Vacant Parking Spaces Research Articles

Preprocessed Mask RCNN for Parking Space Detection in Smart Parking Systems

This research developed a smart parking system through video data analysis using deep learning techniques that automatically determine the availability of vacant parking spaces. This system has two main stages. The first is the stage of marking the parking position on the image of a parking lot captured by the camera. This research proposes a Preprocessed Region-based Convolutional Neural Network (Mask R-CNN) to mark the parking position on the input image of a full parking lot. The preprocess that combining contrast enhancement using the Exposure Fusion framework, aims to overcome the problem of lighting variations in images taken in an open area. In the second stage, each parking position is examined whether the position is vacant or not using mAlexNet. A series of trials on images with varying light conditions indicate that the Preprocessed Mask R-CNN can improve marking the parking positions with an accuracy of Intersection over Union (IoU) reach 85.80%. The result of marking the parking position is then used in the trial of the availability of parking space on video data using mAlexNet, and achieving an accuracy of 73.73%.

Predicting Vacant Parking Space Availability: A Long Short-Term Memory Approach

The accurate prediction of vacant parking space availability is becoming increasingly essential for assisting drivers to determine where to park in advance. It helps ease traffic pressure and reduce gas emission and pollution. This article proposes a novel multistep long short-term memory recurrent neural network (LSTM-NN) model to predict the number of the vacant parking spaces on the basis of historical parking availability information. The key parameters of the model are deeply optimized. The prediction model is fully benchmarked with five well-known machine learning models&#x2014;i.e. a gated recurrent units neural network, a stacked autoencoder, a support vector regression, a back propagation neural network, and a <i>k</i>-nearest neighbor algorithm&#x2014;whose key parameters are sufficiently optimized as well. Adequate experiments with practical data collected from two parking lots with various capacities and traffic flows were conducted to evaluate the models&#x2019; performances on short- and long-term predictions. Experimental results show that the proposed multistep LSTM-NN model outperforms all the benchmark models, especially in a commercial parking lot with heavy traffic flow.

Research on parking sharing strategiesconsidering user overtime parking.

A parking sharing strategy is proposed to solve the problems of parking difficulty caused by the imbalance between parking spaces and parking demand. The vacant parking spaces of residential area can be efficiently utilized to meet the parking demands of those who are working at nearby or come for other activities based on the parking sharing strategy. The paper analyzes the distribution of vehicle arrival numbers and parking durations, then establishes a shared parking allocation model aiming to maximize the parking benefit considering the overtime-parking behavior of the parking users. Simulation methods are used to the analyze the relationship among the parking benefit, proportion of reserved parking, numbers of parking demand, acceptance rate of parking demand and utilization of shared parking spaces. Then, based on the principle of maximum parking benefit, we can determine the optimal proportion of reserved parking, number of shared parking spaces that should be purchased from the residents. Taking the utilization of shared parking spaces as an indicator, the validity of the static allocation principle is proved to be effective. Some allocation rules for parking demand are proposed to guarantees the maximum parking revenue and minimum impact on residents simultaneously.

PARK-A-LOT

With the increasing difficulty in finding parking spaces and the increasing need for security, an automated system to guide vehicles to parking will come as rescue. With this project of ours we aim to develop an approach that will allow automated entry to the employee vehicles that are registered with the office. This system will automatically detect if a vehicle has been registered and accordingly open the gates of the institute for the vehicle along with which it will also display and guide vehicles to vacant parking spaces and alternate parking areas in case of occupied parking lots.

Around View Monitoring-Based Vacant Parking Space Detection and Analysis

Accelerated urbanization and the ensuing rapid increase in urban populations led to the need for a tremendous number of parking spaces. Automated parking systems coupled with new parking lot layouts can effectively address the need. However, most automated parking systems available on the market today use ultrasonic sensors to detect vacant parking spaces. One limitation of this method is that a reference vehicle must be parked in an adjacent space, and the accuracy of distance information is highly dependent on the positioning of the reference vehicle. To overcome this limitation, an around view monitoring-based method for detecting parking spaces and algorithms analyzing the vacancy of the space are proposed in this study. The framework of the algorithm comprises two main stages: parking space detection and space occupancy classification. In addition, a highly robust analysis method is proposed to classify parking space occupancy. Two angles of view were used to detect features, classified as road or obstacle features, within the parking space. Road features were used to provide information regarding the possible vacancy of a parking space, and obstacle features were used to provide information regarding the possible occupancy of a parking space. Finally, these two types of information were integrated to determine whether a specific parking space is occupied. The experimental settings in this study consisted of three common settings: an indoor parking lot, an outdoor parking lot, and roadside parking spaces. The final tests showed that the method’s detection rate was lower in indoor settings than outdoor settings because lighting problems are severer in indoor settings than outdoor settings in around view monitoring (AVM) systems. However, the method achieved favorable detection performance overall. Furthermore, we tested and compared performance based on road features, obstacle features, and a combination of both. The results showed that integrating both types of features produced the lowest rate of classification error.

