Research on Cooperative Vehicle-Infrastructure System Use Case Based on Edge Computing Technology

Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

Video application is a research hotspot in cooperative vehicle-infrastructure systems (CVIS) which is greatly related to traffic safety and the quality of user experience. Dealing with large datasets of feedback from complex environments is a challenge when using traditional video application approaches. However, the in-depth structure of deep learning has the ability to deal with high-dimensional data sets, which shows better performance in video application problems. Therefore, the research value and significance of video applications over CVIS can be better reflected through deep learning. Firstly, the research status of traditional video application methods and deep learning methods over CVIS were introduced; the existing video application methods based on deep learning were classified according to generative and discriminative deep architecture. Then, we summarized the main methods of deep learning and deep reinforcement learning algorithms for video applications over CVIS, and made a comparative study of their performances. Finally, the challenges and development trends of deep learning in the field were explored and discussed.

Unknown remaining time of signal phase at a signalized intersection generally results in extra accelerations and decelerations that increase variations of operating conditions and thus emissions. A cooperative vehicle-infrastructure system can reduce unnecessary speed changes by establishing communications between vehicles and the signal infrastructure. However, the environmental benefits largely depend on drivers’ compliance behaviors. To quantify the effects of drivers’ compliance rates on emissions, this study applied VISSIM 5.20 (Planung Transport Verkehr AG, Karlsruhe, Germany) to develop a simulation model for a signalized intersection, in which light duty vehicles were equipped with a cooperative vehicle-infrastructure system. A vehicle-specific power (VSP)-based model was used to estimate emissions. Based on simulation data, the effects of different compliance rates on VSP distributions, emission factors, and total emissions were analyzed. The results show the higher compliance rate decreases the proportion of VSP bin = 0, which means that the frequencies of braking and idling were lower and light duty vehicles ran more smoothly at the intersection if more light duty vehicles complied with the cooperative vehicle-infrastructure system, and emission factors for light duty vehicles decreased significantly as the compliance rate increased. The case study shows higher total emission reductions were observed with higher compliance rate for all of CO2, NOx, HC, and CO emissions. CO2 was reduced most significantly, decreased by 16% and 22% with compliance rates of 0.3 and 0.7, respectively.

As the trend of the new generation of intelligent transportation systems, the application of the cooperative vehicle-infrastructure system (CVIS) is important to reduce traffic congestion and accidents. For the cooperative vehicle-infrastructure system, the roadside unit (RSU) is a new application without enough deployment experience. Meanwhile the deployment of traditional mechanical and electrical infrastructure is generally based on experience and is easy to change the design. This paper analyzes the key technical parameters of RSU and the Digital Measurable Image (DMI) method in context of the CVIS. After collecting field data from a smart highway pilot project (the Deqing-Fuyang expanded Huzhou section in Chang-Shen Highway) and matching the DMI method with highway mileage pile numbers, an RSU deployment scheme based on DMI data was completed. It provides significant support for smart highway demonstration projects with cooperative vehicle-infrastructure intelligent management and control applications.

Real-time regional path decision method in cooperative vehicle infrastructure system

One of the main issues in Indonesia is congestion. The number of vehicles continues to increase and is less balanced by the development of transportation infrastructure, especially landlines, causing more complex problems. The Indonesian government needs an intelligent application system that can provide knowledge to unravel congestion. The problem is how to perform edge computing to reduce latency so that the highway monitoring application system runs in real time. This research proposes a basic design for a vehicle monitoring application system that can accurately recognize vehicles, count the number of vehicles, and propose an edge computation that brings computation directly to the data source. The dataset is a video of traffic in Bandung, Jakarta, and several other major cities. The images in the dataset consist of 4,890 training images, 467 validation images, and 231 testing images. In the proposed model, the YOLOv5 and YOLOv7 architectures accurately detect and count vehicles. The test results show a mAP value of 99.1% with an IoU threshold of 50%. Other results include a precision value of 96.2% and a recall of 97.7%. The proposed model can accurately monitor vehicles and reduce latency with an edge computing approach.

As one of the most important part of the modern comprehensive transportation system, highway plays a critical role in national traffic system, which bears the most of traffic load. Whereas, ramp areas may become bottleneck areas of highway if the control is inappropriate or ineffective. The cooperative vehicle infrastructure system (CVIS) brings great opportunities for ramp control to address many tricky problems, among which the merging control of on-ramp vehicles is one of the most prominent problem related to traffic efficiency and safety. In this paper, we apply the technology of cooperative vehicle infrastructure control in the highway ramp area to enhance the traffic efficiency and safety, and propose a merging control method for on-ramp vehicles. At last, a simulation is conducted to evaluate this control method.

Collaboration between infrastructures and connected and autonomous vehicles (CAVs) at unsignalised intersections has emerged as an effective strategy for improving transportation efficiency. To enhance the safety and efficiency of CAVs passing through unsignalised intersections, this paper proposes a cooperative vehicle-infrastructure system (CVIS) based multi-vehicle trajectory planning architecture. The architecture comprises a roadside planner and vehicle planners. The roadside planner formulates the problem of generating multi-vehicle trajectories as an optimisation task and addresses it using an improved particle swarm optimisation (PSO) approach along with a space-time voxels strategy to determine the times for vehicles to reach the intersection and the trajectories passing through it. Then, the vehicle planner plans according to the results of the roadside planner. The results show that our approach enables potentially conflicting CAVs to pass through unsignalised intersections without stopping and waiting, ensuring safety while improving traffic efficiency.

