Parking Environment Research Articles

Perancangan Desain Pola Parkir Sepeda Motor Menggunakan Metode Ergonomi Partisipatori (Studi Kasus : Universitas PGRI Wiranegara)

The limited access to public transportation to Universitas PGRI Wiranegara (UNIWARA) has increased the preference for using private vehicles, thereby emphasizing the need for adequate parking facilities. This study aims to create an optimal design for motorcycle parking patterns at UNIWARA that aligns with user needs, utilizing the Participatory Ergonomics method. Through three participatory ergonomic stages, namely the initial interview stage, group discussion stage, and parking pattern design stage, several issues were identified, including the irregular use of parking facilities, suboptimal capacity, poor vehicle circulation, and excessive interventions by parking attendants. The result of the motorcycle parking design using participatory ergonomics revealed a total capacity of 732 motorcycles in three parking sectors. The design specifications include a parking angle of 90°, a parking unit width of 0.75 meters, a parking unit length of 2 meters, and a parking circulation lane width exceeding 1.5 meters. These specifications address the identified issues and provide a well-structured parking environment that meets the needs and comfort of users. Keywords: Participatory Ergonomics, Parking Facilities, Parking Pattern

Parking choice behavior analysis of rural residents based on latent variable random forest model

ABSTRACT The imbalance of rural parking supply and demand has a great impact on traffic congestion and environmental pollution, which has attracted the attention of many scholars as well as policymakers. However, most of the current research on parking choice mainly focuses on urban business and residential areas, lacks research on rural parking choice behavior, and focuses on the analysis of observable factors, ignoring the internal relationship with potential variables. Based on this, this study considers the heterogeneity of individuals and uses the random forest algorithm to construct a model of rural residents’ willingness to choose parking with both latent and explicit variables, to explore how much and in what ways individual characteristics and parking characteristics affect rural residents’ parking choices, and to explore parking planning programs and strategies that are truly applicable to rural areas. The results of the study suggest that safety and convenience of the parking environment are key factors influencing the parking choice behavior of rural residents, and can greatly improve the predictive accuracy of the parking willingness model. Upon comparison, it is found that the application of the random forest algorithm is also significantly better than the logit model in terms of prediction effect, indicating that there is a nonlinear effect among the factors influencing the parking choice behavior of rural residents and that the random forest model with the addition of latent variables provides a better explanatory ability for the study of the parking choice behavior of rural residents.

A PARKING PATH PLANNING METHOD BASED ON CIRCULAR ARCS AND LINES

In recent years, there has been a shortage of parking spaces in many areas as the number of vehicles. Moreover, the parking space for each car may be narrowed, due to increase the number of parking spaces. As a result, parking spaces become narrower and accidents in the parking lot caused by inexperienced drivers are increasing. Thus, driving assistance functions such as automatic parking systems that perform parking tasks on behalf of the driver have recently become widespread. Facilities such as shopping malls and coin-operated parking lots have small parking spaces and are difficult to park. It is expected that the number of accidents caused by inexperienced drivers will decrease. In this paper, we propose a new method to search for a path that allows parking in a narrow parking environment from the start position to the parking target location by generating a path based on map information, arcs and straight lines. We propose a path generation method that is closer to human decision-making by effectively using arcs and straight lines.

Research on Automatic Vertical Parking Path-Planning Algorithms for Narrow Parking Spaces

