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Autonomous Driving Open Road Complexity Classification
Hongpan Yue1, Yichun Jia1, Tongfei Li2
1Beijing Connected and Autonomous Vehicles Technology Co., Ltd., Beijing 100176, China.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
This study introduces a framework to classify road complexity for autonomous vehicle testing. It uses road features, traffic data, and safety metrics to ensure safer and more efficient development of intelligent and connected vehicles.
Area of Science:
- Intelligent Transportation Systems
- Autonomous Driving Technology
- Road Safety Engineering
Background:
- Autonomous vehicle (AV) development relies heavily on open-road testing.
- Classifying road complexity is essential for safe and efficient AV testing.
- Existing methods lack a systematic approach to road complexity assessment.
Purpose of the Study:
- To propose a scientific and systematic framework for classifying road complexity.
- To quantify road complexity using integrated metrics.
- To provide guidance for autonomous driving testing and development.
Main Methods:
- Integration of static road features, dynamic traffic flow indicators, and safety event metrics.
- Application of the Analytic Hierarchy Process (AHP) for quantification.
- Categorization of roads into five distinct complexity levels.
Main Results:
- A comprehensive framework for road complexity classification was developed.
- Roads were categorized into five levels based on integrated metrics.
- The framework offers guidance for phased road opening and algorithm optimization.
Conclusions:
- The proposed framework provides a scientific basis for road complexity classification in AV testing.
- This classification supports phased testing, algorithm refinement, and infrastructure deployment.
- The study offers theoretical and practical support for intelligent and connected vehicle demonstration zones.
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