Finding a best parking place using exponential smoothing and cloud system in a metropolitan area

Finding the vacant space for parking a vehicle during peak hours is becoming a difficult task at ones end. Parking process whether in shopping malls, restaurants, or offices etc. is a long process and also leads to waste of gasoline. Smart car parking helps in finding the parking slot through Vehicular Ad Hoc Networks (VANET’s). For vehicle communication, some devices such as roadside units and on-board units are present that provides parking slot information. In the proposed work, we have introduced an online reservation facility for parking slot. People can reserve their parking space in advance before reaching to their venues in advance. This will help in reducing the waiting time for the parking allocation to the particular vehicle. This will also help to enhance the parking capabilities and will increase the efficiency when compared to other parking strategies. Our proposed approach can minimize the cost of parking on per person basis, exhaust of vehicle, and indirectly it will impact on save of wastage of gasoline and will keep the environment green.

Finding a vacant parking space in outdoor parking lots is a daily concern of most vehicle drivers during rush hours, especially in the urban context. In this paper, an outdoor parking space vacancy detection system is proposed, using mobile devices to improve parking space searching experience for vehicle drivers by providing them with the location and occupancy information of parking spaces. The system uses state-of-the-art image recognition algorithm, namely Convolutional Neural Network with a Raspberry Pi to identify vacant parking spaces from a parking lot image retrieved in real time via an IP camera. A university parking lot has been chosen as the test bed to deploy the proposed system for real time parking space vacancy detection. An Android smartphone application called Driver App is developed to enable ubiquitous visualization of real time outdoor parking spaces occupancy information for vehicle drivers. Evaluation outcomes based on the responses to System Usability Scale (SUS) questionnaire revealed high usability of the Driver App as a tool that provides smart parking service to assist vehicle drivers in searching for a vacant parking space.

With the continuous acceleration of China’s modernization process, the number of private cars has increased sharply, and the traffic flow of various parking lots is increasing. The parking management of motor vehicles has become a problem of great concern to the society. In view of the shortage of parking spaces, the long time of manual fee collection, low efficiency and the inability to quickly find the reserved parking spaces in urban parking lots in China. At the same time, in large shopping malls, there is a common phenomenon that the parking range is single or the parking space is difficult to find, which makes the vehicles stay in the parking lot in a short time and cause congestion. For parking lot managers, it is difficult to timely regulate the parking lot through the existing traffic flow information of the parking lot, and it is impossible to determine the number of vacant parking spaces in the parking lot in real time. At the same time, The existing management system has problems such as low efficiency, poor scalability and less service content, which is difficult to solve in the face of emerging problems. Therefore, based on the above problems, this project designs an urban intelligent parking cloud platform based on wechat applet, which can distinguish users and administrators, select spare parking spaces, route navigation and time payment of parking fees for parking users, and the parking lot managers can understand the spare parking spaces and vehicle payment of the parking lot they manage, At the same time, it can detect the traffic flow through the traffic flow in a period of time and give early warning to prevent the accumulation of vehicles in the parking lot. Users and administrators can obtain more convenient and real-time information only through mobile phones, which solves the difficult problem of parking management in intelligent parking lot.

A Smartphone VANET Based Forward Collision Detection System

In urban vehicular network, existing routing protocols using beacon message scheme and P2P (peer to peer) density discovery scheme can hardly make a good performance as well as keep a high feasibility. This paper proposes an IGOR (improved geography and overlay routing) in urban vehicular environment. In IGOR, RSUs (road side units) are introduced to support the road density discovery. A passive neighbor discovery scheme is used by OBUs (on board units) instead of the beacon message scheme. The performance of IGOR is evaluated against the benchmark routing protocol of LOUVRE (landmark overlays for urban vehicular routing environment). Results show that IGOR has a higher accuracy in density discovery as well as a lower cost of the routing establishment and maintenance.