Narrow parking spaces pose difficulties in path planning and spot turning caused by sudden changes and discontinuities in path curvature. To address these problems, this paper investigates the performance of three path-planning algorithms and proposes a path-optimization algorithm. First, a narrow parking space is defined based on single-step parking using the arc-line combination parking algorithm. Second, to compare the arc-line combination algorithm, Hybrid A* algorithm, and particle swarm optimization parking algorithm with respect to different narrow parking spaces, a multi-objective evaluation function is proposed, including three evaluation indicators, namely, the path length, the number of positive and negative conversions of vehicle speed, and the smoothness of the path. Their performance is compared using a simulation conducted in MATLAB. With the same starting point and different parking space widths, the three algorithms are simulated to generate different planned paths. Then, the evaluation indices are obtained to compare the performance of the algorithms based on the multi-objective function, the values of which indicate the fitness of the algorithm in a narrow parking environment. The results show that the Hybrid A* algorithm is better than the others for narrow parking spaces. Third, to smooth the planning path, a path optimization algorithm based on the cubic B-spline curve and gradient descent is proposed. Finally, the results of a simulation conducted on the proposed algorithm and the Hybrid A* algorithm are provided: the average minimum curvature of the path was reduced by 0.005 m−1, and the path meets the requirements of the minimum turning radius constraint of the analyzed vehicle. The results show that the proposed algorithm can effectively eliminate the curvature mutation point and constrain the path curvature to meet the requirements of a smooth path.

A Protocol for Reliable Real-Time Wireless Communication of Large Data Samples

In this paper, we present a middleware protocol, that transmits larger-than-a-frame data samples within application real-time and reliability requirements over a lossy wireless channel. The protocol efficiently deploys an intelligent retransmission control that exploits the extended deadline requirements of a sample for frame-level scheduling. The transmission of such samples is placed in the context of the necessary integration of such external sensor data into the decision-making process of an autonomous vehicle. Therefore, we provide parameterization and access rules for communication links in resource-managed scenarios and lay out how to integrate the protocol into the popular DDS middleware. The performance of the parameterized links to satisfy their reliability requirements is studied and compared to error protection protocols in the current software stack. We base the evaluation on an OMNeT++ simulation, whereby an established wireless error model is used. The use case is placed in the context of collaborative sensing in a valet parking environment, where external infrastructure sensor data augments the autonomous vehicle's data processing chain via a lossy wireless link. Thereby, the use case adheres to the underlying safety requirements. Results show that if the protocol's awareness of the application requirements is used for parameterization, guarantees for reliability can be provided that outperform existing solutions.

Curbside Parking Monitoring With Roadside LiDAR

Cities worldwide are striving to find more efficient approaches to address the prevalent parking challenges in urban areas. A key aspect of achieving an optimal parking environment is the collection of curbside parking data, which enables informed decision-making and effective management of on-street parking spaces. This study proposes a solution for curbside parking monitoring and data collection using roadside LiDAR systems. By leveraging laser beam variation detection, this solution can extract essential information about parking usage. Unlike existing solutions, such as imagery or embedded sensor-based monitoring, our solution offers portability and ease of deployment for short-term or long-term curbside parking data collection. Additionally, the LiDAR sensor captures only three-dimensional data and is independent of illumination conditions, ensuring stable operation throughout the day while safeguarding privacy by not capturing imagery. These features align with the requirements of city agencies for parking data collection. The workflow follows a simple trend without the need for complex training, as typically seen in machine learning-based methods, and instead relies on parameter tuning based on real-world environmental factors. To validate the effectiveness of our method, we collected curbside parking data for five days at a midtown traffic junction with eight parking spaces. Manual validation confirmed a 95% match between identified parking events and observed data across different time periods. The study further presents parking statistics based on the identified events, revealing crucial insights about parking usage in the study area.

Sistem Pembayaran Parkir Non-Tunai Berbasis Mikrokontroler dengan Metode Template Matching

Parking is an activity to stop or store vehicles in a place that has been provided in public places that have a parking system. The parking system is a system that is applied to a parking environment. Several parking systems developed are manual parking systems, computer-based parking systems, semi-automatic parking systems and RFID-based parking systems. Indonesia has developed a lot of computer-based parking systems, but many areas still use manual parking systems. So that there are still many vehicles parked carelessly, security is not guaranteed and the payment system has not been accumulated based on the length of parking time. This paper describes the development of a parking system based on the template matching method by developing non-cash payments based on the length of parking time. Researchers use a webcam to detect vehicles that will enter the parking area. The captured images are processed in a database to store vehicle entry time information. Researchers use qr codes as a substitute for admission tickets. When leaving the parking area, a QR code will be scanned on the provided application, then users can make payments through the application. This system can be carried out independently by the user so that it does not require full 24-hour supervision.

