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

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Abstract

Recently, we see an increasing number of vehicles coming into our lives, which makes finding car parks a difficult task. To overcome this challenge, efficient and advanced parking techniques are required, such as finding the proper parking slot, increasing users’ experience, dynamic path planning and congestion avoidance. To this end, this survey provides a detailed overview starting from Smart Parking (SP) towards the emerging Autonomous Valet Parking (AVP) techniques. Specially, the SP includes digitally enhanced parking, smart routing, high density parking and vacant slot detection solutions. Moreover, the AVP involves Short-range Autonomous Valet Parking (SAVP) and Long-range Autonomous Valet Parking (LAVP). Finally, open issues and future work are provided.

Introduction

The integration of Internet of Things (IoT) with cloud computing motivates the development of automated valet parking and smart cities (Huang et al., 2018). These services include real-time processing, location awareness, data and load management for environment sensing and coordination, which may have a direct effect on human mobility and future transportation system (Ni et al., 2019, Khalid et al., 2018b). Normally, people tend to travel using their own vehicles due to a higher rate of comfort and availability (Shoup, 2006). According to recent transport statistics of Great Britain, 78% of transportation is covered by private transport means, while the remaining 22% is covered by other public services, e.g., bus and rail (Department, 2016).

According to the British National Travel Survey, a typical household derives 361 h a year (Anon., 2020a) and on average the vehicle is parked for 95% of its lifetime (Cogill et al., 2014). To accommodate the increasing number of vehicles, a large place is normally used in urban areas for parking purposes (Zhou et al., 2018). The parking locations and spaces are normally selected in comparison to human-to-vehicle ratio. For example, about 31% of total land is used for parking in San Francisco, 16% in London, 18% in New York and 81% in Los Angeles (Manville and Shoup, 2005, Pierce and Shoup, 2013).

The car parking services can generate a good revenue for corresponding parties but also create the hurdles in the development of infrastructure.1 The management of supply and demand in the parking system is always an alarming issue. The development of Smart Parking (SP) (Evenepoel et al., 2014) and advances in Information Communication Technology (ICT) facilitate parking management, as depicted in Fig. 1.

If the driver already has information about availability of parking slots at a car park, less fuel consumption can be achieved towards car parking through a precise navigation (Rajabioun and Ioannou, 2015, Barone et al., 2013, Liu et al., 2019). Additionally, the vehicle guidance system can avoid the unnecessary roaming of vehicles in search of car parks. These solutions help to alleviate environmental pollution and traffic congestion (Su et al., 2012, Akhavan-Rezai et al., 2018, Khalid et al., 2019).

The optimal management of utilization of parking slots plays an important role in solving parking problems (Moller and Haas, 2019, Misra et al., 2019, Lin et al., 2017). Such proper utilization can bring a significant benefit in alleviating congestion rate (Lam and Yang, 2019). The advanced parking management techniques can benefit both drivers and car park operators:

  • It provides drivers with parking suggestions with low cost in terms of price and distance, which are inadequately studied in traditional parking techniques.

  • It improves utilization of parking space by accommodating more vehicles and implementing the profitable policies. Information about availability of parking slots and prices may be monitored through a centralized server and accessed through mobile applications or web services (Tahaei et al., 2020).

The operation of parking the vehicle at a fixed parking slot of the car park can be challenging for less experienced drivers (Wan et al., 2014). The skill set required and circular driving in tight space, makes parking operation more difficult. Thus parking a vehicle may need some extraordinary skills such as moving the vehicle backward and forward multiple times (Ni et al., 2018). Although SP applies some improved parking techniques (e.g., checking parking availability, traffic updates and finding a car park with cheaper cost), drivers still feel uncomfortable due to the requirement of human-driven operation (Akhavan-Rezai et al., 2018, Gyawali and Qian, 2019).

