Autonomous Vehicle Test Case Generation via Real-World Data Analysis
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Solution Overview
Problem
The challenge in testing autonomous vehicles lies in the vast and infinite set of potential driving situations, making it difficult to generate comprehensive test cases efficiently, as existing methods are limited by finite application cases and lack automation.
Innovation Solution
A method utilizing real driver experience data, predefined system application cases, and safety/reliability-related cases to automatically generate test cases, combining mass data analysis and prediction to identify and merge relevant driving situations, creating a comprehensive set of test scenarios that can be interpreted by machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If test cases are manually specified by test engineers using software test tools, then test case coverage is limited to finite identified application cases, but the process requires significant manual effort and time
Solution Approach 1:
The patent creates virtual copies of real-world driving situations by analyzing mass data from actual drivers and generating corresponding test cases. Instead of manually creating test cases for every possible scenario, the system automatically copies and adapts real driving behaviors into structured test cases, dramatically reducing manual effort while maintaining comprehensive coverage
Solution Approach 2:
The system enables self-service by automatically generating test cases from mass data without requiring manual intervention for each test case creation. The automated platform analyzes driving situations, identifies patterns, and generates test cases independently, freeing test engineers from repetitive manual specification tasks
2Ease of operation
If the scope of test cases is restricted to finite identified application cases, then manual specification becomes manageable, but the system cannot adequately cover the infinite variety of real-world driving situations
Solution Approach 1:
The patent transforms the static, finite set of manually defined application cases into a dynamic system that continuously adapts to new driving situations. By processing mass data from real drivers, the system automatically discovers and incorporates new driving scenarios, ensuring the test case scope evolves to match the infinite variety of real-world conditions
Solution Approach 2:
The system adds a new dimension to test case generation by incorporating mass data analysis from real-world driving. This transforms the traditional single-dimension approach (manual specification of finite cases) into a multi-dimensional system that combines automated data processing, pattern recognition, and virtual environment simulation to cover unlimited scenarios
3Reliability
If virtual test environments are used to test autonomous vehicles without risk, then safety is improved, but the complexity of generating comprehensive test scenarios increases
Solution Approach 1:
The patent creates virtual copies of real driving environments and situations, allowing safe testing of autonomous vehicles while maintaining realistic scenario complexity. By copying actual driving behaviors and environmental conditions into virtual settings, the system achieves both safety and comprehensive scenario coverage without manual complexity
Solution Approach 2:
The system replaces manual mechanical processes of test scenario design with automated computational processes. Instead of test engineers manually designing complex test scenarios, the system uses automated analysis of mass data and algorithmic generation of test cases, reducing human effort while handling intricate driving situations
Data Source
AI summary
Test cases for autonomous vehicles are generated automatically by using data which have been collected from vehicles participating in public road traffic. A test planning system for autonomous vehicle includes defined application cases for autonomous vehicles. The vehicles are configured to identify test cases with prediction analyzes of a reference catalog of driving situations and the defined application cases, and compare, via a comparative analyzes, the test cases and the defined application cases to compile an expanded set of test cases, wherein the expanded set of test cases are compared to the defined application cases to output a complete set of test cases. The system also includes a central database configured to query the complete set of test cases.