Along with the various technological advancements, the next generation vehicular networks such as the Internet of Vehicles (IoV) also bring in various cybersecurity challenges. To effectively address these challenges, in addition to the existing authentication techniques, there is also a need for identification of the misbehaving entities in the network. This letter proposes a deep learning-based classification framework to identify potential misbehaving vehicles before the communication requests from the On Board Units (OBUs) of the vehicles can be entertained by the network infrastructure such as the Road Side Units (RSUs). The evaluated metrics demonstrate the performance of the proposed classification approaches.

Development and experiment of an intelligent connected cooperative vehicle infrastructure system based on multiple V2I modes and BWM-IGR method

Global misbehavior detection in Cooperative Intelligent Transport Systems (C–ITS) is carried out by a central entity named Misbehavior Authority (MA). The detection is based on local misbehavior detection information sent by Vehicle’s On–Board Units (OBUs) and by Road–Side Units (RSUs) called Misbehavior Reports (MBRs) to the MA. By analyzing these Misbehavior Reports (MBRs), the MA is able to compute various misbehavior detection information. In this work, we propose and evaluate different Machine Learning (ML)-based solutions for the internal detection process of the MA. We show through extensive simulation and several detection metrics the ability of solutions to precisely identify different misbehavior types.

As urbanization grows and the cost of vehicle production decreases, urban traffic has become a major problem of modern life. Developing intelligent vehicles alongside standardizing inter-vehicle communications promises that this technology will be a part of future life. In this research, we propose a method in which clustering is used to gather vehicles' movements information in a vehicle ad-hoc network. This method is based on extending green wave using road-side units as a fixed agent and on board units (OBU) in vehicles as a mobile agent. This information is then transmitted to traffic lights for decision making. This algorithm evaluated using Monte Carlo simulation. The simulation results show this method has a positive effect on reducing the average waiting time and overall stop of the vehicles behind the traffic lights.

With the existing Long Term Evolution(LTE) based Cellular Vehicle to Anything(C-V2X) infrastructure and the recent advancements in 5G implemented Ultra Reliable Low Latency Communication(URLLC), comes the need of fast and secure communications in Connected Autonomous Vehicles(CAVs). Physical layer can always be a fast and reliable medium for exploitation while having legit communication as a source for performance and latency optimization besides the enhanced spectrum efficiency depicted by multicasting and its adequate worth. Utilization of CAVs as connected IoT resources for load mitigation is also eminent from research. In this article, we benefit the 5G V2X environment of CAVs for road safety use case where a secure geo-specific locally containerized co-operative aware autonomous driving system would be the prime concern. In the localized V2X server, Roadside units(RSUs) get benefit from physical layer parameters to generate a key for end to end(E2E) communication while the On Board Unit(OBU) containers provide the cryptographic shreds as microservices in real-time as they do while monitoring or location updating. Mininet and RYU as network emulator and controller are used for the scenario implementation and analysis. Beyond RSU, no need of secure communication exists any longer due to the broadcast nature of messages for all involved vehicles in the marked premises so instead of security, we put prime focus on least latency due to highly dynamic environment and we did zero forced sparse channel estimation for evolved multimedia broadcast multicast service(eMBMS) in C-V2X.

The parking space is one of the crucial problems in the urban city. Most buildings have parking facilities for residents and visitors. The common parking patterns are parallel parking, perpendicular parking and angular parking. Parallel parking is a common design for street side parking whereas perpendicular parking and angular parking are a common design for garages. Angular parking is easy to park than other types. A typical parking space for a personal car is 3 meters in width and 6 meters in length. The parking space have limitation but the number of car increasing along with the economic growth [7]. So the parking space should accommodate the maximum number of car. Normally, the parking space configuration is designed by architects or construction engineers because it's required technical knowledge to calculate a number of car and parking pattern. In parking area, there must be an alleyway for cars to enter and exits. This study is to design and implement a calculation model for number of car and parking pattern in the specified parking area. In the calculation, the dimension and the specific condition of parking area is require. The model calculate the maximum number of car and also calculate the parking pattern for the parking area. This model is use to support non-technical user during the parking area design and can be as engine to develop mobile application for general user in the future.

