Survey of Smart Parking Systems

Ogás, Mathias Gabriel Diaz; Fabregat, Ramón; Aciar, Silvana

doi:10.3390/app10113872

Cited by 50 publications

(20 citation statements)

References 287 publications

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“…In this section, we outline the research method used. To justify this research method and the scope of our work, we first highlight and discuss nine surveys and reviews that cite the datasets analyzed in this work (Meduri & Estebanez, 2018;Mahmud et al, 2020;Paidi et al, 2018;Enríquez et al, 2017;Kawade et al, 2020;Barriga et al, 2019;Chen et al, 2019;Zantalis et al, 2019;Diaz Ogás et al, 2020). In Meduri & Estebanez (2018), a brief review of Deep Learning (DL) based approaches for parking lots is given.…”

Section: Motivationmentioning

confidence: 99%

A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets

de Almeida,

Alves,

Parpinelli

et al. 2022

Preprint

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Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness. To evaluate such methods authors often employ publicly available parking lot image datasets. In this study, we surveyed and compared robust publicly available image datasets specifically crafted to test computer vision-based methods for parking lot management approaches and consequently present a systematic and comprehensive review of existing works that employ such datasets. The literature review identified relevant gaps that require further research, such as the requirement of dataset-independent approaches and methods suitable for autonomous detection of position of parking spaces. In addition, we have noticed that several important factors such as the presence of the same cars across consecutive images, have been neglected in most studies, thereby rendering unrealistic assessment protocols. Furthermore, the analysis of the datasets also revealed that certain features that should be present when developing new benchmarks, such as the availability of video sequences and images taken in more diverse conditions, including nighttime and snow, have not been incorporated.

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Section: Motivationmentioning

confidence: 99%

A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets

de Almeida,

Alves,

Parpinelli

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Difficulties in finding a suitable parking place encountered globally result in traffic congestion and air pollution issues that need to be addressed, and urgently. Readers are highly encouraged to look into the survey conducted by Diaz et al [ 18 ], which extensively investigated the smart parking systems and classified them into six categories: parking reservation systems, parking guidance and information systems, crowdsourcing in intelligent transportation systems, centralized assisted parking search, agent-based guiding systems, and electrical vehicle parking systems. Several other research studies have been conducted to address the car parking issues, and some of the latest solution approaches are highlighted in this section.…”

Section: Cooperative Driving—a Comprehensive Analysis Of the Research Literaturementioning

confidence: 99%

“…To sum up, the exhaustive survey conducted on smart parking systems (SPS) by Reference [ 18 ] uncovered several open issues and challenges in the SPS, such as: the consideration of user preferences while suggesting the optimally available parking spaces to the drivers; highlighting that the implementation of SPS should be context-sensitive since all cities have different infrastructures, budgets, and societies; and found that less work has been done on rescheduling the routes established by the SPS taking towards the parking slot in case of unexpected event triggers. …”

Section: Cooperative Driving—a Comprehensive Analysis Of the Research Literaturementioning

confidence: 99%

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Malik

Khan

El-Sayed

2021

Sensors

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Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

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“…Several review articles have been presented in the literature that explore, classify, and discuss the technical aspects of smart parking solutions [16][17][18][19]. This section discusses related smart parking systems from existing studies, obtained by searching using the keywords 'smart parking' and 'IoT parking' in several databases such as ISI Web of Science and Scopus.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of Smart Parking System Based on Fog Computing and Internet of Things

et al. 2021

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Current parking systems employ a single gateway-centered solution (i.e., cloud) for data processing which leads to the possibility of a single point of failure, data loss, and high delays. Moreover, the parking-spot selection process considers criteria that do not maximize parking utilization and revenue. The pricing strategy does not achieve high revenue because a fixed pricing rate is utilized. To address these issues, this paper proposes a smart parking system based on the Internet of Things (IoT) that provides useful information to drivers and parking administrators about available parking spots and related services such as parking navigation, reservation, and availability estimation. A multi-layer architecture is developed that consists of multiple sensor nodes, and fog and cloud computing layers. The acquired parking data are processed through fog computing nodes to facilitate obtaining the required real-time parking data. A novel algorithm to obtain the optimal parking spot with the minimum arrival time is also presented. Proof-of-concept implementation and simulation evaluations are conducted to validate the system performance. The findings show that the system reduces the parking arrival time by 16–46% compared to current parking systems. In addition, the revenue is increased for the parking authority by 10–15%.

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Survey of Smart Parking Systems

Cited by 50 publications

References 287 publications

A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets

A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Design and Development of Smart Parking System Based on Fog Computing and Internet of Things

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