从静态地图转向动态感知，让自动驾驶更智能可靠
From Static to Dynamic: a Survey of Topology-Aware Perception in Autonomous Driving
- 用传感器实时构建道路拓扑，替代传统静态地图
- 融合语义关系与先验知识，提升环境理解能力
- 适合关注自动驾驶感知进化的研究者与工程师
实现自动驾驶的关键在于拓扑感知，即对驾驶环境进行结构化理解，重点关注车道拓扑与道路语义。本文系统综述了该方向下的四个核心研究领域：矢量地图构建、拓扑结构建模、先验知识融合以及基于语言模型的感知。观察到统一趋势：从静态预构建地图向动态、传感器驱动的感知范式转变。传统静态地图虽提供语义上下文，但构建成本高、难以实时更新，且跨区域泛化能力差，限制了可扩展性。相比之下，动态表示利用车载传感器数据实现实时地图构建与拓扑推理。四个研究方向分别通过紧凑空间建模、语义关系推理、鲁棒领域知识集成以及预训练语言模型驱动的多模态场景理解，共同推动更自适应、可扩展、可解释的自动驾驶系统发展。
原文摘要 · Abstract (English)
The key to achieving autonomous driving lies in topology-aware perception, the structured understanding of the driving environment with an emphasis on lane topology and road semantics. This survey systematically reviews four core research directions under this theme: vectorized map construction, topological structure modeling, prior knowledge fusion, and language model-based perception. Across these directions, we observe a unifying trend: a paradigm shift from static, pre-built maps to dynamic, sensor-driven perception. Specifically, traditional static maps have provided semantic context for autonomous systems. However, they are costly to construct, difficult to update in real time, and lack generalization across regions, limiting their scalability. In contrast, dynamic representations leverage on-board sensor data for real-time map construction and topology reasoning. Each of the four research directions contributes to this shift through compact spatial modeling, semantic relational reasoning, robust domain knowledge integration, and multimodal scene understanding powered by pre-trained language models. Together, they pave the way for more adaptive, scalable, and explainable autonomous driving systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。