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Replacing experts by machine in determining the test road grade of autonomous vehicles: Case study in Hefei and policy implications

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Listed:
  • Wang, Tao
  • Cheng, Yuanle
  • Chen, Bin
  • Cao, Feng
  • Chen, Shukai
  • Tang, Tie-Qiao

Abstract

The road grade classification is a key task for policymakers before autonomous vehicles are tested on the open road. In some pilot cities, classifying the road levels for autonomous vehicle testing requires months of expert consultation. This study seeks to replace the time-consuming process with machine learning methods, using a case study in Hefei to analyze the effectiveness of our approach. We collected data on over 3600 road segments, including road and traffic attributes, and accident records. We constructed several predictive models with various classic machine learning and statistical classification algorithms. We evaluated the strengths and limitations of various algorithms and improved classification performance through data augmentation techniques. Then, we proposed several rules to enhance classification algorithms, effectively mitigating the negative impact of misclassifications in levels 1 and 4. Finally, We tested our algorithms and presented some discussions and the policy implications.

Suggested Citation

  • Wang, Tao & Cheng, Yuanle & Chen, Bin & Cao, Feng & Chen, Shukai & Tang, Tie-Qiao, 2026. "Replacing experts by machine in determining the test road grade of autonomous vehicles: Case study in Hefei and policy implications," Transport Policy, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:trapol:v:175:y:2026:i:c:s0967070x25004093
    DOI: 10.1016/j.tranpol.2025.103866
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    1. Muhammad Atif Butt & Asad Masood Khattak & Sarmad Shafique & Bashir Hayat & Saima Abid & Ki-Il Kim & Muhammad Waqas Ayub & Ahthasham Sajid & Awais Adnan & M. Irfan Uddin, 2021. "Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems," Complexity, Hindawi, vol. 2021, pages 1-11, February.
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