The present invention generally relates to the field of intelligent transportation system. The invention particularly relates to system and method of detecting multitype vehicle using deep learning approach in next generation autonomous connected vehicles. The system provides better traffic management, vehicle tracking as well as time management.

(2) BACKGROUND OF THE INVENTION

Radio resources plays major role in different kind of applications such as V2V, V2X and next generation autonomous connected vehicles communication network. These resources are just like frequency band which allocate user for sharing information about traffic or other road entities. Several approaches were proposed by different researchers to solve the radio resource allocation issues and detection of vehicles.
In 2011, Sami M. Almalfouh propose radio resource allocation algorithm for maximizing the throughput of cognitive radio network. This algorithm includes power allocation steps, subcarrier allocation steps, or the like. It performs outstanding performance with different network parameters such as transmit power, interference threshold, or the like. In 2014, Georgios highlight importance of cognitive radio networks. They introduced signal-to-interference-and-noise ratio and focus on dynamic spectrum allocation. They examine the various optimization methods for CR networks. They present research challenges and issues for further investigation. In 2016, Quanfu fan modify the concept of faster RCNN for specific application and KITTI dataset. KITTI dataset includes large dataset for autonomous driver application. They tested its performance on 18000 real image. They show the great performance of Faster RCNN for detection of vehicle. In 2017, Haoran sun introduced a deep learning technique for wireless resource management. Main advantage of using DNN is that it offers high computational efficiency as compared to other iterative optimization algorithm.
In 2017, Shaoqing Ren introduced a region proposal network for detecting object. It is just like a fully convolutional network and use convolutional feature map. frame rate of this system has 5 fps on GPU. In 2017, Li Wang introduced concept of fast vehicle detection from traffic surveillance cameras, evolving box, a deep learning framework is used to generate anchor boxes. In 2017, Jorge E Espinosa present a study on vehicle detection model using deep learning. Result allows to obtain important conclusion regarding the detection quality, rate of failure and time incurred to complete the detection task. In 2017, Christian Eggert apply faster RCNN for the company logo detection. This approach is evaluating on flicker data set. Eggert examine the issues related to small objects and derive a relationship which describe the size of an object. In 2017, Myung Cheol Roh introduced a refining block for fast RCNN and merge it with faster RCNN into a single network. This scheme is applied to license plate detection and this is applicable only for 4 wheelers and it shows great improvement of RF-RCNN over faster RCNN.
In 2018, Changqing Luo propose a CSI prediction technique called OCEAN. It is utilized for verifiable information forecast. Luo proposed a learning system which is a blend of CNN and LSTM. This experiment result shows highly accurate CSI prediction. In 2018, Jia Guo design a deep neural network for predicting behavior related information from historical data. This model provides optimal solution through prediction. This scheme is heuristic, we have to design deep neural network only once and its results are preliminary. In 2018, Xudong Sun present face detection scheme using faster RCNN approach and proposed several effective strategies for resolving face detection task. In 2018, Yuhua Chen improve robustness of object detection. They use region proposal network in faster RCNN model. They use different datasets including cityscapes, KITTI, SIM10K for implementing this model. In 2019, changqing Cao proposed a modified version of faster RCNN for object detection. This algorithm has good performance on traffic sign.
In 2019, K.I Ahmed trained a deep learning technique using network data. This model provides optimal solution within less time. In 2019, Jin Gao propose a deep learning technique for solving the issue of power allocation, they use mini batch gradient descent (MBGD) algorithm for identifying batch size. This algorithm reduces computational overhead and provide good throughput. In 2019, Dingzhu Wen gave concept of the Radio resource management by presenting its principles as well as research opportunities in different directions. In 2019, Zhenwei proposes a multiadversarial faster RCNN framework for unrestricted object detection. This framework has an outstanding performance over other detectors. In 2020 N. Palanivel Ap propose an algorithm to identify the vehicle’s owner information from the license plate number. This system detects number plate in the vehicle through surveillance camera and identify the owner’s information such as owners name, address, mobile number etc. They capture the video and then applying ANPR (automatic number plate recognition) algorithm. This system is implemented and tested on real video.
In 2020, Wang Weihong highlight the problems which occur during license plate recognition. They highlight the license plate deflection, noisy plate images as well as fuzzy licenses plate’s issues. This research is basically a review of existing license plate recognition algorithm. It includes advantage, disadvantage and comparison between different detection algorithms etc. In 2020, Haijun Zhang introduced the concept of deep neural network for sub channel and power allocation. They generate the sample data by using iterative and machine learning algorithm. They propose the Lagrange dual decomposition technique, while deep neural network are utilized to solve the sub channel task and power control in NOMA heterogeneous network. In 2020, M. Shamim Hossain proposed the deep learning model for radio resource distribution. This model is used to predict future traffic congestion. In 2020, Rui Dong propose a cascade structure of deep neural network where first neural network calculate the allocation of bandwidth and second neural network satisfy the QOS requirement of bandwidth allocation. They establish a deep learning framework for solving mixed integer non-linear programming problem.
In 2020, Yifei Shen introduced the concept of graph neural network (GNN) to solve the problems of radio resource management. They use the concept of named message passing techniques for high computational efficiency. This method is highly reliable and can take less time for solving beamforming problem. In 2020, Rui Liu propose remote sensing object detection method. This method improves accuracy of detection and training convergence speed. It increases the accuracy of faster RCNN algorithm for multi-scale object. They use an algorithm named Soft-NMS (non-maximum suppression) for object detection. In 2020, Yang Liu reviewed about deep learning techniques. They highlight the challenges and solutions for object detection. Liu compare the performance of different methods of detecting objects such as YOLO V3 (You only look once), faster RCNN or SSD (Single Shot Detector).

