From smart parking towards autonomous valet parking: A survey, challenges and future Works

Khalid, Muhammad; Wang, Kezhi; Aslam, Nauman; Cao, Yue; Ahmad, Naveed; Khan, Muhammad Khurram

doi:10.1016/j.jnca.2020.102935

Cited by 73 publications

(43 citation statements)

References 114 publications

(106 reference statements)

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“…For the real time occupancy classification, they developed a computationally inexpensive CNN architecture. More survey works in this domain can be found in [40,41,42].…”

Section: Smart Parking Systemmentioning

confidence: 99%

Smart Parking Space Detection under Hazy conditions using Convolutional Neural Networks: A Novel Approach

Satyanath¹,

Sahoo²,

Roul³

2022

Preprint

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Limited urban parking space combined with urbanization has necessitated the development of smart parking systems that can communicate the availability of parking slots to the end users. Towards this, various deep learning based solutions using convolutional neural networks have been proposed for parking space occupation detection. Though these approaches are robust to partial obstructions and lighting conditions, their performance is found to degrade in the presence of haze conditions. Looking in this direction, this paper investigates the use of dehazing networks that improves the performance of parking space occupancy classifier under hazy conditions. Additionally, training procedures are proposed for dehazing networks to maximize the performance of the system on both hazy and non-hazy conditions. The proposed system is deployable as part of existing smart parking systems where limited number of cameras are used to monitor hundreds of parking spaces. To validate our approach, we have developed a custom hazy parking system dataset from real-world task-driven test set of RESIDE-β dataset. The proposed approach is tested against existing state-of-the-art parking space detectors on CNRPark-EXT and hazy parking system datasets. Experimental results indicate that there is a significant accuracy improvement of the proposed approach on the hazy parking system dataset.

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“…For the real time occupancy classification, they developed a computationally inexpensive CNN architecture. More survey works in this domain can be found in [40,41,42].…”

Section: Smart Parking Systemmentioning

confidence: 99%

Smart Parking Space Detection under Hazy conditions using Convolutional Neural Networks: A Novel Approach

Satyanath¹,

Sahoo²,

Roul³

2022

Preprint

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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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“…Presently, autonomous parking systems are commercially available. In addition, autonomous valet parking that allows an autonomous vehicle to park the car is also available [1].…”

Section: Introductionmentioning

confidence: 99%

Neural Motion Planning for Autonomous Parking

Kim¹,

Huh

2021

Preprint

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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 algorithm performance.

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From smart parking towards autonomous valet parking: A survey, challenges and future Works

Cited by 73 publications

References 114 publications

Smart Parking Space Detection under Hazy conditions using Convolutional Neural Networks: A Novel Approach

Smart Parking Space Detection under Hazy conditions using Convolutional Neural Networks: A Novel Approach

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

Neural Motion Planning for Autonomous Parking

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