Early Detection of Vacant Parking Spaces Using Dashcam Videos

A major problem in metropolitan areas is finding parking spaces. Existing parking guidance systems often adopt fixed sensors or cameras that cannot provide information from the driver’s point of view. Motivated by the advent of dashboard cameras (dashcams), we develop neural-network-based methods for detecting vacant parking spaces in videos recorded by a dashcam. Detecting vacant parking spaces in dashcam videos enables early detection of spaces. Different from conventional object detection methods, we leverage the monotonicity of the detection confidence with respect to the distance away of the approaching target parking space and propose a new loss function, which can not only yield improved detection results but also enable early detection. To evaluate our detection method, we create a new large dataset containing 5,800 dashcam videos captured from 22 indoor and outdoor parking lots. To the best of our knowledge, this is the first and largest driver’s view video dataset that supports parking space detection and provides parking space occupancy annotations.

Smartphone-Based Context Flow Recognition for Outdoor Parking System with Machine Learning Approaches

Outdoor parking systems are one of the most crucial needs in a smart city to find vacant parking spaces in outdoor environments, such as roadsides, university campuses, and so on. In a typical outdoor parking system, the detection of a vehicle entering and leaving the parking zone is a major step. At present, there are numerous external sensor-based and camera-based parking systems available to detect the entrance and leaving of vehicles. Camera-based parking systems rely on sophisticated camera set-ups, while sensor-based parking systems require the installation of sensors at the parking spots or vehicles’ sides. Due to such complication, the deployment and maintenance costs of the existing parking systems are very high. Furthermore, the need for additional hardware and network capacity increases the cost and complexity, which makes it difficult to use for large deployment. This paper proposes an approach for outdoor parking utilizing only smartphone integrated sensors that do not require manpower support nor additional sensor installation. The proposed algorithm first receives sensor signals from the driver’s phone, performs pre-processing to recognize the context of drivers, which is followed by context flow recognition. The final result is obtained from context flow recognition which provides the output of whether the driver is parking or unparking. The proposed approach is validated with a set of comprehensive experiments. The performance of the proposed method is favorable as it uses only the smartphone’s internal sensors to recognize whether the cars are entering or leaving the parking area.

Vision Based Approach for Adaptive Parking Lots Occupancy Estimation

In the large cities, it remains difficult and expensive to create more parking spaces for vehicles since they have almost reached their full occupancy. The lack of available parking places leads to the problem of traffic congestion and consequently to pollution, since drivers will spend a lot of time looking for a vacant parking place. Hence the need to establish a parking management system able to improve the exploitation of the existent parking places at a city level. This paper proposes a new multi agent system for parking vacancies detection based on vision techniques. We explore a network of interconnected video surveillance cameras and parking stations to provide an intelligent service to the drivers in order to facilitate their task and improve the exploitation of the parking resources. The proposed system introduces a new approach for parking spaces modeling and elaborates an adaptive approach for vacancies estimation in order to supply the driver with reliable information about the vacant parking spaces in the city according to the size of his vehicle.

Vacant parking space identification using probabilistic neural network

&lt;span&gt;The need for public parking space is increasing nowadays due to the high number of cars available. Users of car parking services, in general, are still looking for vacant parking locations to park their vehicle manually. With the current technological developments, especially in image processing field, it is expected to solve the parking space problem. Therefore, this research implements image processing to determine the location of vacant parking space or occupied ones that run in real-time. In this study, the proposed method is divided into five stages. The first stage is image acquisition to capture the image of parking location. Then it continues to pre-processing stage which consists of the process of saturation, grayscale and thresholding. The third stage is image segmentation to cut the image into five parts. The next stage is feature extraction using invariant moment, and the last stage would be identification process to determine the location of vacant parking spaces or occupied ones. The results of this research using 100 test images generates an accuracy, recall, and precision of 94%.&lt;/span&gt;

An Advanced Parking Navigation System for Downtown Parking

This paper develops a novel parking navigation system for downtown parking that aims to mitigate parking competition by guiding drivers to appropriate vacant parking spaces. Given drivers’ real-time locations and their parking preferences, a two-sided matching algorithm is firstly adopted to achieve a stable driver-optimal matching, under which drivers will be assigned to their most appropriate parking spaces (if any), and have no incentive to misreport their private information (e.g., parking space preferences). Although drivers’ private information is required for the navigation system, a distributed solution procedure is applied to achieve the space assignment without disclosing such information. Lastly, simulation experiments are conducted to demonstrate the capability of the proposed navigation system on reducing driving time and the frequency of changed navigation compared with other navigation systems.

