Field Of Intelligent Transportation Research Articles

Analysis on the Application of Image Processing in UAV and Autopilot

The purpose of this paper is to analyze the application of image processing in UAV and autopilot. UAV and autonomous driving technology are important development directions in the field of intelligent transportation and agricultural production, and image processing, as one of the core technologies supporting its perception and decision-making, plays a vital role. For UAV, in agricultural plant protection and natural disaster monitoring, the rapid evaluation and identification of farmland growth status and disaster situation can be realized through image processing technology, which provides important support for crop production and rescue work. For the automatic driving system, image processing technology can detect and track the road environment, traffic signs and pedestrians, and improve the driving safety and comfort of vehicles. However, image processing technology also faces some challenges in application, such as complex environmental conditions and real-time requirements. In the future, through the improvement of sensor technology, algorithm optimization and data sharing, the application of image processing technology in UAV and autonomous driving will be continuously improved, bringing more innovation and development opportunities for intelligent transportation and agricultural production.

Research on Data Fusion and Analysis Based on Artificial Intelligence Algorithm in Sensor Networks

This paper discusses the data fusion and analysis technology based on artificial intelligence algorithm in sensor networks. Firstly, the basic concept, composition and structure of sensor network are summarized, and its characteristics and challenges are analyzed. Then, the basic principle of data fusion technology and its application in sensor networks are introduced, and the basic concept, classification and application of artificial intelligence algorithm in data fusion are described in detail. On this basis, this paper constructs a data fusion framework based on artificial intelligence, and proposes a new data fusion method combining deep learning and machine learning algorithm. This method can effectively improve the accuracy and reliability of sensor data and reduce data redundancy and noise interference. In addition, the thesis also conducts in-depth research on the application scenarios of data fusion and analysis in sensor networks, including Environmental monitoring, intelligent transportation, medical health and other fields. The effectiveness and practicability of the proposed method are verified by experiments and case studies. Experimental results show that the data fusion technology based on artificial intelligence algorithm can significantly improve the data processing ability and analysis accuracy of sensor networks, and provide strong support for related applications. Finally, this paper summarizes the main conclusions of the study, and points out the shortcomings of current research and future prospects. In the future, the data fusion method based on artificial intelligence will be further optimized and improved, and its application in more fields will be expanded, which will provide more powerful support for the development and application of sensor network technology.

Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation, as it can help optimize traffic management and improve travel efficiency. To improve prediction accuracy, a crucial issue is how to model spatiotemporal dependency in urban traffic data. In recent years, many studies have adopted spatiotemporal neural networks to extract key information from traffic data. However, most models ignore the semantic spatial similarity between long-distance areas when mining spatial dependency. They also ignore the impact of predicted time steps on the next unpredicted time step for making long-term predictions. Moreover, these models lack a comprehensive data embedding process to represent complex spatiotemporal dependency. This paper proposes a multi-scale persistent spatiotemporal transformer (MSPSTT) model to perform accurate long-term traffic flow prediction in cities. MSPSTT adopts an encoder-decoder structure and incorporates temporal, periodic, and spatial features to fully embed urban traffic data to address these issues. The model consists of a spatiotemporal encoder and a spatiotemporal decoder, which rely on temporal, geospatial, and semantic space multi-head attention modules to dynamically extract temporal, geospatial, and semantic characteristics. The spatiotemporal decoder combines the context information provided by the encoder, integrates the predicted time step information, and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model's accuracy for long-term prediction. Experiments on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5% on three common metrics.

The Impact of New Infrastructure Construction Investment on High Quality Economic Development in the Yangtze River Delta Region

This article aims to explore in depth the impact mechanism of new infrastructure investment on high-quality economic development in the Yangtze River Delta region, providing useful references for policy makers. The Yangtze River Delta region has invested a large amount of funds in the field of new infrastructure, including 5G networks, data centers, intelligent transportation, new energy, and other fields. These investments not only promote the optimization and upgrading of regional industrial structure, but also lay a solid foundation for high-quality economic development. The impact of new infrastructure investment on high-quality economic development is reflected in the following aspects: promoting traditional infrastructure reform and promoting the transformation of old and new driving forces; Promote supply side structural reform and optimize resource allocation; Promote diversification of financing channels and stabilize capital structure; Generate spillover effects, increase employment opportunities, and improve people's well-being. Finally, we propose several policy recommendations.

DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay

<p>In recent years, with the increasingly severe traffic environment, most cities are facing various traffic congestion problems, and the demand for intelligent regulation of traffic signals is also increasing. In this study, we propose a new intelligent traffic light control algorithm, dual experience replay light (DERLight), which innovatively and efficiently designs a dual experience replay training mechanism based on the classic deep Q network (DQN) framework and considers the dynamic epoch function. As results show that compared with some state-of-the-art algorithms, DERLight can shorten the average travel time of vehicles, increase the throughput at intersections, and also speed up the convergence of the network. In addition, the design of this algorithm framework is not only limited to the field of intelligent transportation, but also has transferability for some other fields.</p> <p> </p>

Enhancing traffic signal control with composite deep intelligence

Traffic signal control has always been a hot topic in the field of intelligent transportation. With the increasing complexity of urban traffic conditions due to urbanization, how to develop effective scheduling strategies to adapt to the changing traffic demands has become a key problem in current intelligent transportation. In light of this, this paper focuses on the traffic signal control problem at intersections and proposes a composite intelligent traffic signal control model based on heterogeneous graph neural networks with dual attention mechanisms and deep reinforcement learning. For the first time, the model incorporates the dual attention mechanism in graph neural networks into the traffic signal control, integrating graph neural networks with deep reinforcement learning techniques and traffic intersection scenarios. This allows for the construction of traffic condition models and the scheduling control of traffic resources, catering to the perception and decision-making needs in complex traffic environments. Firstly, the graph relationship representation of intersection resources is established, constructing the graph information structure for traffic flow and signal states. Then, a heterogeneous graph neural network is designed, incorporating both node-level and semantic-level dual attention mechanisms to characterize the traffic state and explore the relationships, enabling the extraction of explicit and implicit information in traffic intersections. Lastly, a deep reinforcement learning algorithm that combines Double Deep Q-Network (DDQN) and Dueling DQN is implemented to improve the algorithm's generalization and execution efficiency, enhancing the adaptability and stability of traffic signal scheduling in complex environments. Simulation tests are conducted on the SUMO simulation platform using real-world application datasets. Compared to four other similar traffic control model, the proposed model demonstrates performance advantages of more than 13% in terms of average reward, average delay, queue length, and waiting time. This validates the effectiveness of the proposed model.

Ecological and Real-Time Route Selection Method for Multiple Vehicles in Urban Road Network

Traffic congestion has been a hot topic of research in the field of intelligent transportation, which can be alleviated by efficient route navigation. Most of the existing route planning methods are non-negotiated algorithms, which do not take into account the route conflicts and collaborative relationships between multiple vehicles. Also, most negotiated algorithms have not been comprehensively considered dynamic route collaboration between vehicles, large-scale efficient computation, environmental pollution, etc. Therefore, an ecological multivehicle real-time route selection model (EMR2SM) for urban road networks is firstly proposed in this paper, which combines real-time traffic conditions of the road network with travel time, distance, and exhaust emissions as optimization indicators. In order to solve the large-scale computation problem of traditional negotiated algorithms, an adaptive multiswarm bee colony (AMSBC) algorithm is designed, which efficiently solves the multivehicle dynamic route selection problem. AMSBC searches the optimal route for each vehicle in parallel through multiple population division and self-adaption mechanism, to make multivehicle route selection reach Nash equilibrium. Compared with three non-negotiated optimization algorithms based on swarm technology, EMR2SM is verified by experiments that it improves the efficiency and accuracy of the optimal route selection for multiple vehicles and reduces vehicle emissions, which can effectively reduce traffic congestion and environmental pollution.

