Parameterizing Hot Starts for Autonomous Vehicle Test Repeatability
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Solution Overview
Problem
Autonomous vehicle software testing faces challenges due to low repeatability of simulation scenarios, making it difficult to reliably validate changes and identify issues, as numerous variables impact test results and it is hard to determine the cause of non-repeatability.
Innovation Solution
The system parameterizes hot starts by replaying real-world road data in simulations to identify an evaluation window that maximizes repeatability, ensuring that divergences in test results are attributed to specific code changes rather than other variables, by iteratively adjusting the simulation window to isolate the event being tested.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional simulation testing is used without parameterization, then testing flexibility is maintained, but test repeatability deteriorates due to numerous variables impacting results
Solution Approach 1:
The patent applies parameter changes by systematically modifying simulation parameters through an evaluation window that adjusts temporal boundaries. The system iteratively changes the start and end times of simulation segments to identify the specific window that maximizes repeatability metrics, transforming unstable simulation results into consistent, repeatable tests by optimizing parameter selection
Solution Approach 2:
The patent implements preliminary action by pre-processing road data to identify candidate evaluation windows before actual testing. The system performs preliminary analysis to determine temporal characteristics of real-world events, pre-configures simulation parameters based on this analysis, and prepares optimized simulation segments in advance, reducing variability during execution
2Reliability
If the simulation window is expanded to capture more context, then scenario completeness improves, but test repeatability deteriorates due to increased variable influence
Solution Approach 1:
The patent applies segmentation by dividing the continuous simulation timeline into discrete evaluation windows with specific start and end points. The system segments the road data into manageable temporal portions, each representing a specific event or scenario, allowing targeted analysis of individual events while maintaining necessary context within each segmented window
Solution Approach 2:
The patent implements local quality by applying different temporal boundaries to different evaluation windows based on the specific characteristics of each event. Rather than using a uniform window size for all scenarios, the system adjusts the start and end times locally for each event to capture exactly the necessary context while minimizing extraneous variables, optimizing repeatability for each specific test case
3Measurement precision
If multiple simulation runs are performed to account for variability, then result accuracy improves, but testing efficiency deteriorates due to increased time consumption
Solution Approach 1:
The patent applies self-service by implementing an automated evaluation process that automatically iterates through candidate evaluation windows, performs simulation runs, evaluates repeatability metrics, and selects the optimal window without manual intervention. The system self-optimizes the simulation parameters and automatically determines the best evaluation window, eliminating the need for manual trial-and-error testing while ensuring accurate results
Data Source
AI summary
Systems and techniques are provided for testing software. A method can generate, based on sensor data reflecting a pose/behavior of an autonomous vehicle (AV) during an event, a simulation of a trip by the AV comprising an evaluation window for testing a simulated pose/behavior of the AV after a software modification; iteratively adjust the time interval to yield an evaluation window for each iteration of adjustments to the time interval, each evaluation window including a time interval including the event and an evaluation start and end; select one of the evaluation windows based on a comparison of AV metrics from each respective simulation of the AV during the evaluation windows and divergences in an AV pose/behavior during each evaluation window and a pose/behavior of the AV during the trip; simulate a pose/behavior of the AV during the evaluation window; and simulate a performance of the AV during the evaluation window.