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

Related papers

• MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation [52.739500459903724]
  Large Language Models (LLMs) have demonstrated remarkable planning abilities across various domains, including robotics manipulation and navigation. We propose a novel multi-agent LLM framework that distributes high-level planning and low-level control code generation across specialized LLM agents. We evaluate our approach on nine RLBench tasks, including long-horizon tasks, and demonstrate its ability to solve robotics manipulation in a zero-shot setting.
  arXiv  Detail & Related papers  (2024-11-26T17:53:44Z)
• DualAD: Dual-Layer Planning for Reasoning in Autonomous Driving [1.8434042562191815]
  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.
  arXiv  Detail & Related papers  (2024-09-26T16:58:04Z)
• DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving [69.82743399946371]
  DriveMLM is a framework that can perform close-loop autonomous driving in realistic simulators. We employ a multi-modal LLM (MLLM) to model the behavior planning module of a module AD system. This model can plug-and-play in existing AD systems such as Apollo for close-loop driving.
  arXiv  Detail & Related papers  (2023-12-14T18:59:05Z)
• Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
  This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems. LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
  arXiv  Detail & Related papers  (2023-11-28T03:13:09Z)
• LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving [87.1164964709168]
  This work employs Large Language Models (LLMs) as a decision-making component for complex autonomous driving scenarios. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination.
  arXiv  Detail & Related papers  (2023-10-04T17:59:49Z)
• Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving [6.728693243652425]
  Large Language Models (LLMs) have shown promise in the autonomous driving sector, particularly in generalization and interpretability. We introduce a unique object-level multimodal LLM architecture that merges vectorized numeric modalities with a pre-trained LLM to improve context understanding in driving situations.
  arXiv  Detail & Related papers  (2023-10-03T11:05:14Z)
• Ground Manipulator Primitive Tasks to Executable Actions using Large Language Models [13.827349677538352]
  We propose a novel approach to ground the manipulator primitive tasks to robot low-level actions using large language models (LLMs) In this way, we enable LLMs to generate position/force set-points for hybrid control.
  arXiv  Detail & Related papers  (2023-08-13T16:52:36Z)
• Automatic Rule Induction for Efficient Semi-Supervised Learning [56.91428251227253]
  Semi-supervised learning has shown promise in allowing NLP models to generalize from small amounts of labeled data. Pretrained transformer models act as black-box correlation engines that are difficult to explain and sometimes behave unreliably. We propose tackling both of these challenges via Automatic Rule Induction (ARI), a simple and general-purpose framework.
  arXiv  Detail & Related papers  (2022-05-18T16:50:20Z)
• From proprioception to long-horizon planning in novel environments: A hierarchical RL model [4.44317046648898]
  In this work, we introduce a simple, three-level hierarchical architecture that reflects different types of reasoning. We apply our method to a series of navigation tasks in the Mujoco Ant environment.
  arXiv  Detail & Related papers  (2020-06-11T17:19:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.