Autonomous Vehicle Run Segment Sampling for Targeted Simulation
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Solution Overview
Problem
Existing methods for validating decision logic updates in autonomous vehicles are computationally inefficient, particularly when many scenarios are simple and provide little insight into the impact of code updates on vehicle behavior.
Innovation Solution
A method that involves providing a set of run segments corresponding to scenarios for simulating autonomous vehicle operation, receiving a user-specified configuration with parameters to identify target run segments, applying the configuration to select relevant run segments, and performing simulations using these segments to generate a simulation output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If random sampling or identification of run segments based simply on roadway type is used, then the sampling process is simple and quick, but interesting scenarios for which important behavioral insights can be generated are overlooked
Solution Approach 1:
The patent transforms the sampling approach by changing from simple random sampling or roadway-type-based sampling to configuration-based sampling that uses multiple parameters including agent types, environmental conditions, and scenario characteristics. This parameter expansion enables identification of behaviorally significant scenarios while maintaining systematic sampling efficiency.
Solution Approach 2:
The patent introduces a configuration file as an intermediary that bridges the gap between simple sampling mechanisms and complex behavioral analysis requirements. The configuration file contains structured parameters that guide the selection of run segments, enabling sophisticated scenario identification without requiring complex real-time analysis during the sampling process.
2Reliability
If code updates are validated across a wide range of scenarios, then comprehensive validation is achieved, but computational efficiency decreases due to testing many simple scenarios that provide little insight
Solution Approach 1:
The patent extracts and isolates the critical parameters that determine behavioral significance from the complete set of run segment attributes. By identifying and extracting only the essential configuration parameters (agent types, environmental conditions, scenario features), the system filters out simple scenarios that provide minimal insight while retaining comprehensive validation of behaviorally significant cases.
Solution Approach 2:
The patent applies partial action by selectively testing only those run segments that match configured behavioral criteria rather than executing all available scenarios. The configuration file specifies particular parameter combinations that represent critical validation cases, enabling focused testing that achieves comprehensive behavioral validation with reduced computational expenditure.
3Loss of information
If configuration-based sampling with multiple parameters is used to identify target run segments, then behavioral insights are improved, but the complexity of the sampling process increases
Solution Approach 1:
The patent resolves the complexity issue by performing preliminary action - all the complex parameter configuration and scenario filtering is done in advance through configuration files before the actual simulation execution. The configuration files contain pre-defined parameter combinations and selection criteria that are processed beforehand, transforming a potentially complex runtime decision-making process into a simple file-based specification approach.
Data Source
AI summary
Embodiments of the present disclosure relate to configuration-based sampling of run segments for simulating autonomous vehicle behavior. In at least one implementation, a method comprises: providing a set of run segments corresponding to scenarios for simulating operation of an autonomous vehicle; receiving a user-specified configuration comprising parameters for identifying target run segments within the set of run segments; applying the user-specified configuration to the set of run segments to identify the target run segments; and causing one or more simulations to be performed using the target run segments to generate a simulation output.