In 2020, Bo Xiao develop data set of different images for training deep learning object detection algorithm. They discuss the performance of different object detection algorithm. In 2021, Vartika Agarwal reviewed about various technologies such as Li-Fi, RFID, VANET and LORAWAN. Such technologies establish connection between different vehicles and avoid any kind of traffic. In 2021, authors focused on working, executions, implementation and application of IOT in traffic management. This technology helps to reduce accident as well as road traffic. In 2021, authors highlighted secured scheduling technique for network resource management. Such techniques are able to complete the project within specified deadline. In 2021, authors investigated deep learning technique to improve radio resource management in next generation autonomous connected vehicles communication network. They trained the model using various algorithm of resource management including network data.
A number of different type of the tools and methods for replacing/changing the system for detecting a multitype vehicle are available in the prior art. For example, the following patents are provided for their supportive teachings and are all incorporated by reference: CN107316007B discloses a method for detecting and identifying multiple types of objects in a monitored image based on deep learning. The invention redesigns the network structure and corresponding various parameters on the disclosed SSD deep learning detection framework by utilizing the disclosed SSD deep learning detection framework, so that the disclosed SSD deep learning detection framework can quickly detect the objects concerned in the monitoring video image. Compared with the traditional image processing method, the method has the advantages that more effective and richer features can be automatically learned by adopting deep learning, so that the method has higher robustness. In summary, the method of the present invention can efficiently and quickly detect the object of interest in the image, and can be generalized to a more general object detection field. However, this prior art document does not appear to disclose identification of vehicle number and route optimization.

Another prior art document, CN106096531B discloses a kind of traffic image polymorphic type vehicle checking method based on deep learning, the feature of neural network is combined with algorithm of generating layered regions first, Area generation and two processes of regional determination are realized simultaneously using the convolutional layer of neural network, then it carries out being determined as that Area generation provides additional reference frame for the moving region of the discrete series image of special scenes using background model, and the update for combining vehicle detection result to carry out point situation to background model is corrected. However, this prior art document does not appear to identify number plate of a vehicle and unable to provide safety warning to the driver of the vehicle.

