The rapid advancement of autonomous systems, including self-driving vehicles and drones, has intensified the need to forge true Spatial Intelligence from multi-modal onboard sensor data. While foundation models excel in single-modal contexts, integrating their capabilities across diverse sensors like cameras and LiDAR to create a unified understanding remains a formidable challenge. This paper presents a comprehensive framework for multi-modal pre-training, identifying the core set of techniques driving progress toward this goal. We dissect the interplay between foundational sensor characteristics and learning strategies, evaluating the role of platform-specific datasets in enabling these advancements. Our central contribution is the formulation of a unified taxonomy for pre-training paradigms: ranging from single-modality baselines to sophisticated unified frameworks that learn holistic representations for advanced tasks like 3D object detection and semantic occupancy prediction. Furthermore, we investigate the integration of textual inputs and occupancy representations to facilitate open-world perception and planning. Finally, we identify critical bottlenecks, such as computational efficiency and model scalability, and propose a roadmap toward general-purpose multi-modal foundation models capable of achieving robust Spatial Intelligence for real-world deployment.

翻译：自动驾驶车辆与无人机等自主系统的快速发展，迫切要求从多模态车载传感器数据中锻造出真正的空间智能。尽管基础模型在单模态场景中表现出色，但如何整合其在相机与激光雷达等异构传感器上的能力以形成统一的环境理解，仍是一个严峻挑战。本文提出了一个全面的多模态预训练框架，系统梳理了推动该目标实现的核心技术体系。我们深入剖析了基础传感器特性与学习策略之间的相互作用，并评估了平台专用数据集对这些进展的支撑作用。本研究的核心贡献在于构建了统一的预训练范式分类体系：从单模态基线方法到学习整体表征的先进统一框架，涵盖三维目标检测与语义占据预测等高级任务。此外，我们探究了文本输入与占据表征的融合机制，以促进开放世界感知与规划能力。最后，我们指出了计算效率与模型可扩展性等关键瓶颈，并提出了通向通用多模态基础模型的路线图，旨在为实现实际部署所需的鲁棒空间智能奠定基础。