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Open AccessReview

Research Review on Parking Space Detection Method

1
School of Computer Information Engineering, Jiangxi Normal University, Nanchang 330022, China
2
School of Software, Tongji University, Shanghai 201804, China
3
School of Software, Jiangxi Normal University, Nanchang 330022, China
4
School of Informatics, Xiamen University, Xiamen 361005, China
*
Author to whom correspondence should be addressed.
Symmetry 2021, 13(1), 128; https://doi.org/10.3390/sym13010128
Received: 1 December 2020 / Revised: 25 December 2020 / Accepted: 28 December 2020 / Published: 13 January 2021
The parking assist system is an essential application of the car’s active collision avoidance system in low-speed and complex urban environments, which has been a hot research topic in recent years. Parking space detection is an important step of the parking assistance system, and its research object is parking spaces with symmetrical structures in parking lots. By analyzing and investigating parking space information measured by the sensors, reliable detection of sufficient parking spaces can be realized. First, this article discusses the main problems in the process of detecting parking spaces, illustrating the research significance and current research status of parking space detection methods. In addition, it further introduces some parking space detection methods, including free-space-based methods, parking-space-marking-based methods, user-interface-based methods, and infrastructure-based methods, which are all under methods of parking space selection. Lastly, this article summarizes the parking space detection methods, which gives a clear direction for future research. View Full-Text
Keywords: parking assist system; parking space detection; symmetrical structures; sensors; reliable detection; free space; parking space marking; user interface; infrastructures parking assist system; parking space detection; symmetrical structures; sensors; reliable detection; free space; parking space marking; user interface; infrastructures
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MDPI and ACS Style

Ma, Y.; Liu, Y.; Zhang, L.; Cao, Y.; Guo, S.; Li, H. Research Review on Parking Space Detection Method. Symmetry 2021, 13, 128.

AMA Style

Ma Y, Liu Y, Zhang L, Cao Y, Guo S, Li H. Research Review on Parking Space Detection Method. Symmetry. 2021; 13(1):128.

Chicago/Turabian Style

Ma, Yong; Liu, Yangguo; Zhang, Lin; Cao, Yuanlong; Guo, Shihui; Li, Hanxi. 2021. "Research Review on Parking Space Detection Method" Symmetry 13, no. 1: 128.

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