Traffic Scenario File Generation From Real Drive Test Clips
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Solution Overview
Problem
Current autonomous driving systems face challenges in fully testing and verifying their algorithms due to the complexity and cost of reproducing complex driving scenarios, leading to inefficiencies in algorithm iteration and update processes.
Innovation Solution
A simulation traffic scenario file generation method that extracts key data from real-world drive test data to create a compact description file, allowing for the reproduction of authentic and complete scenarios in simulation tests, thereby accelerating algorithm iteration and improving testing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full drive test data is used for scenario reconstruction, then scenario authenticity is improved, but data processing time and storage requirements increase
Solution Approach 1:
The patent extracts only the essential elements needed for scenario reconstruction from the full drive test data. This includes identifying key traffic participants, their states, and relevant environmental features, while discarding redundant information. The extraction process maintains scenario authenticity by preserving critical data points necessary for accurate reproduction of driving scenarios.
Solution Approach 2:
The patent segments the drive test data into distinct components: scenario metadata, traffic participant data, environmental data, and sensor data. Each segment is processed and stored separately, allowing for efficient retrieval and reconstruction of specific scenario elements without processing the entire dataset, thereby reducing construction time while maintaining completeness.
2Reliability
If comprehensive drive test data is retained, then scenario completeness is improved, but data storage requirements increase
Solution Approach 1:
The system extracts and retains only the essential data elements required for complete scenario reconstruction. This includes key traffic participant information, critical sensor readings, and necessary environmental conditions. By extracting only what is essential, the system maintains scenario completeness while dramatically reducing storage requirements compared to retaining full drive test data.
Solution Approach 2:
Instead of storing all drive test data and filtering during reconstruction, the system inverts the approach by pre-processing and storing only the essential extracted elements. This inverted strategy reduces storage volume from the beginning while ensuring all necessary information for complete scenario reconstruction is preserved in the compact format.
3Measurement precision
If detailed drive test data is processed, then scenario accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent divides the complex data processing task into segmented stages: data extraction, filtering, structuring, and validation. Each stage handles specific aspects of the data with dedicated processing logic, reducing overall complexity while maintaining accuracy. The segmentation allows parallel processing of different data types and simplifies error handling.
Solution Approach 2:
The system introduces an intermediary data structure that bridges the raw drive test data and the final scenario representation. This intermediary format serves as a standardized buffer that simplifies subsequent processing steps, reducing complexity by providing a consistent interface between data ingestion and scenario construction while preserving measurement precision.
Data Source
AI summary
A simulation traffic scenario file generation method includes obtaining drive test data, where the drive test data includes traffic scenario data collected when an autonomous vehicle performs a driving test on a real road; determining a first moment at which the autonomous vehicle enters a first driving state or generates a request for entering the first driving state during the driving test; determining first clip data from the drive test data based on the first moment and ego vehicle information of the autonomous vehicle during the driving test; and generating a description file of a simulation traffic scenario based on at least the first clip data.