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Building a Lane-Change Cut-in Scenario Library for Simulation Testing Based on Naturalistic Driving Data
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Hua SONG1, 2, Xianjing WU1, 2, Qiong WU1, 2, Bo CHEN1, 2, Zhao DING1, 2
Chinese Journal of Automotive Engineering | 2025, 15(4) : 528 - 538
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Chinese Journal of Automotive Engineering | 2025, 15(4): 528-538
• Intelligent & Connected Technologies Section/Editor in Chief：GAO Zhenhai •
Building a Lane-Change Cut-in Scenario Library for Simulation Testing Based on Naturalistic Driving Data
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Hua SONG1, 2, Xianjing WU1, 2, Qiong WU1, 2, Bo CHEN1, 2, Zhao DING1, 2
Affiliations
  • 1 Anhui Jianghuai Automobile Group Co.，Ltd.，Hefei 230601，China
  • 2 Anhui Provincial Key Laboratory of Automotive Intelligent Connected Technology，Hefei 230601，China
Published: 2025-07-20 doi: 10.3969/j.issn.2095‒1469.2025.04.10
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Virtual simulation testing has become the industry-wide norm for intelligent connected vehicles. Such testing demands a rich set of simulation scenarios. Based on data collected from naturalistic highway-driving scenarios, the paper develops an automated strategy for extracting lane-change cut-in events. A perception risk coefficient is introduced, and one-way ANOVA is used to analyze discrete factors while Pearson correlation analysis is employed for continuous factors, revealing the relationships between scenario elements and risk and identifying the key influencing factors. Typical scenarios are then derived with K-Means clustering and the elbow method, key parameters are retained, and each scenario is constructed on the Prescan simulation platform. The results show that this method can effectively extract critical lane-change cut-in scenarios from real-world data, cluster them into typical scenarios, and reconstruct these scenarios on the simulation platform, providing scientific support for autonomous-driving system testing.

natural driving data  /  lane-change and cut-in  /  perception risk coefficient  /  one-way ANOVA  /  pearson correlation  /  K-Means clustering  /  elbow method
Hua SONG, Xianjing WU, Qiong WU, Bo CHEN, Zhao DING. Building a Lane-Change Cut-in Scenario Library for Simulation Testing Based on Naturalistic Driving Data[J]. Chinese Journal of Automotive Engineering, 2025 , 15 (4) : 528 -538 . DOI: 10.3969/j.issn.2095‒1469.2025.04.10
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Year 2025 volume 15 Issue 4
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Article Info
doi: 10.3969/j.issn.2095‒1469.2025.04.10
  • Receive Date：2025-01-06
  • Online Date：2025-09-10
  • Published：2025-07-20
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  • Received：2025-01-06
  • Revised：2025-02-14
Affiliations
    1 Anhui Jianghuai Automobile Group Co.，Ltd.，Hefei 230601，China
    2 Anhui Provincial Key Laboratory of Automotive Intelligent Connected Technology，Hefei 230601，China
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