Another prior art document, US5448484A discloses a neural network-based system for detecting the presence of a vehicle within a traffic scene. The vehicle detection system comprises an apparatus for producing an image signal representative of an image of the traffic scene and a trainable neural network for identifying the presence of a vehicle within the traffic scene. The reference is directed to a method for detecting the presence of a vehicle within a traffic scene. The vehicle detection method includes the steps of producing an image signal representative of an image of the traffic scene, collecting a training set of these image signals, training a neural network from this training set of image signals to correctly identify the presence of a vehicle within the traffic scene and performing surveillance of the traffic scene with the trained neural network to detect the presence of a vehicle. However, this prior art document does not appear to disclose radio resource allocation by identifying vehicle number plate of the vehicle.
Another prior art document, CN107220603A discloses a kind of vehicle checking method and device based on deep learning, methods described includes: Vehicle sample image is obtained, and the vehicle sample image is pre-processed; Depth convolutional neural networks model is built by pretreated vehicle sample image; Vehicle image to be detected is detected using the depth convolutional neural networks model, and exports testing result. This method can avoid excessive compute repeatedly, so as to improve detection speed, and result in more preferable vehicle identification effect. However, this prior art document is does not appear to identify vehicle number and allocate radio resource based on the type of vehicle.
Another prior art document, CN107730905A discloses a kind of multitask fake license plate vehicle vision detection system based on depth convolutional neural networks, including video camera, traffic Cloud Server and deck false-trademark vehicle detecting system on urban road; Video camera is used to obtain the snapshot image data on each road in city, configures in the top of road; Traffic Cloud Server is used to receive the road video data obtained from video camera, and be submitted to deck false-trademark vehicle detecting system and detected and identified, deck false-trademark vehicle detecting system includes relating to illegal board and identification module, car plate legitimacy detection module, logical consistency detection module, the fine comparing module of car test mark and alarm notification book generation module based on Faster R CNN vehicle locations detection module, vehicle type recognition module, License Plate. And provide a kind of multitask fake license plate vehicle visible detection method based on depth convolutional neural networks. The present invention can fast and accurately lock deck false-trademark vehicle, effectively improve criminal investigation efficiency. However, this prior art document does not appear to disclose subscription based radio resource allocation based on the type of vehicle for crime prevention and to reduce traffic rule violation.
Another prior art document, CN108733051A discloses an advanced sensing of autonomous vehicle and responses. One embodiment provides the computing device in a kind of autonomous vehicle, and the computing device includes: Radio network device, for enabling and the wireless data connection of autonomous vehicle network; One group of multiple processor, including general processor and graphics processing unit, one group of multiple processor are managed for executing management of computing device with a pair execution for computational workload associated with the autonomous vehicle, and the computational workload is associated with the autonomous operation of the autonomous vehicle; And unloading logic, it is configured to execute on one group of multiple processor, the unloading logic is for determining the one or more autonomous vehicles being offloaded to one or more of described computational workload in the range of the radio network device. However, this prior art document is unable to provide subscription-based route optimization and safety warning to the driver of the vehicle.

However, above mentioned references and many other similar references has one or more of the following shortcomings: (a) Unable to provide road optimization; (b) Complex and less efficient; (c) Unable to avoid accidents on road; (d) Unable to provide safety warning; (e) unable to provide assistance to the driver; and (f) Unable to decrease traffic violation rule.
The present application addresses the above-mentioned concerns and shortcomings (and other similar concerns/shortcomings) with regard to detect a multitype vehicle.
There remains a constant need in society for a continuous flow of new and innovative novelty of a system to detect a multitype vehicle. It is in this context, that the subject invention is useful, not only to provide cheap and easy to operate/use but to provide system for detecting a multitype vehicle using neural network in autonomous connected vehicles.