Evaluation of the Quality of the Free/Busy Car Park Information on the Basis of Certainty Factor of Hypothesis

The paper demonstrates an estimation method of the number of vacant parking spaces on the basis of video monitoring data. It provides a method for determining the quality of free/busy car park information using uncertainty modelling method based on certainty factor of hypothesis. The value of quality information IQ (CF) has been calculated with the help of a computer program. The simulation of results was performed depending on the input values change according to the normal distribution.

Predicting vacant parking space availability: an SVR method with fruit fly optimisation

In this study, a novel prediction model for the number of vacant parking spaces after a specific period of time is proposed based on support vector regression (SVR) with fruit fly optimisation algorithm (FOA). In the proposed model, the SVR parameters are initialised as the fruit fly population, and FOA is utilised to search the optimal parameters for SVR. Sufficient experiments within various scenarios, i.e. predicting the vacant parking space availability in parking lots with various capacities after various periods of time, have been conducted to verify the effectiveness of the proposed FOA-SVR prediction model. Three other commonly used prediction models, i.e. backpropagation neural network (NN), extreme learning machine and wavelet NN, are used as the comparison models. The experimental results show that the proposed FOA-SVR method has higher accuracy and stability in all the prediction scenarios.

Smart parking sensors, technologies and applications for open parking lots: a review

Parking a vehicle in traffic dense environments often leads to excess time of driving in search for free space which leads to congestions and environmental pollution. Lack of guidance information to vacant parking spaces is one reason for inefficient parking behaviour. Smart parking sensors and technologies facilitate guidance of drivers to free parking spaces thereby improving parking efficiency. Currently, no such sensors or technologies is in use for open parking lot. This study reviews the literature on the usage of smart parking sensors, technologies, applications and evaluates their applicability to open parking lots. Magnetometers, ultrasonic sensors and machine vision were few of the widely used sensors and technologies on closed parking lots. However, this study suggests a combination of machine vision, convolutional neural network or multi-agent systems suitable for open parking lots due to less expenditure and resistance to varied environmental conditions. Few smart parking applications show drivers the location of common open parking lots. No application provided real-time parking occupancy information, which is a necessity to guide them along the shortest route to free space. To develop smart parking applications for open parking lots, further research is needed in the fields of deep learning and multi-agent systems.

A Universal Vacant Parking Slot Recognition System Using Sensors Mounted on Off-the-Shelf Vehicles.

An automatic parking system is an essential part of autonomous driving, and it starts by recognizing vacant parking spaces. This paper proposes a method that can recognize various types of parking slot markings in a variety of lighting conditions including daytime, nighttime, and underground. The proposed method can readily be commercialized since it uses only those sensors already mounted on off-the-shelf vehicles: an around-view monitor (AVM) system, ultrasonic sensors, and in-vehicle motion sensors. This method first detects separating lines by extracting parallel line pairs from AVM images. Parking slot candidates are generated by pairing separating lines based on the geometric constraints of the parking slot. These candidates are confirmed by recognizing their entrance positions using line and corner features and classifying their occupancies using ultrasonic sensors. For more reliable recognition, this method uses the separating lines and parking slots not only found in the current image but also found in previous images by tracking their positions using the in-vehicle motion-sensor-based vehicle odometry. The proposed method was quantitatively evaluated using a dataset obtained during the day, night, and underground, and it outperformed previous methods by showing a 95.24% recall and a 97.64% precision.

Privacy-preserving Smart Parking Navigation Supporting Efficient Driving Guidance Retrieval

It is frustrating and time consuming for drivers to find an available parking spot in a congested area, such as downtown and shopping malls, especially in peak hours. Thus, it is very helpful for drivers to have real-time parking information to assist them in finding vacant parking spots timely. Unfortunately, to acquire needed parking information, the drivers have to submit personal queries for the availability of parking spaces in their destinations, and this could result in privacy violation if the queries are not protected. To reduce drivers’ hassle and preserve drivers’ privacy, we propose a privacy-preserving smart parking navigation system (P-SPAN) with efficient navigation result retrieval for drivers using Bloom filters. P-SPAN enables a cloud to guide vehicles to vacant parking spaces in the destinations based on real-time parking information without disclosing any personal information about drivers. Specifically, an efficient data retrieval mechanism is developed based on Bloom filters to support navigation result retrieval for querying vehicles. The drivers can anonymously query accessible parking spots to the cloud, and efficiently retrieve the encrypted navigation results from the passing-by roadside units. Therefore, it is unnecessary for a vehicle to keep connected with the queried roadside unit for acquiring the navigation result. Performance evaluation demonstrates that P-SPAN can provide effective parking navigation with high navigation result retrieving probability and low computational and communication overhead.

