CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913767581810688 |
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| author | Yao, Ruoyu Wang, Yubin Liu, Haichao Yang, Rui Peng, Zengqi Zhu, Lei Ma, Jun |
| author_facet | Yao, Ruoyu Wang, Yubin Liu, Haichao Yang, Rui Peng, Zengqi Zhu, Lei Ma, Jun |
| contents | Decision-making and motion planning constitute critical components for ensuring the safety and efficiency of autonomous vehicles (AVs). Existing methodologies typically adopt two paradigms: decision then planning or generation then scoring. However, the former architecture often suffers from decision-planning misalignment that incurs risky situations. Meanwhile, the latter struggles to balance short-term operational metrics (e.g., immediate motion smoothness) with long-term tactical goals (e.g., route efficiency), resulting in myopic or overly conservative behaviors. To address these issues, we introduce CALMM-Drive, a novel Confidence-Aware Large Multimodal Model (LMM) empowered Autonomous Driving framework. Our approach integrates driving task-oriented Chain-of-Thought (CoT) reasoning coupled with Top-K confidence elicitation, which facilitates high-level reasoning to generate multiple candidate decisions with their confidence levels. Furthermore, we propose a novel planning module that integrates a diffusion model for trajectory generation and a hierarchical refinement process to find the optimal trajectory. This framework enables the selection over trajectory candidates accounting for both low-level solution quality and high-level tactical confidence, which avoids the risks within one-shot decisions and overcomes the limitations in short-sighted scoring mechanisms. Comprehensive evaluations in nuPlan closed-loop simulation environments demonstrate the competitive performance of CALMM-Drive across both common and long-tail benchmarks, showcasing a significant advancement in the integration of uncertainty in LMM-empowered AVs. The code will be released upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04209 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model Yao, Ruoyu Wang, Yubin Liu, Haichao Yang, Rui Peng, Zengqi Zhu, Lei Ma, Jun Robotics Decision-making and motion planning constitute critical components for ensuring the safety and efficiency of autonomous vehicles (AVs). Existing methodologies typically adopt two paradigms: decision then planning or generation then scoring. However, the former architecture often suffers from decision-planning misalignment that incurs risky situations. Meanwhile, the latter struggles to balance short-term operational metrics (e.g., immediate motion smoothness) with long-term tactical goals (e.g., route efficiency), resulting in myopic or overly conservative behaviors. To address these issues, we introduce CALMM-Drive, a novel Confidence-Aware Large Multimodal Model (LMM) empowered Autonomous Driving framework. Our approach integrates driving task-oriented Chain-of-Thought (CoT) reasoning coupled with Top-K confidence elicitation, which facilitates high-level reasoning to generate multiple candidate decisions with their confidence levels. Furthermore, we propose a novel planning module that integrates a diffusion model for trajectory generation and a hierarchical refinement process to find the optimal trajectory. This framework enables the selection over trajectory candidates accounting for both low-level solution quality and high-level tactical confidence, which avoids the risks within one-shot decisions and overcomes the limitations in short-sighted scoring mechanisms. Comprehensive evaluations in nuPlan closed-loop simulation environments demonstrate the competitive performance of CALMM-Drive across both common and long-tail benchmarks, showcasing a significant advancement in the integration of uncertainty in LMM-empowered AVs. The code will be released upon acceptance. |
| title | CALMM-Drive: Confidence-Aware Autonomous Driving with Large Multimodal Model |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.04209 |