Journal of Nuclear Energy Science & Power Generation TechnologyISSN: 2325-9809

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Review Article, J Nucl Ene Sci Power Generat Technol Vol: 10 Issue: 9

Cross-Vehicle Communication Based on Packet Network Theory

K Kalaiselvi1*, C Bazil Wilfred2, E Bijolin Edwin3, Nancy Arya4, Purvansh Jain5 and K Sentamilselvan6

1Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur  Chengalpattu, Tamil Nadu, India

2Department of Mathematics, Karunya Institute of Technology and Sciences, Coimbatore, India

3Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

4Department of Computer Science and Engineering, Shree Guru Gobind Singh Tricentenary University, Gurugram-Badli Road, Haryana, India

5Department of Computer Science and Engineering, JAIN (Deemed-to-be-University), Bangalore, India

6Department of Information Technology, Kongu Engineering College, Perundurai, Erode, India

*Corresponding Author: Kalaiselvi
Assistant Professor, Department of Networking and Communications
SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India

Received:August 31, 2021 Accepted : September 15, 2021 Published : September 22, 2021

Citation: Kalaiselvi K, Bazil WC, Bijolin EE, Arya N, Jain P, et al. (2021) Cross-Vehicle Communication Based on Packet Network Theory. J Nucl Ene Sci Power Generat Techno 10:9.


Inter-vehicle information exchange, as a function of the artificial intelligence-based transport has emotionally attached consideration through both academic and industrial in several countries, including the United States, the European Union, and Japan. The capacity based on cross communications to broaden the view of drivers as well as on equipment (such as radar or sensors) has been the most significant aspect of the technology, and it has the potential to enhance road traffic performance and comfort. Automatic braking systems of this kind are intended to help operators with both the course of the training to prevent road accidents, increase speed traffic, and maintain a greater level of control over vehicles in general. These systems take advantage of the cars' communication capabilities to connect not just amongst themselves but also with other facilities. Many of the data is collected and analyzed to provide helpful services to the community.

Wireless appropriate network technologies, which allow for connection between cars and environmental devices, are extensively utilized in this field of application. It is the quality of the routing that supports the creation of a connection, and various routing have previously been studied and evaluated throughout the past; nevertheless, computations and assessments have nearly really been done with random movements as a consideration. Given that this technology is intended for Integration Connectivity and Systemic absorption of Vehicular Networks, it's indeed essential to first discover the car's location using Coordinates. The exactness of the location is improved through the use of available methods and object detection computer program optimizations.

Keywords: Smart transportation system; Localization; Inter-vehicular communication; Global positioning system; Wireless Ad-hoc networks


According to previous research, about 60% of car accidents might be prevented if the vehicle was alerted just one second before another incident happens. Road deaths have surpassed all other causes of death to become the leading cause of death. Technology that is now being developed seems to offer communication methods that are quicker, safer, and more dependable.

IVC, on the other hand, is a critical component of Intelligent Transport Systems (ITS) design, which is why it is being emphasized in this article. Using it, a driver (or his or her vehicle) may connect with some other drivers (or their cars) who are located beyond the motorist's or vehicle's line of vision. As a consequence, the evidence obtained via IVC may be used to enhance the overall performance and comfort of road traffic. Moving cars outfitted with communication equipment, on the other hand, are a perfect example of the long-awaited mobile ad hoc networks that have been anticipated. The computers in the network can make use of the enormous capacities (including both terms of space and power) of automobiles.

Such systems have the capability of transmitting across vast distances and having practically limitless lives. Additionally, many current protocols intended for mobile ad-hoc networks, as well as lessons gained from relevant literature, may be used, allowing for the implementation of findings that have hitherto remained in academic.

This kind of inter-vehicle communication may also be classified into the following categories. It is referred to as Vehicle-to-Vehicle (V2V) transmission when the information travels between two or more automobiles. The term "automobile" (V2I) refers to the interaction between it and an automobile as well as equipment. In this case, cars may interact using a specific protocol across the road to enable certain access to online access, based on cross chat, digital marketing, and so on.

The article explains how development changes, in conjunction with GPS, will be used to determine the position of the vehicle. Its precision is improved via the use of filtering methods and related psychological. At the end of the day, this registration should be assisted by algorithms for pedestrian identification as well as lane detection.

Background Study

The outline of a comparison of various MANET routing protocols based on their suitability for different applications [1]. It is suggested within [2] to examine the particular geographic area routing method. Connectivity is addressed in the developed framework, which includes a study of the various vehicular networks that may be employed for these types of communications. Moreover, it spoke about the need for a particular geographic area communications network to determine the precise position of the vehicles using GPS and screen characters to prevent collisions and manage traffic congestion [3]. Presents several different methods for the creation of controllable alternatives for complex embedded environments that must provide safety and liveness guarantees [4,5]. The potential causes and types of crashes, as well as the technologies available to prevent or minimize accidents, were discussed. Traffic management systems are addressed in terms of their relevance and significance about current circumstances, as well as the benefits and drawbacks of safety systems. Detailed in [6,7] are the underlying technological difficulties and economic incentives that underpin wireless position concerning policy, as well as potential methods for resolving the practical improving business performance issue [8]. Provides an overview of current developments in image stabilization technology [9]. Monte Carlo Localization (MCL) is a group of deterministic localization systems that are presented in this paper [10]. The development, reassessment, and comparative analysis of optimum control segmentation techniques and the Histogram equalization algorithm towards computer-controlled geomorphic network segment in a volcanic geotectonic environment are described in [11-16], as is the evaluation and reference of optimum condition detection algorithms and the Contrast enhancement algorithm in a hydrothermal depositional atmosphere.

