Autonomous Vehicle Decision Validation Using Human Reference Scenarios
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Solution Overview
Problem
Existing methods for evaluating autonomous vehicle control systems are inefficient and require extensive resource consumption, including computational and energy resources, to validate performance against human-like behavior in various driving scenarios.
Innovation Solution
The proposed system uses a simulation framework to compare decisions made by an autonomous vehicle control system with those made by human users, allowing for holistic evaluation of model updates and performance against high-level decision ground truth, without the need for extensive real-world data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive real-world data collection and validation are performed to ensure accurate evaluation of autonomous vehicle control systems, then measurement precision and reliability are improved, but loss of time and use of energy worsen
Solution Approach 1:
The patent creates synthetic copies of real-world driving scenarios through simulation environments. Instead of collecting and validating against extensive real-world data, the system generates artificial driving scenarios that replicate real-world conditions, allowing rapid validation of autonomous vehicle control systems while maintaining evaluation accuracy without the time cost of real-world data collection
Solution Approach 2:
The patent performs preliminary validation by comparing system decisions against human-like behavior patterns before actual deployment. By pre-establishing human behavior models and conducting simulated validations in advance, the system reduces the need for extensive post-deployment real-world testing, thereby decreasing validation time while maintaining measurement precision
2Measurement precision
If extensive real-world data collection and validation are performed to ensure accurate evaluation of autonomous vehicle control systems, then measurement precision and reliability are improved, but use of energy worsens
Solution Approach 1:
The patent replaces energy-intensive real-world data collection and processing with computationally efficient synthetic scenario generation. By copying real-world driving conditions into simulated environments, the system maintains evaluation accuracy while dramatically reducing the computational energy required for data collection, storage, and processing
Solution Approach 2:
The patent uses disposable synthetic scenarios that can be rapidly generated and discarded for validation purposes. Instead of investing significant energy in collecting and maintaining extensive real-world datasets, the system creates lightweight artificial scenarios that serve their validation purpose efficiently and can be regenerated as needed, reducing overall energy consumption
3Measurement precision
If detailed granular prediction or classification tasks are evaluated to assess model performance, then measurement precision is improved, but device complexity worsens
Solution Approach 1:
The patent extracts only the essential high-level decision-making aspects of autonomous vehicle control for validation purposes. Instead of evaluating every granular prediction and classification task, the system focuses on extracting and validating the critical decision points that most significantly impact safety and performance, thereby reducing validation system complexity while maintaining sufficient measurement precision
Solution Approach 2:
The patent introduces human behavior models as intermediaries between the autonomous vehicle system and the validation process. These intermediary models translate complex system outputs into comparable human-like decision patterns, simplifying the validation architecture by providing a straightforward reference framework without requiring analysis of every granular system component
4Device complexity
If hand-tuned hard-coded costs and arbitrary thresholds are used for evaluation, then device complexity is reduced, but measurement precision worsens
Solution Approach 1:
The patent implements feedback mechanisms where the validation system continuously learns from comparisons between autonomous vehicle decisions and human behavior patterns. Instead of relying on static hand-tuned thresholds, the system uses feedback from simulated validations to dynamically adjust evaluation criteria, improving measurement precision while maintaining manageable system complexity through automated learning rather than manual tuning
Data Source
AI summary
An example method includes (a) obtaining reference decision data describing a reference decision associated with navigating a driving scenario, wherein the reference decision data comprises a target action and a corresponding object identifier associated with the target action; and label data that comprises a validity interval associated with the reference decision, the validity interval indicating a time period of the driving scenario during which the reference decision is valid; (b) simulating a performance of a system under test (SUT) in the driving scenario to generate SUT decision data describing one or more SUT decisions associated with controlling an autonomous vehicle to navigate the driving scenario; and (c) determining, based on a comparison of the reference decision data and the SUT decision data, a validation state for the SUT.