Traffic Scenario Similarity Using Movement Profile Sequences
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Solution Overview
Problem
The high cost and time-consuming nature of testing autonomous vehicle functions due to the need for extensive real-world testing and redundant scenario validation, which often results in inefficient use of resources and failure to account for critical scenarios effectively.
Innovation Solution
A method for determining similarity values of traffic scenarios based on movement profiles of traffic participants, using a test device to generate and compare sequences of movement profiles, thereby identifying similar scenarios and optimizing the testing process through virtual simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive real-world testing with billions of kilometers is conducted to verify autonomous driving functions, then testing completeness is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of traffic scenarios through simulation environments that replicate real-world driving conditions. Instead of physically testing billions of kilometers, the system generates and executes virtual scenario copies that preserve the essential characteristics and challenges of real traffic situations, enabling comprehensive validation without proportional time investment
Solution Approach 2:
The system performs preliminary analysis of traffic scenarios to identify critical and representative cases before execution. By pre-processing scenario data, categorizing traffic situations, and selecting the most relevant test cases, the system prepares a optimized test suite that covers essential validation requirements with minimal redundant testing
2Reliability
If all scenarios from databases with thousands of entries are tested to ensure comprehensive coverage, then scenario coverage is improved, but productivity decreases due to redundant testing
Solution Approach 1:
The system extracts and identifies duplicate or highly similar scenarios from the database using similarity comparison algorithms. By detecting redundant test cases through movement profile analysis and scenario feature matching, the system removes unnecessary duplicates from the test suite, retaining only representative scenarios that provide unique validation value
Solution Approach 2:
Instead of testing all scenarios exhaustively, the system applies partial action by selecting and executing only the most representative and non-redundant subset. Through similarity thresholding and scenario prioritization, the system performs sufficient testing on critical cases without the excessive action of repeating tests on identical or near-identical scenarios
3Adaptability or versatility
If continuous collection of new test data through test drives is performed to expand scenario databases, then scenario diversity is improved, but loss of time and resources increase
Solution Approach 1:
The system implements feedback mechanisms that continuously analyze newly collected test data against the existing scenario database. By comparing movement profiles, traffic participant behaviors, and scenario characteristics, the system provides feedback on whether new data represents genuinely novel scenarios or duplicates existing patterns, enabling intelligent decisions about data retention and database expansion
Data Source
AI summary
A method for determining similarity values of traffic scenarios based on movement profiles of traffic participants in the traffic scenarios includes: generating, by a test device, at least one movement profile of a traffic participant using measurement data, wherein the at least one movement profile comprises at least one movement element, and wherein a new segment in the at least one movement profile begins with a change of a movement element; generating, by the test device, a sequence of the at least one movement profile for an ego vehicle and/or a fellow vehicle of a traffic scenario, wherein a sequence change is generated based on a segment being changed for a movement element; determining, by the test device, a measure of similarity based on movement profiles from at least two traffic scenarios by comparing respective sequences created from the movement profiles; and providing the measure of similarity.