Vehicle Controller Safety Validation Using Parameterized Scenarios
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Solution Overview
Problem
Current methods for validating autonomous vehicle controllers are inefficient, requiring extensive computational resources and time to simulate various scenarios, limiting the ability to train and validate AI systems before deployment in real environments, and lacking scalability.
Innovation Solution
The use of parameterized scenarios and error models to generate simulated environments efficiently, leveraging sensor data and ground truth data to create a wide range of scenarios quickly, reducing computational resources needed and improving safety metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional simulation methods are used to validate autonomous vehicle controllers, then safety validation can be performed, but computational resources and time required are excessive
Solution Approach 1:
The patent applies preliminary action by pre-generating diverse scenario templates and error models before actual validation. Scenario templates encompass various driving situations (intersections, pedestrians, weather conditions) and are prepared in advance, allowing rapid instantiation during validation without requiring full simulation setup each time.
Solution Approach 2:
The patent uses copying by creating simplified simulation environments that replicate real-world driving scenarios. Instead of simulating every possible real-world condition, the system creates representative copies of critical scenarios using error models that mimic sensor uncertainties and environmental variations, enabling efficient validation while maintaining safety relevance.
2Reliability
If comprehensive scenario coverage is achieved through traditional simulation, then safety metrics can be validated, but computational resources required are excessive
Solution Approach 1:
The patent applies partial action by focusing simulation efforts on the most critical safety scenarios rather than attempting to simulate all possible driving conditions. Error models prioritize uncertainties in sensor data and edge cases that most impact safety, allowing validation with reduced computational resources while maintaining safety assurance.
Solution Approach 2:
The patent uses parameter changes by systematically varying scenario parameters (weather conditions, object positions, sensor uncertainties) within predefined ranges. This allows comprehensive coverage of safety-critical variations without requiring full enumeration of all possible conditions, optimizing the balance between validation thoroughness and computational efficiency.
3Reliability
If traditional validation methods are used, then controller safety can be assessed, but scalability to multiple scenarios is limited
Solution Approach 1:
The patent applies universality by creating a multi-functional validation framework where scenario templates and error models can be reused across multiple validation campaigns. The same infrastructure supports different vehicle types, sensor configurations, and regulatory requirements, enabling scalable validation without requiring separate systems for each scenario set.
Solution Approach 2:
The patent uses segmentation by dividing the validation process into independent, modular components: scenario templates, error models, validation metrics, and result aggregation. This segmentation allows parallel processing of multiple scenarios and enables the system to scale by adding or removing specific scenario modules without affecting the entire validation pipeline.
Data Source
AI summary
Techniques for determining a safety metric associated with a vehicle controller are discussed herein. To determine whether a complex system (which may be uninspectable) is able to operate safely, various operating regimes (scenarios) can be identified based on operating data and associated with a scenario parameter to be adjusted. To validate safe operation of such a system, a scenario may be identified for inspection. Error metrics of a subsystem of the system can be quantified. The error metrics, in addition to stochastic errors of other systems/subsystems can be introduced to the scenario. The scenario parameter may also be perturbed. Any multitude of such perturbations can be instantiated in a simulation to test, for example, a vehicle controller. A safety metric associated with the vehicle controller can be determined based on the simulation, as well as causes for any failures.