DriVerse：通过多模态轨迹提示和运动对齐实现驾驶模拟的导航世界模型
DriVerse: Navigation World Model for Driving Simulation via Multimodal Trajectory Prompting and Motion Alignment
AI总结：
DriVerse通过多模态轨迹提示和运动对齐技术，实现从单张图像和未来轨迹生成导航驱动的驾驶场景，提升了动态对象的生成精度和时间一致性。
摘要：
本文提出了DriVerse，一种从单张图像和未来轨迹生成导航驱动驾驶场景的生成模型。传统自动驾驶世界模型直接将轨迹或离散控制信号输入生成流程，导致控制输入与2D基础生成模型隐含特征对齐不佳，产生低保真视频输出。一些方法使用粗略文本命令或离散车辆控制信号，缺乏引导细粒度轨迹特定视频生成的精度，不适合评估实际自动驾驶算法。DriVerse引入了两种互补的显式轨迹指导形式：通过预定义趋势词汇将轨迹分词为文本提示，实现无缝语言集成；将3D轨迹转换为2D空间运动先验，增强驾驶场景中静态内容的控制。为进一步处理动态对象，我们引入了轻量级运动对齐模块，专注于帧间一致性，显著提升了长序列中运动元素的时间一致性。DriVerse在nuScenes和Waymo数据集上的未来视频生成任务中，通过少量训练且无需额外数据，均优于专用模型。代码和模型将向公众发布。
英文摘要：
This paper presents DriVerse, a generative model for simulating navigation-driven driving scenes from a single image and a future trajectory. Previous autonomous driving world models either directly feed the trajectory or discrete control signals into the generation pipeline, leading to poor alignment between the control inputs and the implicit features of the 2D base generative model, which results in low-fidelity video outputs. Some methods use coarse textual commands or discrete vehicle control signals, which lack the precision to guide fine-grained, trajectory-specific video generation, making them unsuitable for evaluating actual autonomous driving algorithms. DriVerse introduces explicit trajectory guidance in two complementary forms: it tokenizes trajectories into textual prompts using a predefined trend vocabulary for seamless language integration, and converts 3D trajectories into 2D spatial motion priors to enhance control over static content within the driving scene. To better handle dynamic objects, we further introduce a lightweight motion alignment module, which focuses on the inter-frame consistency of dynamic pixels, significantly enhancing the temporal coherence of moving elements over long sequences. With minimal training and no need for additional data, DriVerse outperforms specialized models on future video generation tasks across both the nuScenes and Waymo datasets. The code and models will be released to the public.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