Intelligent Parking Control Method Based on Multi-Source Sensory Information Fusion and End-to-End Deep Learning

To address the challenges of inefficient intelligent parking performance and reduced efficiency in complex environments, this study presents an end-to-end intelligent parking control model based on a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture incorporating multi-source sensory information fusion to improve decision-making and adaptability. The model can produce real-time intelligent parking control decisions by extracting spatiotemporal features, including comprehensive 360-degree panoramic images and ultrasonic sensor distance measurements. To enhance the coverage of real-world environments in the dataset, a data collection platform was developed, leveraging the PreScan simulation platform in conjunction with actual parking environments. Consequently, a comprehensive parking environment dataset comprising various types was constructed. A deep learning model was devised to ameliorate horizontal and vertical control in intelligent parking systems, integrating Convolutional Neural Networks and Long Short-Term Memory in a parallel configuration. By meticulously accounting for parking environment characteristics, sliding window parameters were optimized, and transfer learning was employed for secondary model training to fortify the prediction accuracy. To ascertain the system’s robustness, simulation tests were performed. The ultimate results from the actual environment experiment revealed that the end-to-end intelligent parking model substantially surpassed the existing approaches, bolstering parking efficiency and effectiveness in complex contexts.

Neural Motion Planning for Autonomous Parking

This paper presents a hybrid motion planning strategy that combines a deep generative network with a conventional motion planning method. Existing planning methods such as A* and Hybrid A* are widely used in path planning tasks because of their ability to determine feasible paths even in complex environments; however, they have limitations in terms of efficiency. To overcome these limitations, a path planning algorithm based on a neural network, namely the neural Hybrid A*, is introduced. This paper proposes using a conditional variational autoencoder (CVAE) to guide the search algorithm by exploiting the ability of CVAE to learn information about the planning space given the information of the parking environment. An efficient expansion strategy is utilized based on a distribution of feasible trajectories learned in the demonstrations. The proposed method effectively learns the representations of a given state, and shows improvement in terms of computational time and the number of node expanded related to algorithm performance.

Asymmetric Consortium Blockchain and Homomorphically Polynomial-Based PIR for Secured Smart Parking Systems

In crowded cities, searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’ time, increases air pollution, and traffic congestion. Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement. But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number, personal identity, and desired destination. This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’ security breaches, single points of failure, and bottlenecks. In this paper, an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval (IACB-HCUPPIR) scheme is proposed to ensure parking lots’ availability with transparency security in a privacy-preserving smart parking system. In specific, an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment. It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval (HCUPPIR) scheme for preserving the location privacy of drivers. The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput, latency, and response time with maximized drivers’ privacy preservation. Moreover, the proposed fully homomorphic algorithm (FHE) was compared against partial-homomorphic encryption (PHE) and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s, whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s. Thus, we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.

Study on path planning and tracking algorithm for autonomous parking system of semi-trailers

Semi-trailers have the advantage that the maximum load capacity is large. However, there are some problems that high driving skill is required because it is necessary to perform unique steering during turning or backward driving. This paper proposes a path planning and tracking algorithm for autonomous parking of semi-trailers. The path planner determines the parking path to the target parking position in accordance with the given parking environment including obstacles. Furthermore, the steering control system is desinged to handle the path tracking and stabilize the hitch angle. The proposed path planning method and tracking control are both unique in that it focuses on the vehicle’s terminal state at the target parking position, and finds the solution in the time-inverse order of the actual driving sequence. The parking simulations is performed and the effectiveness of the proposed method is verified.