Carbon Dioxide Emission Reduction-Oriented Optimal Control of Traffic Signals in Mixed Traffic Flow Based on Deep Reinforcement Learning

To alleviate intersection traffic congestion and reduce carbon emissions at intersections, research on exploiting reinforcement learning for intersection signal control has become a frontier topic in the field of intelligent transportation. This study utilizes a deep reinforcement learning algorithm based on the D3QN (dueling double deep Q network) to achieve adaptive control of signal timings. Under a mixed traffic environment with connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), this study constructs a reward function (Reward—CO2 Reduction) to minimize vehicle waiting time and carbon dioxide emissions at the intersection. Additionally, to account for the spatiotemporal distribution characteristics of traffic flow, an adaptive-phase action space and a fixed-phase action space are designed to optimize action selections. The proposed algorithm is validated in a SUMO simulation with different traffic volumes and CAV penetration rates. The experimental results are compared with other control strategies like Webster’s method (fixed-time control). The analysis shows that the proposed model can effectively reduce carbon dioxide emissions when the traffic volume is low or medium. As the penetration rate of CAVs increases, the average carbon dioxide emissions and waiting time can be further reduced with the proposed model. The significance of this study lies in its dual achievement: by presenting a flexible strategy that not only reduces the environmental impact by lowering carbon dioxide emissions but also enhances traffic efficiency, it provides a tangible example of the advancement of green intelligent transportation systems.

CAGNet: Coordinated attention guidance network for RGB-T crowd counting

Estimating crowd density is a demanding task that has garnered significant research attention in urban planning, intelligent transportation, and other related fields. This study utilizes RGB and thermal images to leverage multimodal information and introduces a coordinated attention guidance network (CAGNet) for RGB-thermal (RGB-T) crowd counting. The framework enhances the expressive capabilities of backbone features by incorporating overall and local relationships through the information enhancement unit module, which utilizes the context coordination perception module for horizontal information mining, interactively compensating for the continuity of spatial information. Subsequently, it utilizes diverse multi-level information features for hierarchical intersection and progression, resulting in an accurate crowd counting density map. Experimental results on the RGBT-CC benchmark dataset demonstrate the robustness and effectiveness of CAGNet for RGB-T crowd counting. Furthermore, the proposed CAGNet can be extended to crowd density estimation and has achieved high performance on the ShanghaiTechRGBD and DroneRGBT datasets. The former dataset comprises paired RGB images and depth maps. The code and model for this research are available at https://github.com/WBangG/CAGNet.

Microelectronics innovation and implementation in intelligent transportation systems

Under the background of urbanization and rapid development of transportation, the innovation of intelligent transportation system has become the key to improving traffic efficiency, relieving traffic pressure, and solving traffic problems. With the continuous progress of microelectronics technology, its application in the field of intelligent transportation is becoming more and more eye-catching. This paper focuses on the innovation and realization of micro-electronic technology in intelligent transportation system, and discusses the application of micro-electronic technology in intelligent navigation, intelligent parking, traffic flow optimization, etc. Through a literature review approach, this study demonstrates how microelectronics technology can drive the development of intelligent transportation systems to improve the efficiency and sustainability of urban transportation. The research results show that microelectronics technology not only brings revolutionary changes to the field of intelligent transportation, but also provides accurate positioning and navigation functions in intelligent navigation systems, realizes more efficient parking process management in intelligent parking systems, and plays a key role in traffic flow optimization. Microelectronic technology has wide application prospects and a positive social influence in the field of intelligent transportation.

A Privacy-Preserving Service Framework for Traveling Salesman Problem-Based Neural Combinatorial Optimization Network

The traveling salesman problem (TSP) is one of the classic combinatorial optimization problems, which can be widely used in intelligent transportation and logistics field. Neural network has shown great potential in combinatorial optimization tasks. However, it faces privacy leakage when a TSP neural combinatorial optimization network and user's data are directly outsourced to a cloud platform to provide and request service. In order to address the issue, this paper proposes a privacy-preserving service framework for a TSP-based neural combinatorial optimization network called PPSF. We first protect the service provider's model parameters and users' data by different split methods in multiple cloud servers, providing a secure outsourced mode for the participators in the PPSF framework. Then, in the secure outsourced mode, a series of secure computation protocols are designed for the cloud servers to support performing the secure computing in each service task. Moreover, it can also protect the process that the cloud servers respond to users after data processing and achieve private service result recovery. Finally, we prove that the proposed framework can realize privacy protection for TSP-based combinatorial optimization service and verify its utility and efficiency by experiments.

