DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving
This addresses the challenge of enabling autonomous vehicles to reason like humans in critical situations, though it is incremental as it builds on existing LLM and rule-based methods.
The paper tackles the problem of autonomous driving by proposing DualAD, a framework that uses a dual-layer approach with a rule-based planner and an LLM for reasoning, which significantly outperforms rule-based planners without reasoning in closed-loop experiments.
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.