Furthermore, the recent development in autonomous systems with computer vision and sensing has provided potential solutions to transportation challenges, which include safety on wheels, efficiency, fuel consumption, traffic congestion and environmental pollution (Kafle et al., 2017, Paidi et al., 2018, Ramamoorthi and Sangaiah, 2019). The Autonomous Vehicle (AV) can perform various driving operations without human intervention, including avoiding obstacles, scanning for a vacant parking slot and parking navigation (Kotb et al., 2017, Thomas and Kovoor, 2018, Khan et al., 2019).

In particular, Autonomous Valet Parking (AVP) has been proposed recently which facilitates AV to drop user2 at a predefined drop-off spot. In the next step, the AV starts moving towards the selected car park, and performs route selection and parking operation autonomously (Williams, 2019).

The AVP can be divided into Short-range Autonomous Valet Parking (SAVP) and Long-range Autonomous Valet Parking (LAVP). In SAVP, users leave AV at car park entrance (e.g., the entrance may be co-located with drop-off spot) (Lou et al., 2019). The AV scans through available parking slots using modern vision techniques; avoids obstacles with the help of sensing technologies; and gets parked in designated parking slot using autonomous car-manoeuvringtechniques (Wang et al., 2015). Moreover, using the modern localization techniques, SAVP can be performed in multi-storey car park (Haas et al., 2020), where the AV may be trained at least once before performing SAVP operations autonomously (Schwesinger et al., 2016, Kong et al., 2019). In comparison, LAVP can deliver a higher rate of comfort and convenience to users. For LAVP, the user is able to leave AV at the centre of the city (or any designated drop-off spot), after which the AV travels from drop-off spot to car park with the appropriate route. The main acronyms used in this paper have been defined in Table 1.

In urban areas, a limited number of car parks are normally available. Also, tight space for parking a vehicle, high cost and congesting rate are the reasons that need immediate attention (Einsiedler et al., 2017). On average, 30% of traffic is caused by vehicles searching for parking slots at car parks. A driver on average spends 6 min in the UK to find a parking slot, reported by JustPark (Anon., 2020b, Gallivan, 2011, Klappenecker et al., 2014, Inci, 2015, Arnott et al., 2015). Moreover, according to a recent report by the insurance institute for highway safety, more than 20% of the car accidents occur in commercial parking areas. Also, the production rate of auto-mobiles has been doubled over the last decade, and it is quite challenging to accommodate the increasing number of vehicles in the current parking infrastructure. The application of emerging techniques like machine learning and intelligent sensing in car parks and roadside may effectively decrease the parking search time and improve mobility. Also, the proper parking management can have a major impact on the overall parking activities as well as reduce the congestion and accident rate (Yang et al., 2020). To this end, the comprehensive survey about the current parking solutions is highly expected, which this paper will deliver to the readers.

The previous survey papers on parking are summarized as follows:

  • The paper of Lin et al. (2017) focused on information collection, system development, and service dissemination in SP.

  • The paper of Banzhaf et al. (2017a) discussed high-density parking solutions.

  • The paper of Mahmud et al. (2013) focused on SP system management facilities with respect to the normal parking solutions.

One can see that the previous surveys mainly focused on SP and lack the investigation on some important points, e.g., smart routing and congestion reduction. Also, they have not covered two other emerging techniques, i.e., SAVP and LAVP, where the framework, methodology and features are discussed in this paper. A comparison of previous survey papers is carried out in Table 2.

The main contributions of this survey are as follows:

  • We first introduces the parking models, including traditional and smart parking.

  • We then provide a comprehensive study of the recent research in SP solutions, including digitally enhanced parking, high-density parking, vacant slot detection techniques and route planning solutions.

  • Next, we review AVP techniques, including SAVP and LAVP, with their procedure, features and working models.

  • Finally, we provide the challenges and future directions, to motivate more innovative research in this field.

The overall structure of this paper is described in Fig. 3. Specifically,

  • Section 2: Parking Module consists of a detailed introduction to the parking components in traditional parking and SP.

  • Section 3: Smart Parking involves a detailed review of SP solutions including cloud based and sensor based SP, vacant slot detection techniques, smart routing methodologies and high density parking mechanism inside dense parking areas.