Impact of driver's advanced reaction time in car-following model with optimal velocity deviation under V2X environment

The vehicle-to-everything (V2X) technology has made a significant advancement in the field of intelligent transportation in recent years. When operating a vehicle in a V2X environment, the driver can get real-time updates on the motion of nearby vehicles. In a real-world traffic situation, there is always some deviation between the actual and anticipated traffic information, and this deviation will undoubtedly have a significant effect on traffic flow. Drivers always maintain the flow of traffic by taking some time to assess and decide how the drivers in front of them are behaving behind the car in the traffic system. As a result, by taking into account the driver's advanced reaction time and optimal deviation in a V2X environment, a novel car-following model is developed and the effects of these parameters on the traffic flow are examined. In order to determine the stability requirements for the new model, the linear and nonlinear stability of the proposed model is examined using the perturbation methods. Studies have found that when the driver's reaction time and the velocity deviation are optimized, it can reduce the amplitude of the stability curve, thus enlarging the area of more stable motion. Also, the numerical simulation supports the theoretical research by showing that the new model may effectively reduce traffic congestion and improve the stability of traffic flow as the influence of these factors on the traffic flow increases.

Yolov5 Vehicle Detection Model in Fog Based on Channel Attention Enhancement

Vehicle detection in foggy weather plays an indispensable role in the field of intelligent transportation. This article proposes an improved YOLOv5 vehicle detection model based on the problems of insufficient detection accuracy and high fault tolerance of most algorithms in foggy weather. First, the AOD-Net network is used for defogging preprocessing of the original image. Then, the SE attention mechanism is fused in the C3 module of the Backbone feature extraction backbone network to adaptively allocate weight information, enhance the attention to important features, and reduce the impact of noise and irrelevant information. Finally, BiFPN is used in the Neck feature fusion network to replace the original PANet and enhance the model’s feature fusion ability. Experiments are conducted on the Cityscapes and RTTS datasets, and the results show that the improved YOLOv5 algorithm in this article has significant improvements in precision, recall rate, and average precision mean compared to the original model, with increases of 8.4%, 9.5%, and 9.2%, respectively. It can better adapt to vehicle detection tasks in foggy weather.

Multi-Modal Feature Pyramid Transformer for RGB-Infrared Object Detection

RGB-Infrared multi-modal object detection utilizes diverse and complementary information, showing some advantages in intelligent transportation field. The main challenge of RGB-Infrared object detection is how to fuse the two modalities. The difficulty of fusion is reflected in two aspects: 1) large visual differences between modalities make it difficult to learn effective complementary features, 2) some misaligned RGB-Infrared images increase the difficulty of fusion. To this end, based on feature pyramid commonly used in object detection, we propose Multi-modal Feature Pyramid Transformer (MFPT) to fuse the two modalities. The proposed MFPT learns semantic and modal complementary information to enhance each modal features via intra-modal feature pyramid transformer and inter-modal feature pyramid transformer. The intra-modal feature pyramid transformer enables features to interact across space and scales, improving the semantic representations of features in each modality. The inter-modal feature pyramid transformer conducts feature interaction between modalities, enabling each modality to learn complementary features from other modalities. Meanwhile, the inter-modal feature pyramid transformer can also learn distance independent dependencies between modalities, which are not sensitive to misaligned images. Furthermore, a local attention mechanism is introduced within different windows into MFPT to achieve efficient correlation between regions of different scales or different modalities. Experimental results on two RGB-Infrared detection datasets demonstrate the proposed method is superior to state-of-the-art methods.

Deep Learning and Geometry Flow Vector Using Estimating Vehicle Cuboid Technology in a Monovision Environment.

This study introduces a novel model for accurately estimating the cuboid of a road vehicle using a monovision sensor and road geometry information. By leveraging object detection models and core vectors, the proposed model overcomes the limitations of multi-sensor setups and provides a cost-effective solution. The model demonstrates promising results in accurately estimating cuboids by utilizing the magnitudes of core vectors and considering the average ratio of distances. This research contributes to the field of intelligent transportation by offering a practical and efficient approach to 3D bounding box estimation using monovision sensors. We validated feasibility and applicability are through real-world road images captured by CCTV cameras.

3D pedestrian localization fusing via monocular camera

In the field of intelligent transportation, autonomous driving technologies, especially visual sensing solutions have attracted increasing attention in recent years. There are still some challenges in pedestrian location based on the monocular camera, as the pedestrian is a non-rigid object and its depth information cannot be obtained from the monocular camera easily and accurately. In this paper, a pedestrian location framework based on monocular cameras is proposed. The framework consists of three parts: coarse positioning, auxiliary information generation and information fusion. In the part of coarse positioning, the human skeleton information is obtained from the monocular images and a light-weight feed-forward neural network is used to predict the pedestrian position based on the skeleton information. In the part of auxiliary information generation, pseudo-LiDAR points with pedestrian depth information are generated from the monocular images through an auxiliary network. Finally, the outputs of the above two parts are fused to achieve the pedestrian location. The experimental results on KITTI dataset show that our method has achieved better performance than other methods.