The Work that is Being Considered

Using various methods, the suggested study provides inter-vehicular communication-application of network technologies, in which we shall witness Inter-vehicular connectivity and decongestion of vehicular-networks via the use of diverse policies and procedures.

The Mechanism for Assertion of Lanes

Using optimization techniques, we will construct a model whose goal will be to continually reduce the gap between our equilibrium speed and the outputs produced, which will be accomplished via the use of the least minimum mean square error estimate, as described above.

Then that will perform the movement shown in Figure 1. In the event of change congestion, its same automated system is customized and broadened because each car continues to treat the situation as something of an adaptive control problem and security patches its present role throughout every prediction step, broadcasting this relevant data to its neighboring countries peer set because every peer car might very well undertake. As a result of this knowledge but its safety and control assessments, it will choose whether or not to remain in the same lane but would not perform the approaching move shown in Figure 2.

Figure 1: Static traffic conditions.

Figure 2: Dynamic traffic conditions.

Vehicular enforcement methodologies, such as with the broken water bucket algorithm, address the issue of traffic jams by maintaining approximately equivalent to the information flow rate, the lane is a party must receive; nevertheless, if the information flow rate will become greater or equal to the throughput flow rate, the carriageway is overcrowded, as illustrated in Figure 3 (a-c) below.

Figure 3:a) Congested roads-steady-state rate b) Lane overcrowding (c) Road enforcement-new model.

Vehicle Identification and Monitoring

The first approach is associated with vehicles attempting to localize their behavior in response to colleagues. The information compiled from GPS Navigation systems and camera systems was often undecided and or moments later unobtainable; therefore it is extremely important that segmentation using Bayesian detection [12] be decided to carry out even though that the spectrum of error reduction from 3-10 meters to 2-8 cm of error. Extended kalman and particulate masking [13-15] are being used for data collection of vehicles. That kind of could be seen in our visualization snapshot in (Figures 4-7)

Figure 4: At the start of the tracking, there is an overpass and underperform.

Figure 5: Error decrease caused by a change in vehicle movement

Figure 6: Error decreases as the vehicle moves forward

Figure 7: The mistake has been precisely tracked using Kalman filtering.

Dijkstra's Algorithm-Adjusted

The vehicle's navigating position toward the next node has been sent. It is determined whether or not all of the nodes have been traversed. The target should be shown on the vehicle's liquid crystal display after all nodes have been bridged. In matlab, a dynamical arrangement of nodes is first created in Figure 8.

Figure 8: Matlab node assignment dynamic configuration.

At execution, each node should be given a configurable connection fee that compensates for fluctuating road conditions on roadways owing to busiest hour traffic or accidents in near real-time. As a result, the quickest route cannot be the only factor to consider when planning a trip as illustrated in Figures 9-12 following.

Figure 9: The best route from node 1 to node 7.

Figure 10:The best route from node 1 to all other nodes.

Figure 11: The best route from node 2 to node 6.

Figure 12: The best route from node 2 to all other nodes.

Detection of pedestrians

We are using open CV v2.4.3 to build a basic pedestrian protection system as shown in Figure 13.

Figure 13:HOG transform confirmation of pedestrian activity recognition.

The car serves as the client, while the apache tomcat database is installed on the user's personal computer. To execute real-time values of ground surrounding vehicles, the connection is created via the use of mobile internet. The configuration is shown in Figure 14 as follows

Figure 14:GPS module with transmitter with google API interconnects and lab view computing.

When the appropriate GPS frame is received, the information is parsed to get the vehicle's latitude and longitude coordinates. Upon those maps, the car is shown in Figure 15, where it is localized using monte-carlo localization and extended Kalman filter, which has been covered before [9].

Figure 15:GPRMC frame with google API-location marker on maps using lab view..

It is necessary to communicate the vehicle's current position to a monitoring control system, and as a result, the database management architecture must be implemented. Figures 16 and 17 illustrates this setup, which depicts the creation of client-server interaction between the different parties. It can recreate this with a web domain that collects all of the real-time data and allows for process administration from over the system.

Figure 16:Tomcat server application is accessible for customer service.

Figure 17:Client to server communication initiated and ready for data reception.

Detection of Lanes

The track in which we are now traveling is critical to understanding whether the lane congestion technique is to be used, or if we are to swerve to avoid any obstructions in the progress of our traffic flow. The legislation passed here quickly illustrates the procedures that we must take to identify the pavement markings and then proceed to the next stage. Figures 18 and 19 a depicts the picture that was utilized for the same purpose. The two most important filters are compared with the original picture. One being the Hough transform [10], which takes as inputs a binary map that may be produced through using the canny algorithm [11], which is itself derived from the canny algorithm. Illustrates how the canny algorithm runs a gradient on an image to identify abrupt changes in feature vectors. Those would be the contouring in the picture, which has been represented sometime in the outcome by a binary map, which can be seen in the previous figure.

Figure 18:Original image

Figure 19: Contour image.

This is followed by applying Hough Transform on the picture and running a probabilistic Hough transform over it to determine the termination of the individual line as shown in Figure 20.

Figure 20: Images after Hough and probabilistic Hough transform.

The bitwise combination of both pictures yields the final image, which can now be produced by multiplying them together. Depicts the final processed picture, which demonstrates the lane detection algorithm.


Vehicle communication is a rapidly emerging topic that is becoming a significant source of study. Because of improvements in portable and satellite communications technology, as well as the expansion of computing and processing capacity in cars. Furthermore, as autos grow more "intelligent," owing in part to the installation of intercommunications systems, new safety and privacy problems emerge, which academics and industry should examine. In conclusion, mathematical road traffic models are useful tools for the development of inter-vehicle communication systems since large-scale communication systems still require simulation.


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