Autonomous Vehicle Validation Using Simulated Exemplar Driving
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Solution Overview
Problem
Current methods for validating autonomous vehicle systems are inefficient, requiring extensive data collection and computational resources, and often rely on hand-tuned costs and thresholds, making it difficult to evaluate the performance of autonomous vehicles effectively without explicit modeling of decision-making heuristics.
Innovation Solution
The proposed solution involves injecting a simulated autonomous vehicle into driving scenarios to compare its behavior with human exemplars, using log data to validate the operational systems by simulating outcomes and determining test scores based on environmental states, allowing for holistic evaluation of the autonomous vehicle's performance without the need for extensive data accumulation or explicit modeling of decision-making processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive data collection and computational resources are used for validation, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent uses simulated autonomous vehicles and simulated driving scenarios as copies of real-world systems and environments. By validating against synthetic data and simulated exemplar behavior rather than requiring extensive real-world data collection, the system achieves validation accuracy while dramatically reducing the time and computational resources needed. The simulation environment serves as a efficient proxy for physical testing.
Solution Approach 2:
The patent performs preliminary validation by comparing autonomous vehicle behavior against pre-collected log data from human exemplars before actual deployment. This preliminary comparison establishes baseline expectations for safe and legal driving behavior, allowing the system to identify and correct issues early in development without requiring extensive field testing and data collection during validation.
2Device complexity
If hand-tuned costs and thresholds are used for evaluation, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent employs machine learning models that automatically learn evaluation criteria and decision-making heuristics from log data without requiring manual tuning of costs and thresholds. The system self-calibrates by training on exemplar human driving behavior, extracting patterns and rules that naturally encode safe and legal driving expectations. This eliminates the need for complex hand-tuned parameters while maintaining high evaluation accuracy.
Solution Approach 2:
The patent transforms the evaluation approach from using fixed, hand-tuned parameters to using dynamically learned parameters from data. By changing from static cost functions and thresholds to adaptive machine learning models trained on real driving logs, the system achieves both simplicity (no manual tuning) and precision (data-driven accuracy). The parameters are automatically adjusted based on the learned behavior patterns.
3Measurement precision
If extensive open-ended accumulation of driving miles and data samples is performed, then measurement precision is improved, but productivity and loss of time deteriorate
Solution Approach 1:
The patent uses simulated driving scenarios and synthetic data generation as efficient copies of real-world driving experiences. Instead of accumulating extensive real driving miles to encounter rare edge cases, the system uses simulation to efficiently generate and validate against diverse scenarios including rare events. This copying approach maintains measurement precision while dramatically improving productivity by reducing the time needed to accumulate validation data.
Solution Approach 2:
The patent creates a universal validation framework using log data from human exemplars that can evaluate multiple aspects of autonomous vehicle performance simultaneously. The same dataset serves multiple purposes: establishing baseline behavior, defining safety expectations, encoding legal compliance rules, and providing training data for machine learning models. This multi-functional use of data increases validation efficiency without sacrificing comprehensiveness.
Data Source
AI summary
An example method includes obtaining log data descriptive of an exemplar action of an exemplar vehicle in an environment, the exemplar action occurring in an initial state of the environment; determining, using the operational system, a planned action for a simulated vehicle in the initial state of the environment; simulating an SUT state of the environment resulting from the simulated vehicle executing the planned action in the initial state of the environment and an actor performing an actor action subsequent to the planned action and an exemplar state of the environment resulting from the simulated vehicle executing the exemplar action in the initial state of the environment and the actor performing the actor action subsequent to the exemplar action; determining a test score based on the SUT state and a reference score based on the exemplar state; evaluating the operational system based on the test score and the reference score.