Edge-Cloud Collaborative Motion Planning for Autonomous Driving with Large Language Models
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929463720148992 |
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| author | Chen, Jiao Dai, Suyan Chen, Fangfang Lv, Zuohong Tang, Jianhua |
| author_facet | Chen, Jiao Dai, Suyan Chen, Fangfang Lv, Zuohong Tang, Jianhua |
| contents | Integrating large language models (LLMs) into autonomous driving enhances personalization and adaptability in open-world scenarios. However, traditional edge computing models still face significant challenges in processing complex driving data, particularly regarding real-time performance and system efficiency. To address these challenges, this study introduces EC-Drive, a novel edge-cloud collaborative autonomous driving system with data drift detection capabilities. EC-Drive utilizes drift detection algorithms to selectively upload critical data, including new obstacles and traffic pattern changes, to the cloud for processing by GPT-4, while routine data is efficiently managed by smaller LLMs on edge devices. This approach not only reduces inference latency but also improves system efficiency by optimizing communication resource use. Experimental validation confirms the system's robust processing capabilities and practical applicability in real-world driving conditions, demonstrating the effectiveness of this edge-cloud collaboration framework. Our data and system demonstration will be released at https://sites.google.com/view/ec-drive. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09972 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Edge-Cloud Collaborative Motion Planning for Autonomous Driving with Large Language Models Chen, Jiao Dai, Suyan Chen, Fangfang Lv, Zuohong Tang, Jianhua Robotics Artificial Intelligence Integrating large language models (LLMs) into autonomous driving enhances personalization and adaptability in open-world scenarios. However, traditional edge computing models still face significant challenges in processing complex driving data, particularly regarding real-time performance and system efficiency. To address these challenges, this study introduces EC-Drive, a novel edge-cloud collaborative autonomous driving system with data drift detection capabilities. EC-Drive utilizes drift detection algorithms to selectively upload critical data, including new obstacles and traffic pattern changes, to the cloud for processing by GPT-4, while routine data is efficiently managed by smaller LLMs on edge devices. This approach not only reduces inference latency but also improves system efficiency by optimizing communication resource use. Experimental validation confirms the system's robust processing capabilities and practical applicability in real-world driving conditions, demonstrating the effectiveness of this edge-cloud collaboration framework. Our data and system demonstration will be released at https://sites.google.com/view/ec-drive. |
| title | Edge-Cloud Collaborative Motion Planning for Autonomous Driving with Large Language Models |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2408.09972 |