Traffic Sign Detection Based on Improved YOLOv5

<p>As a popular research direction in the field of intelligent transportation, various scholars have widely concerned themselves with traffic sign detection However, there are still some key issues that need to be further solved in order to thoroughly apply related technologies to real scenarios, such as the feature extraction scheme of traffic sign images, and the optimal selection of detection methods. For the purpose of overcoming these difficulties. This paper proposes a YOLO-based traffic sign detection framework. Firstly, a lightweight convolution attention mechanism is embedded into the backbone network to obtain the information of space and channel; Secondly, the multi-scale awareness module is used to replace large convolution with 3×3 convolution superposition to improve the receptive field area of the object in the model and enhance the feature fusion performance of the model; Finally, CIoU is used as the loss function of the bounding box to locate the experimental object with high precision. The experimental results show that on the CCTSDB data set, the MAP of this method reaches 91.0%, which is 3.5% higher than the original YOLOv5. Compared with other mainstream object detection algorithms, it has a certain degree of improvement, which proves the effectiveness of this method.</p> <p> </p>

Hardware Circuit Design of Intelligent Induction Control Classified Garbage Can

This design is based on the STC89C52RC single chip microcomputer and the integration of various multi-function modules to complete the overall system control. It is composed of the core modules of humidity detection, relay drive, metal detection, intelligent alarm, MQ-2 smoke sensor, infrared sensing, ultrasonic detection, etc. We collect specific information through ultrasonic testing, and LCD1602 displays the current status; an infrared sensor and a smoke sensor are used for relevant monitoring; a humidity sensor and metal sensor automatically remind us to classify and put garbage; a relay analog control trash can automatically open and close. The system has the characteristics of ideal performance, low cost, convenient design, simple structure, and intelligence. It has found a feasible solution for the current environmental pollution problem to do garbage classification and treatment. It can play an important role in smart homes, smart campuses, intelligent transportation, telemedicine, and other fields, and will have a broader market prospect and application fields.

English

In the field of vision, applying accurate- time tracking technology in sequence images is of excellent use value to many fields, for example. It has broad prospects for development in alcohol and tobacco, passenger flow statistics, traffic surveillance, etc. In this paper, a Visual tracking simulation is carried out on corresponding detection, extraction, recognition and tracking of the moving target in an image sequence to obtain the moving parameters and trajectory. The tracked moving object performs further processing and analysis accordingly and provides a reliable database for the next more advanced task. Therefore, visual tracking plays an important role in the localization analysis of moving objects in the military, work, scientific research, intelligent transportation and other fields. The computer has undergone four development stages and made a qualitative leap from hardware to software. Stock price fluctuation represents current market trends and corporate development that can be used to determine whether to sell or acquire stocks. A stock market estimate is one of the most complex and vital duties because of the nonlinear or dynamic nature of the market. Therefore, according to the requirements of target tracking in video, a particle filter algorithm is selected to optimize the technology and is addressed for efficient outcomes through simulation. The news, blogs, sentiment analysis, and other media are connected to this relationship between supply and demand. Sentiment analysis stock market forecast focused on the stock market’s autonomous performance.

Dimensionality Reduction and Visualization in Public Management Research from the Perspective of Algorithm Recommendation

With the development of computer technology, algorithm recommendation systems have been widely used in intelligent transportation, urban planning, and other fields. Aiming at the current problems in public management in China, this paper proposes a dimensionality reduction calculation method, and uses the improved particle swarm optimization algorithm to analyze and evaluate the dimensionality reduction process. With the expectation of providing some reference value and ideas for solving practical application problems, a corresponding relationship equation is established with the minimum expected value as the objective function to solve the corresponding optimal solution, the evaluation results of system performance indicators and comprehensive scoring criteria under relevant parameters and constraints are given. The test results show that the visualization effect of the dimensionality reduction visualization model for public management research based on the algorithm recommendation perspective is over 90%.