SCOPE: Smart Cooperative Parking Environment

SCOPE: Smart Cooperative Parking Environment

Multi-strategy vertical parking path planning considering the influence of obstacles

For the vertical parking scene with narrow parking spaces, this paper proposes three kinds of path planning strategies to guide the vehicle to park safely and successfully in the parking spaces by using the path planning method of arc-line-mitigation curve and considering the initial posture and position relationship of different vehicles. Then, considering the complexity of the actual parking environment, obstacle analysis and future trajectory prediction of dynamic obstacles are added, and the impact of different obstacles on the parking path is analyzed. Three obstacle avoidance methods are proposed, and three planning strategies are combined to achieve safe and efficient vertical parking. Finally, the path planning method is simulated and verified in QT environment. The results show that the vertical parking path planning method proposed in this paper meets the parking needs of narrow vertical parking spaces and achieves safe and efficient parking with predictability.

Parking Space Detection and Path Planning Based on VIDAR

The existing automatic parking algorithms often neglect the unknown obstacles in the parking environment, which causes a hidden danger to the safety of the automatic parking system. Therefore, this paper proposes parking space detection and path planning based on the VIDAR method (vision-IMU-based detection and range method) to solve the problem. In the parking space detection stage, the generalized obstacles are detected based on VIDAR to determine the obstacle areas, and then parking lines are detected by the Hough transform to determine the empty parking space. Compared with the parking detection method based on YOLO v5, the experimental results demonstrate that the proposed method has higher accuracy in complex parking environments with unknown obstacles. In the path planning stage, the path optimization algorithm of the A ∗ algorithm combined with the Bezier curve is used to generate smooth curves, and the environmental information is updated in real time based on VIDAR. The simulation results show that the method can make the vehicle efficiently avoid the obstacles and generate a smooth path in a dynamic parking environment, which can well meet the safety and stationarity of the parking requirements.

Deep reinforcement learning‐based autonomous parking design with neural network compute accelerators

AbstractWe describe the design and implementation of an autonomous prototype vehicle which finds an empty parking slot in a parking area, and parks itself in the empty parking slot, using neural networks based on deep reinforcement learning (RL). To perform an autonomous parking procedure for our prototype vehicle, two different artificial neural networks (ANNs) are trained using a deep RL Algorithm in a simulation environment and embedded into the computing platform of the prototype car. One of the ANNs enables the vehicle to drive autonomously in the parking environment. At the same time, an image processing algorithm is used to determine whether a parking slot is empty. When the image processing algorithm finds a suitable parking slot, a different ANN is activated and performs a safe parking procedure. However, ANN‐based machine learning techniques require high processing power and impose a high computational burden on embedded CPU and GPU platforms. To alleviate the computational burden, one can achieve higher performance and less power consumption using an application‐specific hardware design, where logic resources are fully exploited according to the algorithm of interest, in an energy‐efficient manner. In this article, hardware accelerators for our ANN models are designed and generated via the Vivado high‐level synthesis (HLS) tool, targeting an ARM based programmable SoC platform, ZedBoard. Our ANN accelerators have achieved a speedup of 17x as compared to an ARM software implementation. For deeper fully‐connected layers used in deep RL‐based solutions, function‐level parallelism (Vivado's dataflow) is employed to improve the computational efficiency. Our proposed stage‐level description for fully connected layers outperforms recent studies in terms of computation time.

Remote Monitoring And Localization: Tools For Smart Parking

The increase in usage of IoT environment is beyond the limits, which use Wireless (WSN).These networks can be used in many environments like logistics, supply chain management,health management,e-governance, smart parking, smart city, and smart appliances. WSN is a collection of sensors spread across them to base station/sink. Remote sensing sensors do sensing of an object remotely and detectthe static or dynamic information.In this paper, discussion is made on how to monitor and track the person’scar within a shopping mall. Remote monitoring and local optimization techniques are used in smart parking architecture, which suits for smart parking environment. Further, description of some of the monitoring and tracking techniques which was used earlier also been discussed with different types of protocols used for this appropriate environment. These systems can be implemented for effective smart parking.