  • Section 4: Autonomous Valet Parking discusses the innovative autonomous parking techniques, i.e., SAVP and LAVP. Also, their working model and procedures are explored.

  • Section 5: Challenges & Future Directions presents the challenges which aim to inspire the future research in this field.

  • Section 6: Conclusion concludes this survey paper by providing a summary of start-of-the-art technologies in smart and autonomous car parking.

Section snippets

Traditional parking

A few decades ago, the availability of parking information about location, pricing and vacant spaces was not adequately addressed (Zhang and Li, 2017). There was no prior information about where to park, how much it will cost and how far it is from car park. In traditional parking, a driver needs to go to every car park, and checks each parking slot in search of a free space (Shoup, 2017). On one hand, it costs highly in terms of time as well as fuel consumption and some users also suffer from

Smart parking

The exponential increase in the number of vehicles without proper management is alarming for existing infrastructure. The solution towards proposing efficient parking management has become an uttermost need these days. Currently, SP solutions have minimized congestion and pollution rate as compared to traditional parking approaches. As the automotive industry is moving towards autonomy, the parking techniques also require much attention. This section will discuss smart parking techniques

Autonomous valet parking

AVP is proposed to apply modern ICT techniques to cope with scheduling, path planning and reservation challenges in real time (Hane et al., 2017). With the increase of the number of vehicles, more traffic congestion and parking problems may arise in near future7  (Choi et al., 2015). With the help of AVP, the user can leave AV at a

Challenges & future directions

There are several challenges and open issues in SP and AVP which will be discussed as below.

Conclusion

This paper has summarized current parking solutions from traditional parking, towards the smart parking, and then reach the autonomous valet parking schemes. We have provided the taxonomy of SP and AVP, where the SP is divided into DEP, smart routing, high-density parking and parking slot detection mechanisms, while AVP is divided into Short-range AVP and Long-range AVP. In SAVP, users can leave their vehicles at the parking entrance and the vehicle can then park autonomously in a selected

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Muhammad Khalid completed his MS in computer science form Institute of Management Sciences, Peshawar, Pakistan. He is working towards his Ph.D. degree from Northumbria University, Newcastle Upon Tyne, UK. His research interests include Re-inforcement Learning, EV charging and scheduling, Internet of Things, Wireless Sensor Network, and Autonomous Valet Parking.

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    Muhammad Khalid completed his MS in computer science form Institute of Management Sciences, Peshawar, Pakistan. He is working towards his Ph.D. degree from Northumbria University, Newcastle Upon Tyne, UK. His research interests include Re-inforcement Learning, EV charging and scheduling, Internet of Things, Wireless Sensor Network, and Autonomous Valet Parking.

    Kezhi Wang received his B.E. and M.E. degrees in School of Automation from Chongqing University, China, in 2008 and 2011, respectively. He received his Ph.D. degree in Engineering from the University of Warwick, U.K. in 2015. He was a senior research officer in University of Essex, U.K. Currently he is a Lecturer with Department of Computer and Information Sciences at Northumbria University, U.K. His research interests include wireless communication, mobile edge computing and machine learning.

    Nauman Aslam is a Reader in Department of Computer and Information Sciences, at Northumbria University, Newcastle upon Tyne, UK. He received Ph.D. in Engineering Mathematics from Dalhousie University, Halifax, Nova Scotia, Canada in 2008. He worked as an Assistant Professor at Dalhousie University, Canada until 2011. His research interests include wireless ad hoc and sensor networks, fault tolerant and reliable communication, remote health monitoring application.

    Yue Cao received the Ph.D. degree from the Institute for Communication Systems (ICS), University of Surrey, U.K., in 2013. He was a Research Fellow with ICS, University of Surrey, a Lecturer and a Senior Lecturer with the Department of Computer and Information Sciences, Northumbria University, U.K., and has been the International Lecturer with the School of Computing and Communications, Lancaster University, U.K. His research interest includes intelligent transport systems. He is the Associate Editor of IEEE ACCESS, KSII Transactions on Internet and Information Systems, IGI Global International Journal of Vehicular Telematics and Infotainment Systems, and EURASIP Journal on Wireless Communications and Networking (Springer).

