Using SD map priors through hybrid raster-vector fusion and an intersection keypoint auxiliary task improves lane perception and topology reasoning on OpenLane-V2 by up to 5.9 OLUS points.
Local Map Construction with SDMap: A Comprehensive Survey
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abstract
Local map construction is a vital component of intelligent driving perception, offering necessary reference for vehicle positioning and planning. Standard Definition map (SDMap), known for its low cost, accessibility, and versatility, has significant potential as prior information for local map perception. This paper mainly reviews the local map construction methods with SDMap, including definitions, general processing flow, and datasets. Besides, this paper analyzes multimodal data representation and fusion methods in SDMap-based local map construction. This paper also discusses key challenges and future directions, such as optimizing SDMap processing, enhancing spatial alignment with real-time data, and incorporating richer environmental information. At last, the review looks forward to future research focusing on enhancing road topology inference and multimodal data fusion to improve the robustness and scalability of local map perception.
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SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving
Using SD map priors through hybrid raster-vector fusion and an intersection keypoint auxiliary task improves lane perception and topology reasoning on OpenLane-V2 by up to 5.9 OLUS points.