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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/2409.06558 |
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| _version_ | 1866909310798266368 |
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| author | Aliazam, Mahdieh Javadi, Ali Monazzah, Amir Mahdi Hosseini Azirani, Ahmad Akbari |
| author_facet | Aliazam, Mahdieh Javadi, Ali Monazzah, Amir Mahdi Hosseini Azirani, Ahmad Akbari |
| contents | As autonomous vehicles become more prevalent, highly accurate and efficient systems are increasingly critical to improve safety, performance, and energy consumption. Efficient management of energy-reliability tradeoffs in these systems demands the ability to predict various conditions during vehicle operations. With the promising improvement of Large Language Models (LLMs) and the emergence of well-known models like ChatGPT, unique opportunities for autonomous vehicle-related predictions have been provided in recent years. This paper proposed MAPS using LLMs as map reader co-drivers to predict the vital parameters to set during the autonomous vehicle operation to balance the energy-reliability tradeoff. The MAPS method demonstrates a 20% improvement in navigation accuracy compared to the best baseline method. MAPS also shows 11% energy savings in computational units and up to 54% in both mechanical and computational units. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06558 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science Aliazam, Mahdieh Javadi, Ali Monazzah, Amir Mahdi Hosseini Azirani, Ahmad Akbari Hardware Architecture Robotics As autonomous vehicles become more prevalent, highly accurate and efficient systems are increasingly critical to improve safety, performance, and energy consumption. Efficient management of energy-reliability tradeoffs in these systems demands the ability to predict various conditions during vehicle operations. With the promising improvement of Large Language Models (LLMs) and the emergence of well-known models like ChatGPT, unique opportunities for autonomous vehicle-related predictions have been provided in recent years. This paper proposed MAPS using LLMs as map reader co-drivers to predict the vital parameters to set during the autonomous vehicle operation to balance the energy-reliability tradeoff. The MAPS method demonstrates a 20% improvement in navigation accuracy compared to the best baseline method. MAPS also shows 11% energy savings in computational units and up to 54% in both mechanical and computational units. |
| title | MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science |
| topic | Hardware Architecture Robotics |
| url | https://arxiv.org/abs/2409.06558 |