Autonomous Vehicle Planning Stack Evaluation via Parameter Ablation
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Solution Overview
Problem
Existing shadow mode testing for autonomous vehicles requires manual analysis of test data to compare autonomous driving system performance with human driving, which is inefficient and lacks automated methods to evaluate performance improvements.
Innovation Solution
A computer-implemented method for evaluating autonomous vehicle planning stack components by generating evaluation data under controlled modifications of operating parameters, using continuous ablation to compare performance metrics between different stack configurations, and displaying results on performance cards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to compare autonomous driving system performance with human driving, then evaluation accuracy can be maintained, but evaluation efficiency deteriorates
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computer-implemented evaluation system. The system automatically generates evaluation data from shadow mode operations, modifies operating parameters through continuous ablation, and compares performance metrics without human intervention, thereby maintaining accuracy while dramatically improving efficiency
Solution Approach 2:
The evaluation system performs self-assessment by automatically comparing autonomous driving system performance against human driving benchmarks. The system generates its own evaluation data, executes parameter modifications, and conducts performance comparisons autonomously, eliminating the need for external manual analysis
2Quantity of substance
If shadow mode testing is conducted to accumulate shadow miles, then performance comparison data is obtained, but automated evaluation capability is lost
Solution Approach 1:
The patent introduces an automated evaluation system as an intermediary between shadow mode data collection and performance analysis. This intermediary automatically processes shadow miles data, executes continuous ablation studies, and generates performance comparisons, thereby enabling automated evaluation while preserving the value of accumulated shadow miles
Solution Approach 2:
The system performs preliminary automated processing of shadow mode data by pre-generating evaluation datasets and pre-executing parameter modifications before formal performance comparison. This preliminary automation prepares the data for efficient automated analysis while maintaining the integrity of the shadow miles accumulation process
Data Source
AI summary
A computer implemented method of evaluating the performance of at least one component of a planning stack for an autonomous robot, the method comprising: generating first evaluation data of a first run by operating the autonomous robot under the control of a planning stack under test in a scenario; modifying at least one operating parameter of at least one component of the planning stack by applying a variable modification to the operating parameter; generating second evaluation data of a second run by operating the autonomous robot under the control of the planning stack in which the at least one operating parameter has been modified, in the scenario; and comparing the first evaluation data with the second evaluation data using at least one performance metric for the comparison.