Deep Learning Based Multi-Zone AVP System Utilizing V2I Communications
Autonomous Valet Parking (AVP) is a technology that enables vehicles to park themselves without human intervention. It uses advanced sensing and communication systems to find a suitable parking space and to park the vehicle safely and efficiently. While various artificial intelligence (AI) based met...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10226216/ |
| _version_ | 1827856409777143808 |
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| author | Arati Kantu Kale Mohammad Sajid Shahriar Azharul Islam Kyunghi Chang |
| author_facet | Arati Kantu Kale Mohammad Sajid Shahriar Azharul Islam Kyunghi Chang |
| author_sort | Arati Kantu Kale |
| collection | DOAJ |
| description | Autonomous Valet Parking (AVP) is a technology that enables vehicles to park themselves without human intervention. It uses advanced sensing and communication systems to find a suitable parking space and to park the vehicle safely and efficiently. While various artificial intelligence (AI) based methods have demonstrated the benefits of AVP, including reducing traffic congestion, improving safety, and enhancing convenience and comfort for drivers, the issue of developing and evaluating AVP systems that can effectively handle multi-zone parking areas in real-world settings is yet to be solved. This paper presents an AVP system for three parking zones situated within a 1 km radius and utilizes a combination of existing tools and Deep Deterministic Policy Gradient (DDPG) algorithm to address the issue. DDPG algorithm controls the AVP system to allocate parking spaces efficiently in order to navigate and park vehicles autonomously. This work assumes the utilization of 5G-NR Vehicle-to-Infrastructure (V2I) communications for information exchange between vehicles and the system. It also studies the effect of communication latency on the system performance. Results of simulations show that the proposed system efficiently and safely parks vehicles in the three parking zones, achieving a reduction of 7% in waiting time compared to existing deep reinforcement learning methods. This work represents a notable advancement over current solutions and helps to advance the vision of smart cities for the future. |
| first_indexed | 2024-03-12T12:24:22Z |
| format | Article |
| id | doaj.art-12f3a254a7b14250986a2accd5d530a4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T12:24:22Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-12f3a254a7b14250986a2accd5d530a42023-08-29T23:00:27ZengIEEEIEEE Access2169-35362023-01-0111905109052510.1109/ACCESS.2023.330757110226216Deep Learning Based Multi-Zone AVP System Utilizing V2I CommunicationsArati Kantu Kale0https://orcid.org/0000-0002-1204-4073Mohammad Sajid Shahriar1https://orcid.org/0000-0003-3129-9466Azharul Islam2https://orcid.org/0000-0002-4715-7369Kyunghi Chang3https://orcid.org/0000-0002-2565-5391Department of Electrical and Computer Engineering, Inha University, Michuhol, Incheon, Republic of KoreaDepartment of Electrical and Computer Engineering, Inha University, Michuhol, Incheon, Republic of KoreaDepartment of Electrical and Computer Engineering, Inha University, Michuhol, Incheon, Republic of KoreaDepartment of Electrical and Computer Engineering, Inha University, Michuhol, Incheon, Republic of KoreaAutonomous Valet Parking (AVP) is a technology that enables vehicles to park themselves without human intervention. It uses advanced sensing and communication systems to find a suitable parking space and to park the vehicle safely and efficiently. While various artificial intelligence (AI) based methods have demonstrated the benefits of AVP, including reducing traffic congestion, improving safety, and enhancing convenience and comfort for drivers, the issue of developing and evaluating AVP systems that can effectively handle multi-zone parking areas in real-world settings is yet to be solved. This paper presents an AVP system for three parking zones situated within a 1 km radius and utilizes a combination of existing tools and Deep Deterministic Policy Gradient (DDPG) algorithm to address the issue. DDPG algorithm controls the AVP system to allocate parking spaces efficiently in order to navigate and park vehicles autonomously. This work assumes the utilization of 5G-NR Vehicle-to-Infrastructure (V2I) communications for information exchange between vehicles and the system. It also studies the effect of communication latency on the system performance. Results of simulations show that the proposed system efficiently and safely parks vehicles in the three parking zones, achieving a reduction of 7% in waiting time compared to existing deep reinforcement learning methods. This work represents a notable advancement over current solutions and helps to advance the vision of smart cities for the future.https://ieeexplore.ieee.org/document/10226216/Autonomous valet parkingmulti-zone AVPdeep deterministic policy gradientvehicle-to-infrastructure communications |
| spellingShingle | Arati Kantu Kale Mohammad Sajid Shahriar Azharul Islam Kyunghi Chang Deep Learning Based Multi-Zone AVP System Utilizing V2I Communications IEEE Access Autonomous valet parking multi-zone AVP deep deterministic policy gradient vehicle-to-infrastructure communications |
| title | Deep Learning Based Multi-Zone AVP System Utilizing V2I Communications |
| title_full | Deep Learning Based Multi-Zone AVP System Utilizing V2I Communications |
| title_fullStr | Deep Learning Based Multi-Zone AVP System Utilizing V2I Communications |
| title_full_unstemmed | Deep Learning Based Multi-Zone AVP System Utilizing V2I Communications |
| title_short | Deep Learning Based Multi-Zone AVP System Utilizing V2I Communications |
| title_sort | deep learning based multi zone avp system utilizing v2i communications |
| topic | Autonomous valet parking multi-zone AVP deep deterministic policy gradient vehicle-to-infrastructure communications |
| url | https://ieeexplore.ieee.org/document/10226216/ |
| work_keys_str_mv | AT aratikantukale deeplearningbasedmultizoneavpsystemutilizingv2icommunications AT mohammadsajidshahriar deeplearningbasedmultizoneavpsystemutilizingv2icommunications AT azharulislam deeplearningbasedmultizoneavpsystemutilizingv2icommunications AT kyunghichang deeplearningbasedmultizoneavpsystemutilizingv2icommunications |