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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.08005 |
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| _version_ | 1866910739317391360 |
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| author | Muzahid, Abu Jafar Md Zhao, Xiaopeng Wang, Zhenbo |
| author_facet | Muzahid, Abu Jafar Md Zhao, Xiaopeng Wang, Zhenbo |
| contents | The capabilities of automated vehicles are advancing rapidly, yet achieving full autonomy remains a significant challenge, requiring ongoing human cognition in decision-making processes. Incorporating human cognition into control algorithms has become increasingly important, as researchers work to develop strategies that minimize conflicts between human drivers and AI systems. Despite notable progress, many challenges persist, underscoring the need for further innovation and refinement in this field. This review covers recent progress in human-vehicle interaction (HVI) and AI collaboration for vehicle control. First, we start by looking at how HVI has evolved, pointing out key developments and identifying persistent problems. Second, we discuss the existing techniques, including methods for integrating human intuition and cognition into decision-making processes and developing systems that can mimic human behavior to enable optimal driving strategies and achieve safer and more efficient transportation. This review aims to contribute to the development of more effective and adaptive automated driving systems by enhancing human-AI collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08005 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Survey on Human-Vehicle Interactions and AI Collaboration for Optimal Decision-Making in Automated Driving Muzahid, Abu Jafar Md Zhao, Xiaopeng Wang, Zhenbo Systems and Control The capabilities of automated vehicles are advancing rapidly, yet achieving full autonomy remains a significant challenge, requiring ongoing human cognition in decision-making processes. Incorporating human cognition into control algorithms has become increasingly important, as researchers work to develop strategies that minimize conflicts between human drivers and AI systems. Despite notable progress, many challenges persist, underscoring the need for further innovation and refinement in this field. This review covers recent progress in human-vehicle interaction (HVI) and AI collaboration for vehicle control. First, we start by looking at how HVI has evolved, pointing out key developments and identifying persistent problems. Second, we discuss the existing techniques, including methods for integrating human intuition and cognition into decision-making processes and developing systems that can mimic human behavior to enable optimal driving strategies and achieve safer and more efficient transportation. This review aims to contribute to the development of more effective and adaptive automated driving systems by enhancing human-AI collaboration. |
| title | Survey on Human-Vehicle Interactions and AI Collaboration for Optimal Decision-Making in Automated Driving |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2412.08005 |