Perception Error Models for Scalable Autonomous Vehicle Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for validating autonomous vehicle controllers are inefficient, requiring extensive manual enumeration of scenarios and consuming excessive computational resources, limiting the ability to train and validate AI systems before deployment in real environments.
Innovation Solution
The use of parameterized scenarios and error models to simulate various environments, allowing for the efficient generation of simulated scenarios that cover a wide range of variations, reducing computational resources and time needed for validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual enumeration of scenarios is used for validation, then comprehensive scenario coverage is achieved, but computational resources and time are excessively consumed
Solution Approach 1:
The patent creates simplified copies of complex real-world scenarios through parameterized scenario templates. These templates capture essential scenario characteristics without requiring full manual enumeration of every possible scenario variation, thereby reducing validation time while maintaining comprehensive coverage through systematic parameter variation.
Solution Approach 2:
The patent employs parameterized scenarios where key scenario attributes are defined as adjustable parameters. By systematically varying these parameters across defined ranges and combinations, the system achieves comprehensive scenario coverage without manually creating each scenario instance, significantly reducing both time and computational resource requirements.
2Reliability
If manual enumeration of scenarios is used for validation, then thorough testing is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent segments the validation process into modular parameterized scenario templates. Each template represents a category of scenarios with defined parameters, allowing the system to systematically explore scenario space through parameter variation rather than exhaustive manual enumeration, thereby reducing computational resource consumption while maintaining testing thoroughness.
Solution Approach 2:
The parameterized scenario templates serve multiple functions: they define scenario structure, specify parameter ranges, generate test cases, and enable systematic exploration of scenario space. This multi-functionality eliminates the need for separate manual creation and management of individual scenarios, reducing computational overhead while maintaining comprehensive testing.
3Reliability
If extensive manual scenario enumeration is performed, then complete scenario coverage is achieved, but scalability is limited
Solution Approach 1:
The patent enables scalable scenario generation by defining scenarios through parameterized templates with adjustable parameters and ranges. This allows systematic generation of numerous scenario variations by simply varying parameter values, making the validation process highly scalable without requiring proportional increases in manual effort, thereby achieving complete scenario coverage with improved productivity.
Data Source
AI summary
Techniques for determining an error model based on vehicle data and ground truth data are discussed herein. To determine whether a complex system (which may be not capable of being inspected) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data. To provide safe operation of such a system, an error model can be determined that can provide a probability associated with perception data and a vehicle can determine a trajectory based on the probability of an error associated with the perception data.