PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929611465555968 |
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| author | Luo, Xuewen Ding, Fan Song, Yinsheng Zhang, Xiaofeng Loo, Junnyong |
| author_facet | Luo, Xuewen Ding, Fan Song, Yinsheng Zhang, Xiaofeng Loo, Junnyong |
| contents | There is growing interest in leveraging the capabilities of robust Multi-Modal Large Language Models (MLLMs) directly within autonomous driving contexts. However, the high costs and complexity of designing and training end-to-end autonomous driving models make them challenging for many enterprises and research entities. To address this, our study explores a seamless integration of MLLMs into autonomous driving systems by proposing a Zero-Shot Chain-of-Thought (Zero-Shot-CoT) prompt design named PKRD-CoT. PKRD-CoT is based on the four fundamental capabilities of autonomous driving: perception, knowledge, reasoning, and decision-making. This makes it particularly suitable for understanding and responding to dynamic driving environments by mimicking human thought processes step by step, thus enhancing decision-making in real-time scenarios. Our design enables MLLMs to tackle problems without prior experience, thereby increasing their utility within unstructured autonomous driving environments. In experiments, we demonstrate the exceptional performance of GPT-4.0 with PKRD-CoT across autonomous driving tasks, highlighting its effectiveness in autonomous driving scenarios. Additionally, our benchmark analysis reveals the promising viability of PKRD-CoT for other MLLMs, such as Claude, LLava1.6, and Qwen-VL-Plus. Overall, this study contributes a novel and unified prompt-design framework for GPT-4.0 and other MLLMs in autonomous driving, while also rigorously evaluating the efficacy of these widely recognized MLLMs in the autonomous driving domain through comprehensive comparisons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02025 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving Luo, Xuewen Ding, Fan Song, Yinsheng Zhang, Xiaofeng Loo, Junnyong Robotics Artificial Intelligence There is growing interest in leveraging the capabilities of robust Multi-Modal Large Language Models (MLLMs) directly within autonomous driving contexts. However, the high costs and complexity of designing and training end-to-end autonomous driving models make them challenging for many enterprises and research entities. To address this, our study explores a seamless integration of MLLMs into autonomous driving systems by proposing a Zero-Shot Chain-of-Thought (Zero-Shot-CoT) prompt design named PKRD-CoT. PKRD-CoT is based on the four fundamental capabilities of autonomous driving: perception, knowledge, reasoning, and decision-making. This makes it particularly suitable for understanding and responding to dynamic driving environments by mimicking human thought processes step by step, thus enhancing decision-making in real-time scenarios. Our design enables MLLMs to tackle problems without prior experience, thereby increasing their utility within unstructured autonomous driving environments. In experiments, we demonstrate the exceptional performance of GPT-4.0 with PKRD-CoT across autonomous driving tasks, highlighting its effectiveness in autonomous driving scenarios. Additionally, our benchmark analysis reveals the promising viability of PKRD-CoT for other MLLMs, such as Claude, LLava1.6, and Qwen-VL-Plus. Overall, this study contributes a novel and unified prompt-design framework for GPT-4.0 and other MLLMs in autonomous driving, while also rigorously evaluating the efficacy of these widely recognized MLLMs in the autonomous driving domain through comprehensive comparisons. |
| title | PKRD-CoT: A Unified Chain-of-thought Prompting for Multi-Modal Large Language Models in Autonomous Driving |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2412.02025 |