An improved parking space recognition algorithm based on panoramic vision

In order to reduce parking difficulties caused by small parking spaces, low driver driving experience, and complex parking environment, parking assistance systems have attracted attention, but the identification of parking spaces is a key problem and technical difficulty. In this paper, an improved parking space recognition algorithm based on panoramic vision is proposed. Firstly, in the look-around image forming part, a method of distortion correction (DC) and perspective transformation (PT) based on LUT (Look Up Table) transformation is proposed to improve the processing speed of the algorithm. Then, to improve the accuracy of parking space recognition, an improved method combining rough extraction and fine matching is proposed to identify parking spaces in a look-around image. The experimental results show that the method achieves a detection rate of 97.63% under sufficient illumination and 79.77% even under insufficient illumination.

Semi-Autonomous Parking system for Automatic Transmission vehicles

This paper proposes an automated perpendicular parking strategy for a car which is to be designed. The design considers general cases of perpendicular parking for a rectangular body within a rectangular space. The system works in phases named: The Scanning phase, the parking environment is detected by ultrasonic sensors mounted on the extreme ends of body and a parking position and maneuvering path is produced if the space is enough. Then in the positioning phase, the model positions itself and get aligned with the parking space avoiding potential collisions. Finally, in maneuvering phase, the model moves to the parking position in the parking space in a specified path, which requires clutch, brake control, backward and forward maneuvers depending on the dimensions of the parking space. Based on the characteristics, a collision-free path is planned about the surroundings. The strategy is to be integrated into an automated parking system, and implemented in the car, showing capable of safe parallel parking in tight situations and as automated parking device to help vehicle drivers.

Automatic Parking Path Planning Based on Ant Colony Optimization and the Grid Method

This paper analyzes the path planning problem in the automatic parking process, and studies a path planning method for automatic parking. The grid method and the ant colony optimization are combined to find the shortest path from the parking start point to the end point. The grid method is used to model the parking environment to simulate the actual parking space of automatic parking; then this paper makes some improvements to the basic ant colony optimization, finds the destination by setting the ants’ movement rules in the grid, and finds the shortest path after N iterations; since the optimal path found is a polyline, it will increase the difficulty of controlling vehicle path tracking and affect the accuracy of vehicle path tracking. The bezier curve is used to generate a smooth path suitable for vehicle walking. Finally, through matlab simulation, the obstacles in the environment are simulated, and the parking trajectory is obtained. The results show that the path planning method proposed in this paper is feasible.

Context-Based Parking Slot Detection With a Realistic Dataset

The autonomous parking of vehicles requires the ability to accurately locate an available parking slot in the vicinity of a vehicle. Since parking slots have a variety of shapes and colors, may be occluded by obstacles, or look different due to surroundings such as lighting, accurately locating them can be a challenging task. In this paper, we propose a context-based parking slot detection method inspired by the process of a human driver finding a parking slot. Our method consists of two deep network modules: a parking context recognizer and parking slot detector . The parking context recognizer identifies the parking environment (type, angle, and availability of a parking slot), whereas the parking slot detector locates the exact position of a parking slot by multiple type-based fine-tuned detectors with rotated anchor boxes and a rotated non-maximal suppression. In addition, we release a realistic parking slot dataset, which comprises 22817 images of parking slots having various attributes and external conditions. We also propose a new evaluation metric for parking slot detection, reflecting whether a vehicle can be parked within the detected parking slot. Through comparison and ablation study in experiments, we demonstrate that our method outperformed the previous deep-learning-based methods, along with having a short operation time. The source codes and the dataset are available at https://github.com/dohoseok/context-based-parking-slot-detect/ .