研究人员推出多个用于生成逼真且可控驾驶场景的新框架，这对于训练自动驾驶汽车至关重要。DriveWAM将视频扩散Transformer适配到自回归动作策略的创建中，整合了场景理解和记忆以实现长时规划。AnyScene提供了一个统一的以占用为中心的模型，能够从任意BEV布局进行精确控制，并生成时间上一致的多视图视频。DriveGen3D结合了高效视频扩散与3D场景重建，用于高质量、可控的动态场景，支持长驾驶视频和3D表示。此外，还策划了一个名为Nuplan-Occ的新数据集，以促进自动驾驶领域的大规模生成建模和下游应用。 AI
影响 合成数据生成方面的这些进步，通过提供更逼真和可控的训练环境，有望加速自动驾驶系统的开发和测试。
排序理由 多篇研究论文介绍了用于驾驶场景生成的新方法和数据集。
AI 生成摘要 · Google Gemini · 来自 6 个来源。 我们如何撰写摘要 →
arXiv:2605.28544v1 Announce Type: new Abstract: Pretrained foundation models have become an important basis for end-to-end autonomous driving. In contrast to vision-language models pretrained primarily on static image-text pairs, video generative models capture temporal dynamics …
Pretrained foundation models have become an important basis for end-to-end autonomous driving. In contrast to vision-language models pretrained primarily on static image-text pairs, video generative models capture temporal dynamics and motion priors that are naturally suited for …
arXiv:2605.26113v1 Announce Type: cross Abstract: Generating high-fidelity and controllable synthetic data is critical for advancing end-to-end autonomous driving, particularly for addressing the long tail of rare safety-critical scenarios. Existing occupancy-guided methods typic…
arXiv:2510.15264v3 Announce Type: replace Abstract: We present DriveGen3D, a novel framework for generating high-quality and highly controllable dynamic 3D driving scenes that addresses critical limitations in existing methodologies. Current approaches to driving scene synthesis …
arXiv:2510.22973v2 Announce Type: replace Abstract: Driving scene generation is a critical domain for autonomous driving, enabling downstream applications, including perception and planning evaluation. Occupancy-centric methods have recently achieved state-of-the-art results by o…
Generating high-fidelity and controllable synthetic data is critical for advancing end-to-end autonomous driving, particularly for addressing the long tail of rare safety-critical scenarios. Existing occupancy-guided methods typically rely on shallow conditioning mechanisms and r…