(3) SUMMARY OF THE INVENTION:
In the view of the foregoing disadvantages inherent in the known types of technologies for detecting multitype vehicle using neural network in vehicles now present in the prior art, the present invention provides an improved system 100 for detecting multitype vehicle using neural network. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved system 100 for detecting a multitype vehicle using neural network in autonomous connected vehicles, which has all the advantages of the prior art and none of the disadvantages.
It is object of the invention is to provide a system 100 for detecting a multitype vehicle using neural network in autonomous connected vehicles, said system 100 comprising: a processor 102; a computer readable medium 104; a display 106; a user interface 108; an external device 110; a communication network 112; and a memory communicatively coupled to the processor 102. The memory stores processor instructions, which, on execution, causes the processor to detect said multitype vehicle for providing a subscription plan based on a type of vehicle.
It is another object of the present invention is to provide a method of detecting said multitype vehicle for providing said subscription plan based on the type of vehicle comprising: pre-training, said neural network, through a set of input data to identify a number plate of a vehicle from an image; receiving, by the system 100, a real time traffic data from a real time traffic database to identify said type of vehicle from a multitype vehicle; identifying, by the system 100, a license plate number of each vehicle present in said real time traffic data using said neural network, wherein said system 100 identifies a type of vehicle based on said license plate number; authenticating, by the system 100, said license plate number of each vehicle by matching with the transport database; identifying, by the system 100, radio resource requirement of each vehicle when a vehicle history is available in a vehicle travelling history database; and matching, by the system 100, said radio resource requirement of each vehicle in a radio resource management database through at least one of resource allocation, resource scheduling, resource levelling, or resource forecasting.
Yet another object of the present invention is that the vehicle is not allocated said radio resource when said license plate number is not available in transport database.
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Yet another object of the present invention is to provide an external device 110 comprises at least one of desktop, a laptop, a notebook, a netbook, a tablet, a smartphone, a mobile phone, or any other computing device.
Yet another object of the present invention is to identify a type of vehicle comprises at least one of four-wheeler, two-wheeler, three-wheeler, commercial vehicle, Motor vehicle, agricultural vehicle, Road Vehicle, Heavy Duty Vehicles, Defense Vehicles, or the like.
Yet another object of the present invention is that the service provider provides said subscription plan to each vehicle user based on said type of vehicle of said multitype vehicle.
Yet another object of the present invention is that the service provider provides a temporary plan to said vehicle user when a vehicle history is not available in said vehicle travelling history database.
Yet another object of the present invention is to store each of resource allocation, resource scheduling, resource levelling, and resource forecasting of each vehicle user in said vehicle travelling history database.
In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments and of being practiced and carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.

(4) BRIEF DESCRIPTION OF THE DRAWINGS
The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
Fig. 1 illustrates a system for detecting a multitype vehicle using neural network in autonomous connected vehicles, according to an embodiment herein.
Fig. 2 depicts an autonomous connected vehicle communication network in which the system is implemented to detect a multitype vehicle, according to an embodiment herein.
Fig. 3 depicts a flow diagram identifying a type of vehicle from the multitype vehicle using deep learning techniques, according to an embodiment herein.
Fig. 4 depicts an exemplary method of detecting a multitype vehicle using neural network in autonomous connected vehicles, according to an embodiment herein.

