Continuous Integration Validation for Driver Assistance Systems
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Solution Overview
Problem
Current continuous integration approaches for Automated Driver Assist Systems (ADAS) and Automated Driving Systems (ADS) are limited to simple sub-component verification testing and lack comprehensive validation in real-world traffic scenarios, due to complexity and the large number of possible traffic scenarios that cannot be fully described or replicated.
Innovation Solution
A method and system that utilize real-time measurement data from vehicle sensors to create a test data set for continuous integration testing, where the driver assistance system's software is compiled, tested, and validated at predetermined intervals, allowing for automatic validation of customer-specific scenarios and performance assessment without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous integration approaches are applied to ADAS/ADS, then error detection and fixing capability is improved, but the complexity of implementing comprehensive test cases increases significantly
Solution Approach 1:
The patent creates virtual copies of real-world traffic scenarios through simulated test cases. Instead of physically reproducing complex real-world situations, the system generates digital representations of traffic scenarios, vehicles, sensors, and environmental conditions that can be repeatedly tested in a controlled virtual environment while maintaining realistic test conditions.
Solution Approach 2:
The patent performs preliminary generation and validation of test cases before actual ADAS/ADS deployment. Test scenarios, including edge cases and failure conditions, are pre-configured and stored in a database, allowing comprehensive testing to be performed in advance rather than requiring complex real-time test setup during continuous integration cycles.
2Reliability
If all traffic scenarios are tested to ensure functional adequacy, then system reliability is improved, but the time and resources required for testing increase exponentially
Solution Approach 1:
The patent implements a layered testing strategy where critical safety-related scenarios are tested exhaustively, while less critical scenarios use representative sampling. The system prioritizes testing high-risk situations (e.g., pedestrian detection, collision avoidance) with comprehensive coverage, while using statistical sampling for lower-priority scenarios, achieving adequate functional validation without exhaustive testing of all possible scenarios.
Solution Approach 2:
The patent divides the vast space of traffic scenarios into manageable segments or categories (e.g., urban driving, highway driving, adverse weather, edge cases). Each segment is tested independently with focused test suites, and results are aggregated to assess overall system functional adequacy, reducing the time required compared to testing all scenarios as a monolithic set.
3Measurement precision
If real-world traffic data is used for testing, then test realism is improved, but the difficulty of reproducing and documenting test cases increases
Solution Approach 1:
The patent creates structured digital replicas of real-world traffic data, extracting essential characteristics (sensor readings, vehicle states, environmental conditions) and representing them in standardized formats. This allows realistic test scenarios to be reproduced consistently across different testing instances without requiring access to the original complex real-world data sources.
Solution Approach 2:
The patent develops a universal test case framework that can accommodate multiple types of real-world scenarios through a common data structure and execution engine. The system uses parameterized test templates that can be configured with different scenario parameters (weather, traffic conditions, vehicle types), enabling the same testing infrastructure to handle diverse real-world situations without requiring separate reproduction mechanisms for each scenario type.
Data Source
AI summary
A method uses a continuous integration approach for improving driver assistance systems. The method uses a test data set (11) with a time series of input data and output data of the driver assistance system and is formed during driving in a real traffic. A system-under-test is formed by a continuously changed overall software. A data-driven validation is carried out at predetermined time intervals. The method simultaneously matches (14) the output of a current software version with ground-truth data (13), which results in an assessment of a performance of the current software version and continues by forming a performance statistic on all differences and their performance score and evaluating the changes to the overall software.