AD-H: Autonomous Driving with Hierarchical Agents

• URL: http://arxiv.org/abs/2406.03474v1
• Date: Wed, 5 Jun 2024 17:25:46 GMT
• Title: AD-H: Autonomous Driving with Hierarchical Agents
• Authors: Zaibin Zhang, Shiyu Tang, Yuanhang Zhang, Talas Fu, Yifan Wang, Yang Liu, Dong Wang, Jing Shao, Lijun Wang, Huchuan Lu,
• Abstract summary: We propose to connect high-level instructions and low-level control signals with mid-level language-driven commands. We implement this idea through a hierarchical multi-agent driving system named AD-H.
• Score: 64.49185157446297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the impressive capabilities of multimodal large language models (MLLMs), recent works have focused on employing MLLM-based agents for autonomous driving in large-scale and dynamic environments. However, prevalent approaches often directly translate high-level instructions into low-level vehicle control signals, which deviates from the inherent language generation paradigm of MLLMs and fails to fully harness their emergent powers. As a result, the generalizability of these methods is highly restricted by autonomous driving datasets used during fine-tuning. To tackle this challenge, we propose to connect high-level instructions and low-level control signals with mid-level language-driven commands, which are more fine-grained than high-level instructions but more universal and explainable than control signals, and thus can effectively bridge the gap in between. We implement this idea through a hierarchical multi-agent driving system named AD-H, including a MLLM planner for high-level reasoning and a lightweight controller for low-level execution. The hierarchical design liberates the MLLM from low-level control signal decoding and therefore fully releases their emergent capability in high-level perception, reasoning, and planning. We build a new dataset with action hierarchy annotations. Comprehensive closed-loop evaluations demonstrate several key advantages of our proposed AD-H system. First, AD-H can notably outperform state-of-the-art methods in achieving exceptional driving performance, even exhibiting self-correction capabilities during vehicle operation, a scenario not encountered in the training dataset. Second, AD-H demonstrates superior generalization under long-horizon instructions and novel environmental conditions, significantly surpassing current state-of-the-art methods. We will make our data and code publicly accessible at https://github.com/zhangzaibin/AD-H

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