(5) DETAILED DESCRIPTION OF THE INVENTION
In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural and logical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
References will now be made in detail to the exemplary embodiment of the present disclosure. Before describing the detailed embodiments that are in accordance with the present disclosure, it should be observed that the embodiments reside primarily in combinations arrangement of the system according to an embodiment herein and as exemplified in FIG. 1 – FIG. 4.
In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the arrangement of the system according to an embodiment herein. It will be apparent, however, to one skilled in the art, that the present embodiment can be practiced without these specific details. In other instances, structures are shown in block diagram form only in order to avoid obscuring the present invention.
Fig. 1 illustrates a system 100 for detecting a multitype vehicle using neural network in autonomous connected vehicles, according to an embodiment herein. Radio resource allocation in next generation autonomous connected vehicles is a challenging role in an intelligent transportation system due to traffic congestion. Lot of time is wasted because of traffic congestion. Due to traffic congestion, user have to miss their important work. The system 100 provides radio resource allocation scheme using Efficient Multitype Vehicle Detection Algorithm (EMVD) so that user can utilize their time by taking the advantage of subscription plan. The EMVD in real time traffic database is using deep learning, its history will match in transport database and vehicle travelling history database. The system 100 indicates 99% accuracy for multitype vehicle detection. Subscription plans are allocated to the user on the basis of resource allocation, scheduling, levelling and forecasting. This scheme is better for traffic management, vehicle tracking as well as time management.
In particular, the system 100 may include an external device 110 (for example, a server, a desktop, a laptop, a notebook, a netbook, a tablet, a smartphone, a mobile phone, or any other computing device) that may be implemented in the user device to detect a multitype vehicle. Moreover, the system 100 may include a processor 102, a computer-readable medium 104 (for example, a memory), and a display 106. The computer-readable storage medium 104 may store instructions that, when executed by the processor 102, may cause the processor 102 to detect a multitype vehicle.
The computer-readable storage medium 104 may also store various data (for example, a set of input data, a real time traffic data, license plate number, vehicle travelling history database and the like) that may be captured, processed, and/or required by the system 100. The system 100 may interact with a user via a user interface 108 accessible via the display 106. The system 100 may also interact with one or more of an external device 110 over a communication network 112 for sending or receiving various data. The external device 110 may include, but are not be limited to a remote server, a digital device, or another computing system. The system 100 may be adapted to exchange data with other components or service providers using the communication network 112.
Next generation vehicular communication network is the network in which various vehicles are communicating and providing each other safety and traffic information. This network is helpful for avoiding an accident and traffic congestion. VCN prevent an accident by allowing vehicles to communicate with each other. If an emergency occurs with any vehicle, vehicle’s driver may simply receive a warning message so that an emergency vehicle has to treat urgently. The VCN is very effective for traffic management, providing assistance to drivers as well as improve fuel efficiency. The VCN prevent possible crashes. The system 100 can minimize up to 70 to 80 % accidents due to next generation autonomous connected vehicles.
Radio resources plays an important role in next generation autonomous connected vehicles communication network. Radio resources allows different users to share the same bandwidth so that they can get the message at same time of any emergency, location etc. such resources satisfy user need by providing relevant information and save the time of user. Radio resources includes beacons, bandwidth, and spectrum sensing as well as geo-location database. These resources are helpful for establishing the communication between vehicles. Such resources have a challenging role in an intelligent transportation system due to traffic congestion.
Traffic congestion is increasing day by day because of poor road infrastructure and increasing number of vehicles on road. As a result, user have to wait for a long time to reach their destination. Hence, the concept of radio resource allocation is used in the system 100. The system 100 help users to properly utilize their time. In the system 100, multitype vehicles have to identify from real time traffic database, its history will match in transport database (TD) and vehicle travelling history database. On the basis of user’s vehicle travelling history (VTH), subscription plan is allocated to the user on the basis of allocation, scheduling and levelling technique. User have to pay charges for it.
Main challenges come in whole process is multitype vehicle detection. Most existing method detect particular vehicle types such as two-wheeler, three-wheeler etc. but they failed to detect multitype vehicle. Multitype vehicle detection is important in the system 100 to find all information of vehicle just from one clue. Hence, in the system 100 FRCNN algorithm is used. The system 100 help users for effectively utilize their time. Idea of providing subscription plan to the user comes from those cities where there are a huge number of traffic and user have to wait for a long time. So, user can utilize their time by taking subscription plan and get better output.
Fig. 2 depicts an autonomous connected vehicle communication network 200 in which the system 100 is implemented to detect a multitype vehicle, according to an embodiment herein. Next generation autonomous connected vehicles communication network is the network in which various vehicles are communicating and sharing information about traffic and safety. Main objective of autonomous connected vehicles communication network is to provide safety and eliminate the excessive cost of traffic collision. In the vehicle communication network 200, car1, car 2, and car 3 may have vehicle to vehicle communication with each other. Moreover, each of the cars may be communicatively coupled with the road side unit. The roadside unit sends the data to the intelligent transportation server through the internet. The autonomous connected vehicles communication network 200 works for the different areas which may include but may not be limited to:
o Route Optimization – The system 100 helps drivers to get their destination with more efficiency. It delivers shortest route for reaching the destination quickly.
o Safety Warning – The system 100 enhances the capabilities of driver by monitoring the distance between vehicles it warns the driver if the distance decreases under a threshold.
o Preventing Road Accident – Next generation autonomous connected vehicles communication network successfully manage current accidents on the road. It detects road problems via sensors and inform driver about the road situation.
