Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A Review
Advanced Driver Assistance System (ADAS) has become an essential feature in vehicles, and it is leading to the evolution of autonomous vehicles. But the technologies to implement ADAS suffer from certain inherent limitations, such as latency rate, computational speed, accuracy of the algorithm, secu...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10850907/ |
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| author | Avantika Rattan Abhishek Rudra Pal Muralimohan Gurusamy |
| author_facet | Avantika Rattan Abhishek Rudra Pal Muralimohan Gurusamy |
| author_sort | Avantika Rattan |
| collection | DOAJ |
| description | Advanced Driver Assistance System (ADAS) has become an essential feature in vehicles, and it is leading to the evolution of autonomous vehicles. But the technologies to implement ADAS suffer from certain inherent limitations, such as latency rate, computational speed, accuracy of the algorithm, security, and privacy, which are also the important factors for realizing full autonomous vehicles. With respect to these hindrances, an in-depth analysis of the existing research has shown that quantum machine learning (QML) can hold a powerful and alternate solution for the development of autonomous vehicles. The perks of quantum computation (QC) over classical systems are apparent with respect to security, privacy, and an exponentially high computation rate. The current review study underlines the benefits of quantum computation and asks for more QML research to improve real-time decision-making in autonomous vehicles, ultimately improving their safety and efficiency. The promise of quantum computing to handle the massive data and computational complexity that classical methods struggle with necessitates new studies in quantum machine learning (QML) for autonomous vehicles. |
| format | Article |
| id | doaj-art-edf275996700424093d7c3230aca27bc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-edf275996700424093d7c3230aca27bc2025-01-31T00:00:49ZengIEEEIEEE Access2169-35362025-01-0113175541758210.1109/ACCESS.2025.353295810850907Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A ReviewAvantika Rattan0https://orcid.org/0009-0000-5280-9870Abhishek Rudra Pal1https://orcid.org/0000-0002-4274-1195Muralimohan Gurusamy2https://orcid.org/0000-0002-9431-2456School of Mechanical Engineering, Vellore Institute of technology, Chennai, IndiaSchool of Mechanical Engineering, Vellore Institute of technology, Chennai, IndiaSchool of Mechanical Engineering, Vellore Institute of technology, Chennai, IndiaAdvanced Driver Assistance System (ADAS) has become an essential feature in vehicles, and it is leading to the evolution of autonomous vehicles. But the technologies to implement ADAS suffer from certain inherent limitations, such as latency rate, computational speed, accuracy of the algorithm, security, and privacy, which are also the important factors for realizing full autonomous vehicles. With respect to these hindrances, an in-depth analysis of the existing research has shown that quantum machine learning (QML) can hold a powerful and alternate solution for the development of autonomous vehicles. The perks of quantum computation (QC) over classical systems are apparent with respect to security, privacy, and an exponentially high computation rate. The current review study underlines the benefits of quantum computation and asks for more QML research to improve real-time decision-making in autonomous vehicles, ultimately improving their safety and efficiency. The promise of quantum computing to handle the massive data and computational complexity that classical methods struggle with necessitates new studies in quantum machine learning (QML) for autonomous vehicles.https://ieeexplore.ieee.org/document/10850907/Quantum machine learning (QML)autonomous vehicles (AVs)quantum computation (QC)advanced driving assistance system (ADAS)connected vehicles |
| spellingShingle | Avantika Rattan Abhishek Rudra Pal Muralimohan Gurusamy Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A Review IEEE Access Quantum machine learning (QML) autonomous vehicles (AVs) quantum computation (QC) advanced driving assistance system (ADAS) connected vehicles |
| title | Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A Review |
| title_full | Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A Review |
| title_fullStr | Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A Review |
| title_full_unstemmed | Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A Review |
| title_short | Quantum Computing for Advanced Driver Assistance Systems and Autonomous Vehicles: A Review |
| title_sort | quantum computing for advanced driver assistance systems and autonomous vehicles a review |
| topic | Quantum machine learning (QML) autonomous vehicles (AVs) quantum computation (QC) advanced driving assistance system (ADAS) connected vehicles |
| url | https://ieeexplore.ieee.org/document/10850907/ |
| work_keys_str_mv | AT avantikarattan quantumcomputingforadvanceddriverassistancesystemsandautonomousvehiclesareview AT abhishekrudrapal quantumcomputingforadvanceddriverassistancesystemsandautonomousvehiclesareview AT muralimohangurusamy quantumcomputingforadvanceddriverassistancesystemsandautonomousvehiclesareview |