This paper proposes a roadside unit (RSU)-coordinated synchronous multi-channel medium access control (MAC) scheme for vehicular ad hoc networks (VANETs). The proposed scheme allows the on board unit (OBU) to reserve service channels (SCHs) on the control channel (CCH) during almost the whole synchronous interval and supports simultaneous transmissions on different SCHs. It enhances the performance of VANETs and decreases the CCH congestion. Moreover, we use RSU to record the rendezvous information and broadcast it to the OBUs. This method avoids the multi-channel hidden terminal problem. An analytical model is developed to evaluate the aggregate throughput on SCHs. This model considers the following factors: non-safety message transmission probability in each synchronous interval, average size of the non-safety message, and the number of OBUs. Furthermore, the requirement of obtaining the maximum throughput is computed. Simulation results are provided to validate the analytical model and to demonstrate the improvement in throughput. The results indicate that the proposed scheme can provide higher aggregate throughput than that of the vehicular enhanced multi-channel MAC and IEEE 1609.4, and better performance in CCH congestion control especially under high network load conditions.

Vehicular ad hoc networks (VANETs) have recently received considerable attentions for both safety and commercial applications. In this paper, we address popular content distribution (PCD) in VANETs, in which a popular file is downloaded by a group of on-board units (OBUs) passing by a roadside unit (RSU). Due to high speeds and channel fadings, the OBUs may not finish downloading the entire file from the RSU, but only possess several content pieces. In this paper, we propose a cooperative approach using coalitional graph game to establish a peer-to-peer (P2P) network among the OBUs, in which content pieces are exchanged to complete the file downloading process. Further, we adopt cognitive radio (CR) technique for implementing vehicle-to-vehicle (V2V) transmissions. Simulation results show that the proposed algorithm enjoys a better performance in various conditions, compared to the non-cooperative approach, where OBUs randomly response to content requests from other OBUs.

This paper aims to present an intelligent system for parking space detection based on image processing technique. The proposed system captures and processes the rounded image drawn at parking lot and produces the information of the empty car parking spaces. In this work, a camera is used as a sensor to take photos to show the occupancy of car parks. The reason why a camera is used is because with an image it can detect the presence of many cars at once. Also, the camera can be easily moved to detect different car parking lots. By having this image, the particular car parks vacant can be known and then the processed information was used to guide a driver to an available car park rather than wasting time to find one. The proposed system has been developed in both software and hardware platform. An automatic parking system is used to make the whole process of parking cars more efficient and less complex for both drivers and administrators.

The change in approaches to the organization of parking space in the conditions of modern urban development of cities is considered. The domestic and foreign practice of designing parking lots oriented to residential development is analyzed. The trend of changes in the design, construction and organization of parking lots in highly developed cities has been revealed, in the direction of moving away from meeting the needs of all car owners in parking spaces to integrating parking spaces into the residential and transport infrastructure of cities. Modern parking lots are adaptive, integrated, functionally changing urban spaces over time, which allow regulating the number of cars on city streets. In Ukraine, the design of parking lots still gravitates towards the level of car mobility and the radius of accessibility to parking lots. The article examines the types, distribution, types by purpose and placement of parking spaces in the city of Vinnytsia. The problem of parking space organization in the city of Vinnytsia has been determined. The peculiarities of the formation of the existing parking space in the city of Vinnytsia, depending on the formation of the existing development of the city districts, functional purpose and the way of using the territory, were studied. The main typology of the city's parking lots and their condition have been determined. The main trends in the organization of parking space in the city of Vinnytsia are presented and rational directions for its further development are proposed, which allow to reduce the use of personal vehicles when moving around the city and free the city spaces from excessive overloading with cars.