o Providing Driver Assistance – V2V communication gives drivers adequate control over their vehicles. The system 100 help with safe parking, providing possible alerts to drivers and avoiding unsafe drifts.
o Improving fuel efficiency – Next generation autonomous connected vehicles communication network 200 provide shortest routes for reaching the destination. It saves time and improve fuel efficiency.
Next generation autonomous connected vehicles communication network 200 is a technology which deliver numerous kinds of services such as road-safety, traffic efficiency, comfort driving etc. The radio resource management (RRM) plays major role in next generation autonomous connected vehicles communication network. It includes radio resources such as bandwidth, sensors, cellular network, transmitter etc. It concerns multicellular and multiuser network capacity issues. Hence, the RRM is important and it has a great impact on traffic congestion which may include:
o Crime prevention – the system 100 can easily identify information of vehicle just through license plate number. This information is helpful to police for solving cases.
o Traffic rule violation – If any vehicle breaks traffic rules then the system 100 easily identify it.
o Crowd counting – Through multitype vehicle detection, the system 100 can easily count the no of vehicles in a specified area.
o Tracking vehicle – the system 100 can easily track the specific vehicle.
o License verification – Through multitype vehicle detection, the system 100 can easily verify the license of a vehicle.
o Alerts – Know if any vehicle has broken down and dispatch help.
o Vehicle location – Through multitype vehicle detection, the system 100 can easily track the location of vehicle.
o Unauthorized Parking- If any car is parked on an unauthorized area, the system 100 can easily identify it.
o Video surveillance system – Multitype vehicle detection can be fused with existing video surveillance system so that the system 100 can get multiple result from a single standalone system.
o Decoding Facial Recognition –The system 100 can easily identify face of driver in vehicle focusing on particular feature of face such as eye, ear, etc. From face recognition, the system 100 can compare this data with already present data in database to match face with a name.
o Survey of Vehicles on-demand- the system 100 can easily identify the vehicle type which are on demand or mostly used by travelers when they want to travel.
o Autonomous Vehicle Detection – Due to this scheme, the system 100 can easily track the record of an autonomous vehicles (driverless car or robo car). These are the vehicles that have capability for detecting current circumstances of road and moving securely without human intervention.
o Public safety and security – the system 100 can easily count the number of vehicles on road within a specified time.
o traffic Analysis – the system 100 can easily count the no of vehicles on roads so that the system 100 can analyze traffic and take certain actions for reducing such traffic.
o Stolen Vehicle Recovery – Through multitype vehicle detection, the system 100 can easily detect the stolen vehicle. This is used for crime prevention.
RRM is the distribution of an electromagnetic spectrum into radio frequency bands. Radio resources are just like radio frequency bands which allocates to the vehicle for an entertainment purpose. These frequency bands may represent one communication channel and we can divide such channel into different frequency range. The system 100 can allocate spectrum to those vehicles which have 80% allocation of radio resources. These spectra help users to properly utilize their time. Spectrum unable users to make call from their devices, use unlimited bandwidth, continue their office work and do everything whatever they want. These services are allocated to the users on the basis of their vehicle travelling history. If user is unauthorized or unlicensed, then no radio resource is allocated. User can take services according to their usage and subscription plan. Mobile Telecom Operator, Railways, various defense services as well as governmental and other international organization use spectrum for communication. There are different types of spectrum allocation which includes No one may transmit, Anyone may transmit, Only authorized users of specific band may transmit, or the like.
RRM plays significant role in controlling power consumption. It includes transmission power management, allocation, forecasting, levelling and scheduling of radio resources. Main function of radio resource management is to effectively utilize radio resources within a network There are different schemes for radio resource management such as traffic control as well as congestion control.
o Resource Allocation - According to International Telecommunication Union, frequency allocation means allocation of frequency bands for the purpose of serving radio telecommunication services under specified conditions. It may vary from country to country. list of frequency ranges may be set by international agreements which is called band allocation. modes of allocations within each frequency band are called band plan. Some band plan may not be available or may have restriction on usage in certain countries or regions.
o Resource Scheduling – It means to allocate data packets to the user at each predefined transmission time interval. Main objective of resource scheduling is to focus on throughput, fairness, identifying delay as well as packet loss. Resource scheduling help to optimize the cost and duration of task. It helps to evaluate the efficiency of resources.
o Resource Leveling - It means shifting of resources. If there is a problem occur with transmission of data packets then by arranging additional data packets or resources the condition of packet loss can be avoided. It saves time and increase efficiency of tasks.
o Resource Forecasting – It means prediction of future resource requirement. The system 100 can predict the future resources needed for communication. It helps us to plan in advance about the requirement of resources.
RRM is important to ensure optimized use of available network resources. It manages, assign and release radio resources in a multicellular environment. RRM is required for interference management, admission control, congestion control, traffic scheduling, power control as well as self-optimizing networks. In Interference management, RRM cover large areas and reuse the same channel frequencies. In admission control RRM is required for traffic handling. RRM initiate packet transmission and guarantee QOS through bit rate and delay adjustments. RMM decreases packet loss, provide higher throughput, identify delay time and increase efficiency of tasks. RRM self-optimize various QOS control parameters. Self-optimize means shifting of resources if any problem occurs during transmission of data packets. RRM works for power allocation and enhancing the system performances. RRM increase network lifetime through optimized energy usage. RRM fulfill various users’ requirement for bandwidth with limited resource.
