% improvement in the situational consciousness of the environment variable, the traffic.

License plate recognition is an active research field as demands sharply increase with the development of Intelligent Transportation System (ITS). However, since the license plate recognition(LPR) is sensitive to the conditions of the surrounding environment such as a complicated background in the image, viewing angle and illumination change, it is still difficult to correctly recognize letters and digits on LPR. This study applies Deep Convolutional Neural Network (DCNN) to the license plate recognition. The DCNN is a method of which the performance has recently been proven to have an excellent generalization error rate in the field of image recognition. The proposed layer structure of the DCNN used in this study consists of a combination of a layer for judging the existence of a license plate and a layer for recognizing digits and characters. This learning method is based on Multi- Task Learning (MTL). Through experiments using real images, this study shows that this layer structure classifies digits and characters more accurately than the DCNN using a conventional layer does. We also use artificial images generated directly for training model.

With the rapid development of intelligent transportation systems and autonomous driving technologies, vehicle recognition under adverse weather conditions has become a critical issue to be addressed. This study focuses on the optimization of intelligent vehicle recognition algorithms for rainy environments, proposing an improved vehicle detection model by integrating deep learning and multi-modal data fusion techniques. By introducing a spatial attention mechanism, an image restoration module, and a multi-sensor data fusion strategy, the algorithm's recognition accuracy and robustness under complex conditions such as low visibility and raindrop interference have been significantly enhanced. Experiments conducted on the DAWN dataset validated the effectiveness of the model, showing that the optimized algorithm achieves a mean average precision (mAP) of 89.7% in rainy environments, representing a 12.3% improvement over the traditional YOLOv5 model. This research provides theoretical support and technical solutions for the practical application of intelligent transportation systems under adverse weather conditions.

In the past decade, due to increasingly challenging traffic conditions in large cities, pollution and safety issues, the development of intelligent transportation systems gained a significant interest due to the potential they present for traffic optimization. The current paper describes the MobiWay Integrated Platform for the development of Intelligent Transportation Systems which provides the infrastructure required for creating location-based applications by providing traffic data in real time as an input. In addition to that, this paper presents related work in the field of traffic management solutions and envisions future work in the development of the MobiWay platform.

The paper gives an overview of the past development and implementation of Intelligent Transport Systems in pedestrian traffic. Starting from recent development projects and methodological approaches to the development of ITS, an overview of the existing technologies of implementing ITS in pedestrian traffic is given, and some solutions are proposed that would be feasible in the Republic of Croatia, as well as in other transition countries. As the most significant functions of integrated systems within ITS, the possibilities are presented of locating pedestrians in the network, pedestrian guidance (especially the blind and low-vision persons as well as persons in wheelchairs), provision of on-trip information, etc. Special focus is on the area of pedestrian protection in traffic (avoidance of accidents) using devices fitted onboard vehicles and embedded in road infrastructure. The significance of timely integration of the pedestrian traffic in designing the future ITS architecture has been indicated. KEY WORDS: intelligent transport systems (ITS), pedestrian traffic, pedestrian guidance, real-time information, traffic safety