MIMO-FMCW Radar-Based Parking Monitoring Application With a Modified Convolutional Neural Network With Spatial Priors

Radar imaging is a competitive option for smart city applications over optical approaches, as it raises no privacy concerns. The inherent difficulty of interpreting radar signals can be overcome using deep learning techniques to leverage the capabilities of monitoring sensors with a minimum of human intervention. In this paper, we use a modified convolutional neural network (CNN) for classifying radar images in order to detect vacant parking spaces with a 77-GHz imaging radar. Although training CNNs for radar-image classification is challenging due to poor generalization performance caused by the lack of labeled training data, the modified architecture takes into account the properties of the radar image in order to introduce prior information into the model and improve performance. A MIMO-FMCW radar is utilized to render a slant-range image of a parking scenario, and the image patches corresponding to each parking location are classified independently in the CNN. Since the radiation pattern of a MIMO array varies as a function of the scanning angle, the corresponding spatial coordinate of each patch is included as an additional feature in the upper layers of the network. This allows the model to combine local features from each patch with global scenario information in order to learn robust features that generalize properly to new scenarios. Several models are trained end to end with data from four different parking scenarios and evaluated in a 4-fold cross-validation scheme, and performance is improved when spatial prior information is included.

MADM-based smart parking guidance algorithm.

In smart parking environments, how to choose suitable parking facilities with various attributes to satisfy certain criteria is an important decision issue. Based on the multiple attributes decision making (MADM) theory, this study proposed a smart parking guidance algorithm by considering three representative decision factors (i.e., walk duration, parking fee, and the number of vacant parking spaces) and various preferences of drivers. In this paper, the expected number of vacant parking spaces is regarded as an important attribute to reflect the difficulty degree of finding available parking spaces, and a queueing theory-based theoretical method was proposed to estimate this expected number for candidate parking facilities with different capacities, arrival rates, and service rates. The effectiveness of the MADM-based parking guidance algorithm was investigated and compared with a blind search-based approach in comprehensive scenarios with various distributions of parking facilities, traffic intensities, and user preferences. Experimental results show that the proposed MADM-based algorithm is effective to choose suitable parking resources to satisfy users’ preferences. Furthermore, it has also been observed that this newly proposed Markov Chain-based availability attribute is more effective to represent the availability of parking spaces than the arrival rate-based availability attribute proposed in existing research.

Gradient-driven parking navigation using a continuous information potential field based on wireless sensor network

Wireless sensor networks can support building and transportation system automation in numerous ways. An emerging application is to guide drivers to promptly locate vacant parking spaces in large parking structures during peak hours. This paper proposes efficient parking navigation via a continuous information potential field and gradient ascent method. Our theoretical analysis proves the convergence of a proposed algorithm and efficient convergence during the first and second steps of the algorithm to effectively prevent parking navigation from a gridlock situation. The empirical study demonstrates that the proposed algorithm performs more efficiently than existing algorithms.

ParkUs: A Novel Vehicle Parking Detection System

Finding on-street parking in congested urban areas is a challenging chore that most drivers worldwide dislike. Previous vehicle traffic studies have estimated that around thirty percent of vehicles travelling in inner city areas are made up of drivers searching for a vacant parking space. While there are hardware sensor based solutions to monitor on-street parking occupancy in real-time, instrumenting and maintaining such a city wide system is a substantial investment. In this paper, a novel vehicle parking activity detection method, called ParkUs, is introduced and tested with the aim to eventually reduce vacant car parking space search times. The system utilises accelerometer and magnetometer sensors found in all smartphones in order to detect parking activity within a city environment. Moreover, it uses a novel sensor fusion feature called the Orthogonality Error Estimate (OEE). We show that the OEE is an excellent indicator as it’s capable of detecting parking activities with high accuracy and low energy consumption. One of the envisioned applications of the ParkUs system will be to provide all drivers with guidelines on where they are most likely to find vacant parking spaces within a city. Therefore, reducing the time required to find a vacant parking space and subsequently vehicle congestion and emissions within the city.

Trajectory analysis for parking lot vacancy detection system

Taking a vacant parking lot in crowded metropolitan areas is a major problem especially with the huge number of vehicles. In this study, the authors provide a vision-based system for real-time management of parking spaces in the case of outdoor parking. Different real-world challenges may face these systems such as weather conditions, luminance variation, perspective distortion and inter-spaces occlusion. In this study, they propose a decisional module based on a tracking approach that determines in real time the state of the parking lots and localises the vacant parking spaces according to several extracted attributes. This decision is based on local characteristics of the parking spaces as well as the performed vehicles' events.