Fig. 3 depicts a flow diagram 300 of identifying a type of vehicle from the multitype vehicle using deep learning techniques, according to an embodiment herein. The deep learning is a computer software that identify neuron network in brain. It is an AI function which is used for object detection, speech recognition, language translation and making decision. Deep learning works best when the system have a huge number of input and output. The CNN is basically for image as well as non-image data. It can take 2D structure of an image and extract important properties of an image. It represents every image in the form of pixel value. Moreover, when an image is entered as input in Region based convolutional neural network (RCNN), the RCNN produces a set of bounding box as output. However, It is slow and time consuming process. If datasets have 4000 images, entire process will run 4000 times. It takes lot of time to train the model.
RCNN and fast RCNN both works for selective search. Both use an algorithm called edge boxes to generate region proposals. RCNN detector crop and resize image whereas fast RCNN detector process entire image. It reduces the total number of initial features for CNN. It uses SoftMax function which takes less time in comparison of RCNN. After modification of fast RCNN algorithm. It is known as faster RCNN algorithm. It uses region proposal network instead of selective search. For all region proposals in image, fixed length feature vector is extracted then extracted feature vectors are classified into fast RCNN and then detected objects with bounding boxes are returned. It resizes the input image and convert it into 600*1000 px.
Next generation autonomous connected vehicles communication network are the networks in which various vehicles are communicating and sharing information about traffic and safety. Main objective of next generation autonomous connected vehicles communication network is to provide safety and eliminate the excessive cost of traffic collision. Whereas CNN is used to detect objects. With the help of convolutional neural network, the system 100 detect license plate number and identify vehicle types so that service provider can provide subscription plan to the user based on their vehicle type. As will be appreciated, the system 100 may receive a real time traffic data at step 301. Further at step 302, the system 100 may select the region from the real time traffic data using RCNN. Further at step 303, the system 100 may identify boundary edge of each vehicle. Further, using RCNN, the system 100 may consider each image of the vehicle as input image at step 304. Further at step 305, the system 100 may identify the vehicle number from the input image. Further at step 305, the system 100 may match the identified vehicle number with the transport database to identify the type of vehicle. Hence, CNN is used for object classification and detection. Moreover, CNN, RCNN, Fast RCNN, FRCNN all these are used for feature extraction. Fast RCNN use selective search while FRCNN use region proposal network (RPN). RPN form a unified network which is used for object detection.
Fig. 4 depicts an exemplary method 400 of detecting a multitype vehicle using neural network in autonomous connected vehicles, according to an embodiment herein. At step 401, the system 100 may pre-training, said neural network, through a set of input data to identify a number plate of a vehicle from an image. Further at step 402, the system 100 may receive a real time traffic data from a real time traffic database to identify said type of vehicle from a multitype vehicle. Further at step 403, the system 100 may identify a license plate number of each vehicle present in said real time traffic data using said neural network, wherein said system 100 identifies a type of vehicle based on said license plate number. Further at step 404, the system 100 may authenticate said license plate number of each vehicle by matching with the transport database. Further at step 405, the system 100 may identify the radio resource requirement of each vehicle when a vehicle history is available in a vehicle travelling history database. Further at step 406, the system 100 may match said radio resource requirement of each vehicle in a radio resource management database through at least one of resource allocation, resource scheduling, resource levelling, or resource forecasting. By way of an example, the system 100 may collect the traffic data in real time. Further, to identify multitype vehicle (for example- 2-wheeler, 4-wheeler, commercial vehicle, and agricultural vehicle) from real time traffic database, the system 100 may scan license plate number using FRCNN algorithm and match its history in transport database. If vehicle’s details are found in transport database, it means user is licensed and if details are not found in transport database, it means user is not licensed. If user is licensed, the system 100 check its vehicle travelling history in vehicle travelling database. However, when user is not licensed, no radio resources are allocated. Further, when history of vehicle is available in vehicle traveling history database. Then fetch data from radio resource management database about the availability of plan. Then telecom service provider gives subscription plan to users as per usage. (Resource Allocation). Service provider allocate plan to users at each predefined transmission time interval (Resource Scheduling). Service provider allocate additional data packets to the user if required. (Resource Levelling). Hence, the system 100 can predict future resource requirement of user through resource scheduling and levelling. Then update its history in vehicle travelling history database. If history of vehicle is not available in vehicle travelling history database. Service provider offer temporary plan to user and update its history in vehicle travelling history database. For the subscription plan, traffic area can be represented by a Set K = {1, 2……...K}. DTRR Update their real time traffic information follows by traffic data. DTRR identify multitype vehicles such as Dt1, Dt2, Dt3, Dt4, Dc, Dr, Dm, DA, Dh, Df and matching its history in Dt. If information about vehicle is available in Dt. It means user is licensed. If user is not licensed it means no radio resources are allocated. After identification of licensed user, the system 100 can check its history in DVTH. If vehicle history is not available in DVTH. Then the system 100 go to DRR, Service provider offer temporary plan and its history will update in DVTH. If vehicles travelling history is available in DVTH. DVTH follows Dt. Then the system 100 go to DRRm. After that the system 100 can check again the availability of vehicles in DTRR If vehicle is available in DTRR. DTRR follows Dt can represent it in form of following equation:
Dt= 1, If vehicle is available in DTRR
Dt=0, If Vehicle is not available in DTRR
DRRm follows Dt. In DRRm Telecom service provider give plan to user as per usage (Resource Allocation).
DRRm(A) = Plan Availability (PA)*Cost of minimum Data plan (MDP)/ Left Plan (LP)
Resource Scheduling – It means to allocate data packets to the user at each predefined transmission interval.
Initial Plan = 0, d =1
DRRm(S) = Initial Plan + d
Resource Levelling – If user need additional data packets, the system 100 can add it in resource allocation scheme.
DRRm(L) = DRRm (A) + d
On the basis of resource allocation, scheduling and levelling the system 100 can predict the future resource requirement of a user which is known as resource forecasting.
It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-discussed embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The benefits and advantages which may be provided by the present invention have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the embodiments.
While the present invention has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the invention is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the invention

