Doe-1: Closed-Loop Autonomous Driving with Large World Model

• URL: http://arxiv.org/abs/2412.09627v1
• Date: Thu, 12 Dec 2024 18:59:59 GMT
• Title: Doe-1: Closed-Loop Autonomous Driving with Large World Model
• Authors: Wenzhao Zheng, Zetian Xia, Yuanhui Huang, Sicheng Zuo, Jie Zhou, Jiwen Lu,
• Abstract summary: We propose a large Driving wOrld modEl (Doe-1) for unified perception, prediction, and planning. We use free-form texts for perception and generate future predictions directly in the RGB space with image tokens. For planning, we employ a position-aware tokenizer to effectively encode action into discrete tokens.
• Score: 63.99937807085461
• License:
• Abstract: End-to-end autonomous driving has received increasing attention due to its potential to learn from large amounts of data. However, most existing methods are still open-loop and suffer from weak scalability, lack of high-order interactions, and inefficient decision-making. In this paper, we explore a closed-loop framework for autonomous driving and propose a large Driving wOrld modEl (Doe-1) for unified perception, prediction, and planning. We formulate autonomous driving as a next-token generation problem and use multi-modal tokens to accomplish different tasks. Specifically, we use free-form texts (i.e., scene descriptions) for perception and generate future predictions directly in the RGB space with image tokens. For planning, we employ a position-aware tokenizer to effectively encode action into discrete tokens. We train a multi-modal transformer to autoregressively generate perception, prediction, and planning tokens in an end-to-end and unified manner. Experiments on the widely used nuScenes dataset demonstrate the effectiveness of Doe-1 in various tasks including visual question-answering, action-conditioned video generation, and motion planning. Code: https://github.com/wzzheng/Doe.

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