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Data Collection and Annotation Method for Radar on Some Key Scenarios
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Kaibo Huang1, Weiwen Deng1, Ying Wang2, Rui Zhao1, Juan Ding3
Automotive Engineering | 2024, 46(12) : 2257 - 2266
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Automotive Engineering | 2024, 46(12): 2257-2266
• Selected Papers •
Data Collection and Annotation Method for Radar on Some Key Scenarios
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Kaibo Huang1, Weiwen Deng1, Ying Wang2, Rui Zhao1, Juan Ding3
Affiliations
  • 1. School of Transportation Science and Engineering，Beihang University，Beijing 100191
  • 2. College of Computer Science and Technology，Jilin University，Changchun 130015
  • 3. Jiaxing Nanhu University，Jiaxing 314000
Published: 2024-12-25 doi: 10.19562/j.chinasae.qcgc.2024.12.012
Outline
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False alarm and missed alarm of automotive radar are key factors affecting the safety and reliability of autonomous driving systems，thus requiring a large amount of labeled test data for targeted research. However，the occurrence probability of false alarm and missed alarm is low，and the unstable status of radar targets makes it difficult to label them. Therefore，in this paper，firstly efficient test schemes are designed to obtain key radar data based on the generation mechanism of radar false alarm and missed alarm. Then，by constructing a correlation function to quantify the correlation between radar targets and scene targets and using genetic algorithms to optimize this function，an automatic labeling method for radar targets is established. Finally，the effectiveness of the proposed method is verified through real data acquisition. The experimental results show that the proposed method can efficiently obtain crucial false alarm and missed alarm data. The labeling method in this paper can accurately identify radar targets corresponding to scene targets and distinguish between false alarm and real targets.

autonomous driving  /  millimeter-wave radar  /  false alarm  /  missed alarm  /  object labeling
Kaibo Huang, Weiwen Deng, Ying Wang, Rui Zhao, Juan Ding. Data Collection and Annotation Method for Radar on Some Key Scenarios[J]. Automotive Engineering, 2024 , 46 (12) : 2257 -2266 . DOI: 10.19562/j.chinasae.qcgc.2024.12.012
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Year 2024 volume 46 Issue 12
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Article Info
doi: 10.19562/j.chinasae.qcgc.2024.12.012
  • Receive Date：2024-05-11
  • Online Date：2025-07-21
  • Published：2024-12-25
Article Data
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History
  • Received：2024-05-11
  • Revised：2024-06-13
Funding
Affiliations
    1. School of Transportation Science and Engineering，Beihang University，Beijing 100191
    2. College of Computer Science and Technology，Jilin University，Changchun 130015
    3. Jiaxing Nanhu University，Jiaxing 314000
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