We / I Claimed:

1. A system 100 for detecting a multitype vehicle using neural network in autonomous connected vehicles, said system 100 comprising:
a processor 102;
a computer readable medium 104;
a display 106;
a user interface 108;
an external device 110;
a communication network 112;
a memory communicatively coupled to the processor 102,
wherein the memory stores processor instructions, which, on execution, causes the processor to detect said multitype vehicle for providing a subscription plan based on a type of vehicle.

2. The system 100 as claimed in claim 1, wherein method of detecting said multitype vehicle for providing said subscription plan based on the type of vehicle comprising:
pre-training, said neural network, through a set of input data to identify a number plate of a vehicle from an image;
receiving, by the system 100, a real time traffic data from a real time traffic database to identify said type of vehicle from a multitype vehicle;
identifying, by the system 100, a license plate number of each vehicle present in said real time traffic data using said neural network, wherein said system 100 identifies a type of vehicle based on said license plate number;
authenticating, by the system 100, said license plate number of each vehicle by matching with the transport database;
identifying, by the system 100, radio resource requirement of each vehicle when a vehicle history is available in a vehicle travelling history database; and
matching, by the system 100, said radio resource requirement of each vehicle in a radio resource management database through at least one of resource allocation, resource scheduling, resource levelling, or resource forecasting.

3. The system 100 as claimed in claim 1, wherein a vehicle is not allocated said radio resource when said license plate number is not available in transport database.

4. The system 100 as claimed in claim 1, wherein said type of vehicle comprises at least one of four-wheeler, two-wheeler, three-wheeler, commercial vehicle, Motor vehicle, agricultural vehicle, Road Vehicle, Heavy Duty Vehicles, Defense Vehicles, or the like.

5. The system 100 as claimed in claim 1, wherein said external device 110 comprises at least one of desktop, a laptop, a notebook, a netbook, a tablet, a smartphone, a mobile phone, or any other computing device.

6. The system 100 as claimed in claim 1, wherein a service provider provides said subscription plan to each vehicle user based on said type of vehicle of said multitype vehicle.

7. The system 100 as claimed in claim 1, wherein said service provider provides a temporary plan to said vehicle user when a vehicle history is not available in said vehicle travelling history database.

8. The system 100 as claimed in claim 1 further comprises storing each of resource allocation, resource scheduling, resource levelling, and resource forecasting of each vehicle user in said vehicle travelling history database.