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Road infrastructure perception for autonomous driving surveyed

By PulseAugur Editorial · [1 sources] ·

This paper provides a comprehensive survey of road infrastructure perception for autonomous driving, organizing existing research into geometric-structural, functional-spatial, and regulatory-semantic categories. It reviews sensing modalities, scene representations, methods, and benchmarks, highlighting the field's shift towards more sophisticated scene modeling. The survey also identifies limitations in current benchmarks regarding reasoning, consistency, and planning-oriented assessment, and discusses future research directions for unified representation, generalization, and evaluation. AI

IMPACT Provides a foundational taxonomy and identifies key challenges for advancing autonomous driving perception systems.

RANK_REASON The item is an academic paper surveying a specific technical field within AI. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Road infrastructure perception for autonomous driving surveyed

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The item is an academic paper surveying a specific technical field within AI. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Guoyang Zhao, Weiqing Qi, Kai Zhang, Zhihai Bi, Tianshuai Hu, Zeying Gong, Dezong Zhao, Jun Ma ·

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

    arXiv:2610.33758v1 Announce Type: new 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 individua…

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