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Full Coverage Testing Method for Automated Driving System in Logical Scenario Parameters Space.

Haitao Min1, Zhiqiang Zhang1,2, Tianxin Fan1

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Summary
This summary is machine-generated.

This study introduces a new method for testing automated driving systems (ADS) by ensuring complete coverage of logical scenarios. The hybrid algorithm efficiently selects parameters, improving safety and test reproducibility.

Keywords:
automated driving systemconcrete scenario representativenessfull coverage testingtest scenario

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Scenario-based testing is crucial for automated driving systems (ADS) safety.
  • Current methods struggle with fairness and coverage due to parameter space complexity and system variability.

Purpose of the Study:

  • To develop an automated driving system testing method for comprehensive logical scenario coverage.
  • To propose a quantitative method for evaluating scenario representativeness.
  • To create an efficient algorithm for selecting representative parameters.

Main Methods:

  • A quantitative evaluation of scenario representativeness using naturalistic driving data and hazard analysis.
  • A hybrid algorithm combining heat-guided hierarchical search and genetic optimization for parameter selection.
  • Validation through empirical studies on lead vehicle braking and cut-in scenarios.

Main Results:

  • Achieved 100% coverage of the logical scenario parameter space with an 8% boundary fitting error.
  • Outperformed Monte Carlo (84.3%), combinatorial testing (86.5%), and importance sampling (72.0%) in coverage and error rates.
  • Demonstrated efficient selection of representative parameters for exhaustive logical scenario coverage.

Conclusions:

  • The proposed method ensures exhaustive coverage of the logical scenario space with fewer concrete scenarios.
  • This approach enhances the consistency, reproducibility, and efficiency of scenario generation for ADS testing.
  • The method effectively supports the development of robust safety evaluation frameworks for automated driving systems.