GPD-1: Generative Pre-training for Driving

• URL: http://arxiv.org/abs/2412.08643v1
• Date: Wed, 11 Dec 2024 18:59:51 GMT
• Title: GPD-1: Generative Pre-training for Driving
• Authors: Zixun Xie, Sicheng Zuo, Wenzhao Zheng, Yunpeng Zhang, Dalong Du, Jie Zhou, Jiwen Lu, Shanghang Zhang,
• Abstract summary: We propose a unified Generative Pre-training for Driving (GPD-1) model to accomplish all these tasks. We represent each scene with ego, agent, and map tokens and formulate autonomous driving as a unified token generation problem. Our GPD-1 successfully generalizes to various tasks without finetuning, including scene generation, traffic simulation, closed-loop simulation, map prediction, and motion planning.
• Score: 77.06803277735132
• License:
• Abstract: Modeling the evolutions of driving scenarios is important for the evaluation and decision-making of autonomous driving systems. Most existing methods focus on one aspect of scene evolution such as map generation, motion prediction, and trajectory planning. In this paper, we propose a unified Generative Pre-training for Driving (GPD-1) model to accomplish all these tasks altogether without additional fine-tuning. We represent each scene with ego, agent, and map tokens and formulate autonomous driving as a unified token generation problem. We adopt the autoregressive transformer architecture and use a scene-level attention mask to enable intra-scene bi-directional interactions. For the ego and agent tokens, we propose a hierarchical positional tokenizer to effectively encode both 2D positions and headings. For the map tokens, we train a map vector-quantized autoencoder to efficiently compress ego-centric semantic maps into discrete tokens. We pre-train our GPD-1 on the large-scale nuPlan dataset and conduct extensive experiments to evaluate its effectiveness. With different prompts, our GPD-1 successfully generalizes to various tasks without finetuning, including scene generation, traffic simulation, closed-loop simulation, map prediction, and motion planning. Code: https://github.com/wzzheng/GPD.

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