DualAD用双层架构模拟人类驾驶推理，提升复杂场景决策能力。
DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving
- 底层规则规划处理常规驾驶，上层文本编码+大模型处理危险情境
- 零样本下显著超越传统规则模型，文本编码提升场景理解力
- 框架可随更强LLM升级，适合需要安全推理的自动驾驶研究
我们提出一种新型自动驾驶框架DualAD，旨在模仿人类驾驶时的推理过程。该框架包含两层：底层为基于规则的运动规划器，负责处理无需深度推理的常规驾驶任务；上层则包含基于规则的文本编码器，将绝对状态下的驾驶场景转换为文本描述，并交由大型语言模型（LLM）进行决策。当底层决策可能引发危险时，上层会介入干预，模拟人类在关键时刻的判断。闭环实验表明，仅使用零样本预训练模型，DualAD显著优于缺乏推理能力的传统规则规划器。实验还验证了文本编码器的有效性，显著增强了模型对场景的理解能力。此外，集成后的DualAD随着更强的LLM而表现提升，展现出持续优化潜力。代码与基准测试已开源于github.com/TUM-AVS/DualAD。
原文摘要 · Abstract (English)
We present a novel autonomous driving framework, DualAD, designed to imitate human reasoning during driving. DualAD comprises two layers: a rule-based motion planner at the bottom layer that handles routine driving tasks requiring minimal reasoning, and an upper layer featuring a rule-based text encoder that converts driving scenarios from absolute states into text description. This text is then processed by a large language model (LLM) to make driving decisions. The upper layer intervenes in the bottom layer's decisions when potential danger is detected, mimicking human reasoning in critical situations. Closed-loop experiments demonstrate that DualAD, using a zero-shot pre-trained model, significantly outperforms rule-based motion planners that lack reasoning abilities. Our experiments also highlight the effectiveness of the text encoder, which considerably enhances the model's scenario understanding. Additionally, the integrated DualAD model improves with stronger LLMs, indicating the framework's potential for further enhancement. Code and benchmarks are available at github.com/TUM-AVS/DualAD.
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