Fully cloudified mobile network infrastructure, which is featured by the joint deployment of heterogeneous cloud radio access networks and edge computing, will successfully cope with the data deluge by densely deploying virtualized wireless base stations and servers while providing the system design with high flexibility, reliability, availability, and scalability. On the other hand, the massive replication of the wireless and computing infrastructure will significantly increase the energy footprint to prohibitive levels. In order to gain actionable insights on energy-efficiency for a fully cloudified mobile network infrastructure, this paper first presents a comprehensive survey of the recent research breakthroughs on each building block of the system, namely: remote radio heads, baseband unit pool, fronthaul, backhaul, HetNet, and edge and cloud computing. Next, we consolidate the discussion with the challenges and open issues of a joint operation.

Edge computing is a novel network architecture that is in proximity to the end devices in an Internet of Things (IoT). As the IoT becoming a major factor in our daily life, provisioning a low response time of the services to IoT users through edge computing is an important problem. Caching necessary program data for the task in an edge node effectively reduces the response time of the computation task. However, due to the increase of IoT users and devices, it is noteworthy that limited-resource edge nodes would receive a number of tasks, having a heavy burden of processing the requests. Therefore, the limited resource and caching space at cloudlet need the careful design of the caching algorithm to utilize the space of multiple edge nodes and relieve the burden of computations. In this paper, we propose a cooperative program caching system that makes different edge nodes cooperatively store program data and cache the replicas of the data requested frequently to handle a number of requests from IoT users. In particular, we develop a cooperative caching algorithm that caches the appropriate number of data replicas depending on the number of requests on each cloudlet and the popularity of the data to minimize the response time. The simulation results show that the proposed cooperative caching algorithm can effectively reduce the response time for IoT users compared to other existing algorithms.

With the increase in natural gas consumption, distributed natural gas supply and transaction have become new development goals of the industrial Internet of Things (IoT) for natural gas. However, there are obvious disadvantages of the existing natural gas pipeline network in aspects of infrastructure warning, multilevel data transmission, automatic transaction, and security. Emerging technologies, such as blockchain, edge computing, and AI have been introduced to address these shortcomings. This article proposes Smart Micro-GaS, i.e., the concept of a cognitive micro natural gas industrial ecosystem based on mixed blockchain and edge computing. Three aspects, multilevel, multiview, and multidimension, are put forward for its design and deployment. Then, based on the most important smart contract algorithm in blockchain, a mixed transaction model for natural gas is established. Finally, a case analysis is conducted on a smart natural gas testbed for data prediction and the proposed smart contract algorithm. The framework proposed in this article makes the natural gas data have multilevel liquidity and realizes diversified transactions.

In the current enterprise management system, the coupling relationship between enterprise management and operation and maintenance database is not considered, which leads to many system vulnerabilities, slow running speed, and long response time. An enterprise management system based on edge computing architecture is designed. According to the characteristics of mobile edge computing, we design the architecture of the edge computing platform, integrate the MEC virtualization infrastructure layer, MEC platform layer, and MEC application layer, construct the edge computing model, reflect the coupling relationship between enterprise management and operation and maintenance database, calculate the delay vector of the enterprise management server, and complete the parameter calculation of enterprise management and operation and maintenance system. This paper analyzes the feasibility, objectives, and users and functional requirements of the enterprise management system, designs the functional structure of the system, realizes the design of the public module, function menu module, and login module, adopts the SQL database management system, and designs the database and data table, so as to realize the design of the enterprise management system based on edge computing architecture. Experimental results show that the proposed method has fewer system vulnerabilities and can effectively speed up the system operation speed and shorten the system response time.

With the improvement of Internet of Things (IoT) and edge computing, the smart agricultural system is driven by data produced by the different sensors and smart computing devices in the agricultural land. A new methodological paradigm high-performance edge computing is incorporated with blockchain implemented precision agriculture system to improve the data processing operation related with resource management. Edge computing nodes collect and analyze the sensor data locally without transforming to the remote centralized cloud server, which rises data processing speed and reduce the network latency. The blockchain technology is incorporated with machine learning algorithm to maintain secured and protected distributed database for storing smart farm details like pH, soil moisture, temperature, crop management, humidity, and water irrigation level. The proposed system improves the productivity of food items and performance of the smart agricultural system by providing useful information to the farmers to make time-based decision about the land and increase the profit.

The premise of implementing an effective traffic control strategy is the accurate traffic state recognition. In the existing study, traffic state recognition methods were processed by using statistical characteristics and long-term scale detection of field traffic data. Hence, the dynamic characteristics and subtle changes in traffic flow were easy to overlook. At present, more and more advanced traffic detection technology provides reliable and accurate data for measuring and distinguishing the state of urban road traffic, such as the cooperative vehicle-infrastructure system, wide-area radar technology, and 5G technology. This study proposes a novel method called HTSI (High Precision Traffic State Identification Method), which is based on the advanced detection technology in traffic state recognition at the intersection: The raw data used for intersection traffic state recognition is high-precision detection data of tracking characteristics, which make the data look like a picture of the intersection at God’s perspective. To this end, we construct an image model for intersections and implement image feature extraction in a way that is different from traditional image processing. Then, the traffic state recognition problem at the intersection is translated into an image searching problem with tags. The image searching is realized by the hashing algorithm. Finally, the comprehensive experiments prove that the proposed method is more accurate and finer than other methods.

A distributed dynamic route guidance approach based on short-term forecasts in cooperative infrastructure-vehicle systems