Road Infrastructure Perception for Autonomous Driving: Taxonomy, Representations, Tasks, and Challenges

Abstract

Road infrastructure perception is a fundamental component of autonomous driving, as it recovers the structured road elements that define roadway geometry, delimit operational space, and encode traffic regulations. Although individual tasks such as lane detection, drivable-space estimation, traffic-sign recognition, and online map construction have been extensively studied, the field remains fragmented across task definitions, sensing assumptions, output representations, datasets, and evaluation protocols. This survey provides a function-oriented synthesis of road infrastructure perception. We first clarify its scope and organize existing studies into three complementary categories: geometric-structural, functional-spatial, and regulatory-semantic infrastructure. Based on this taxonomy, we review sensing modalities, scene representations, representative methods, benchmarks, and evaluation protocols, emphasizing the field's evolution from image-space recognition toward metric, vectorized, topology-aware, occupancy-based, and rule-grounded scene modeling. We further examine how representation choices affect evaluation and downstream usability, revealing limitations of current benchmarks in relation reasoning, temporal consistency, robustness, and planning-oriented assessment. Finally, we discuss open challenges and future directions toward unified infrastructure representation, open-environment generalization, relation- and rule-aware reasoning, and downstream-oriented evaluation. By connecting tasks, representations, and evaluation under a common functional framework, this survey aims to establish road infrastructure perception as a coherent foundation for map-compatible and behavior-relevant autonomous-driving scene understanding.