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: this https URL.
Abstract (translated)
对驾驶场景的演化进行建模对于评估和自主驾驶系统的决策至关重要。现有的大多数方法侧重于场景演化的某一方面，如地图生成、运动预测和轨迹规划等。在本文中，我们提出了一种统一的驾驶预训练模型（GPD-1），可以在不进行额外微调的情况下完成所有这些任务。我们将每个场景表示为自我车辆（ego）、代理（agent）和地图标记，并将自动驾驶问题表述为一个统一的令牌生成问题。我们采用了自回归Transformer架构，并使用场景级注意力掩码以实现场景内的双向交互。对于自我车辆和代理标记，我们提出了一种层次位置编码器来有效地编码二维位置和方向信息。对于地图标记，我们训练了一个地图向量量化自动编码器，以高效地将自我中心语义地图压缩为离散标记。我们在大规模的nuPlan数据集上对GPD-1进行预训练，并通过广泛的实验评估其有效性。使用不同的提示词，我们的GPD-1成功泛化到了各种任务中，无需微调即可完成场景生成、交通模拟、闭环模拟、地图预测和运动规划等任务。代码：此 https URL。
URL
https://arxiv.org/abs/2412.08643