    Naveed Ahmad received his BS(Computer Science) degree from University of Peshawar, Pakistan in 2007 and Ph.D. in Computer Science from University of Surrey, UK in 2013. He is currently working as an Assistant Professor in Department of Computer Science, University of Peshawar, Pakistan. His research interests include security and privacy in emerging networks such as VANETs, DTN, and Internet of Things (IoT).

    Muhammad Khurram Khan is currently working as a Full Professor at the Center of Excellence in Information Assurance (CoEIA), King Saud University, Kingdom of Saudi Arabia. He is one of the founding members of CoEIA and has served as the Manager R&D from March 2009 to March 2012. He developed and successfully managed the research program of CoEIA, which transformed the centre as one of the best centres of research excellence in Saudi Arabia as well as in the region. He is the Editor-in-Chief of the well-esteemed ISI-indexed international journal Telecommunication Systems (Springer-Verlag) since 1993, with an impact factor of 1.163 (JCR 2013). Furthermore, he is the full-time Editor/Associate Editor of several ISI-indexed international journals/magazines, including IEEE COMMUNICATIONS MAGAZINE, Journal of Network & Computer Applications (Elsevier), IEEE ACCESS, Journal, Security & Communication Networks (Wiley), IEEE Consumer Electronics Magazine, PLOS ONE (USA), IET Wireless Sensor Systems, Electronic Commerce Research (Springer), Journal of Information Hiding and Multimedia Signal Processing, International Journal of Biometrics (Inderscience), Journal of Physical & Information Sciences, and Journal of Independent Studies and Research-Computing, etc. He has also been the Guest Editor of several international ISI-indexed journals of Springer-Verlag and Elsevier Science, etc. Moreover, he is one of the organizing chairs of more than 5 dozen international conferences and member of technical committees of more than 10 dozen international conferences. In addition, he is an active reviewer of many international journals. Prof. Khurram is an Adjunct Professor at Fujian University of Technology, China and an Honorary Professor at IIIRC, Shenzhen Graduate School, Harbin Institute of Technology, China. He has secured an outstanding leadership award at the IEEE International Conference on Networks and Systems Security 2009, Australia. He has been included in the Marquis Who’s Who in the World 2010 edition. Besides, he has received certificate of appreciation for outstanding contributions in “Biometrics & Information Security Research” at the AIT International Conference, June 2010, in Japan. He has been awarded a Gold Medal for the Best Invention & Innovation Award at 10th Malaysian Technology Expo 2011, Malaysia. Moreover, his invention recently received a Bronze Medal at the 41st International Exhibition of Inventions in Geneva, Switzerland, April 2013. In addition, he was awarded the best paper award from the Journal of Network & Computer Applications (Elsevier) in December 2015. Prof. Khurram is the recipient of the King Saud University Award for Scientific Excellence (Research Productivity) in May 2015. He is also a recipient of King Saud University Award for Scientific Excellence (Inventions, Innovations, and Technology Licensing) in May 2016. He has published over 260 research papers in the journals and conferences of international repute. In addition, he is an inventor of 10 US/PCT patents. He has edited 7 books/proceedings published by Springer-Verlag and IEEE. He has secured several national and international research grants in the domain of information security. His research areas of interest are cybersecurity, digital authentication, biometrics, multimedia security, and technological innovation management. He has recently played a leading role in developing the BS Cybersecurity Degree Program and the Higher Diploma in Cybersecurity at King Saud University. He is a fellow of the IET (UK), fellow of the BCS (UK), fellow of the FTRA (Korea), senior member of the IEEE (USA), a member of the IEEE Technical Committee on Security & Privacy, and a member of the IEEE Cybersecurity community.

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