Autonomous Vehicle Scenario Selection Using Information Gain
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Solution Overview
Problem
Current methods for testing autonomous vehicles (AVs) are inefficient, as they often involve a large number of scenarios without considering the information gained from each, leading to wasted resources and diminishing returns, which slows down the development and validation of AV autonomous navigation systems.
Innovation Solution
A computer system is developed to select a limited set of scenarios that maximize information gain about AV performance, using a Bayesian Hierarchical Model to prioritize scenarios that reduce entropy, thereby accelerating the development and validation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of scenarios are tested to comprehensively evaluate AV performance, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent extracts and identifies the most informative test scenarios from a large scenario pool using information gain metrics. By selecting only the most valuable scenarios rather than testing all scenarios, the system achieves comprehensive evaluation accuracy with reduced testing time and resource consumption.
Solution Approach 2:
The patent changes the selection criterion from random or exhaustive scenario selection to information gain-based selection. This parameter change in the selection strategy enables the system to prioritize scenarios that maximize information about AV performance, thereby reducing the total number of scenarios needed while maintaining evaluation precision.
2Measurement precision
If a large number of scenarios are tested to comprehensively evaluate AV performance, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent extracts the most informative scenarios from the complete scenario space using information gain calculations. This extraction process identifies a minimal subset of scenarios that provide maximum evaluation value, thereby accelerating development productivity without sacrificing measurement precision.
Solution Approach 2:
The patent applies partial action by testing only a selected portion of all possible scenarios rather than the complete set. The selection is optimized to provide sufficient information for accurate performance evaluation, achieving the necessary precision with fewer tests and faster development cycles.
3Reliability
If many test scenarios are conducted to ensure safety targets are met, then reliability is improved, but loss of time and resource expenditure worsen
Solution Approach 1:
The patent extracts scenarios that provide the highest information gain for validating safety targets. By focusing computational and testing resources on these high-value scenarios rather than distributing resources uniformly across all scenarios, the system achieves reliable safety validation with reduced resource expenditure.
Solution Approach 2:
The patent uses information gain as a feedback metric to iteratively select scenarios that maximize validation efficiency. This feedback-driven selection process ensures that each test scenario contributes maximally to safety target validation, optimizing the balance between reliability and resource consumption.
Data Source
AI summary
In an example method, a computer system receives first data representing a plurality of scenarios for estimating a performance of a vehicle in conducting autonomous operations. Further, the computer system determines, for each of the scenarios: (i) a first metric indicating an observed performance of the vehicle in that scenario, where the first metric is determined based on at least one rule, and (ii) a second metric indicating a degree of information gain associated with that scenario. The computer system selects a subset of the scenarios based on the first metrics and the second metrics, and outputs second data indicative of the subset of the scenarios.