Vehicle Testing Knowledge Structure for Rare Scenario Simulation
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Solution Overview
Problem
Current autonomous vehicle testing methods are inefficient in evaluating and validating performance across a wide range of operating conditions, particularly rare or unevaluated scenarios, which can lead to safety concerns and increased testing costs.
Innovation Solution
A computer-implemented method and system for generating and utilizing a vehicle testing knowledge structure that appends evaluated and unevaluated operating conditions over time, allowing for real-time updates and simulations to validate autonomous vehicle performance across various scenarios, reducing the need for extensive real-world testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive real-world testing is conducted to evaluate all operating conditions, then validation reliability is improved, but testing time and cost increase significantly
Solution Approach 1:
The system performs preliminary evaluation of operating conditions by appending tested conditions to a knowledge structure before actual autonomous vehicle deployment. By pre-evaluating and storing operating conditions in the vehicle testing knowledge structure, the system avoids the need for extensive real-world testing of already-evaluated conditions, thereby reducing testing time while maintaining validation reliability.
Solution Approach 2:
The system creates a virtual copy of the real-world testing environment through simulation. By using the vehicle testing knowledge structure to store and retrieve operating conditions, the system can simulate and evaluate rare or unevaluated scenarios without physically deploying vehicles, thus reducing actual testing time and costs while maintaining validation effectiveness.
2Reliability
If comprehensive testing of rare operating conditions is performed, then safety is improved, but testing cost increases
Solution Approach 1:
The system uses simulation to create virtual copies of rare operating conditions instead of physically testing them with actual vehicles. By storing evaluated conditions in the knowledge structure and using simulation to evaluate unevaluated conditions, the system can comprehensively test rare scenarios without the high costs associated with real-world testing, thereby improving safety while controlling testing costs.
Solution Approach 2:
The system performs preliminary evaluation of rare operating conditions by appending them to the knowledge structure when they are first encountered or identified. This preliminary action allows the system to prepare evaluation data in advance, reducing the need for costly repeated real-world testing of the same rare conditions while maintaining comprehensive safety validation.
3Adaptability or versatility
If real-time updates of operating conditions are implemented, then adaptability is improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms by continuously updating the vehicle testing knowledge structure with newly evaluated operating conditions. When an unevaluated condition is encountered, the system evaluates it and appends the results to the knowledge structure, creating a feedback loop that improves adaptability over time. This structured feedback approach manages complexity by organizing updates systematically rather than requiring complex real-time processing.
Solution Approach 2:
The system performs preliminary organization of operating conditions by appending evaluated conditions to the knowledge structure in advance. This preliminary action creates a structured repository of testing data that can be efficiently queried and used for future evaluations, reducing the complexity of real-time updates while maintaining high adaptability to new operating conditions.
Data Source
AI summary
A first vehicle testing tuple comprising a plurality of first testing parameters and a second vehicle testing tuple comprising a plurality of second testing parameters can be obtained. The plurality of first testing parameters can be determined to be associated with an evaluated operating condition. The first tuple can be appended to a first portion of a plurality of portions of a vehicle testing knowledge structure. A second testing parameter can be determined to be associated with an unevaluated operating condition. The unevaluated operating condition can be evaluated. A second portion comprising the second vehicle testing tuple can be generated for the vehicle testing knowledge structure.