Autonomous Vehicle Validation Using Human Exemplar Simulation
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Solution Overview
Problem
Existing technologies face challenges in validating autonomous vehicle systems without requiring hand-tuned, hard-coded complex costs and arbitrary thresholds, and they often consume significant resources for data accumulation and simulation.
Innovation Solution
The proposed solution involves injecting a simulated autonomous vehicle into driving scenarios to compare planned behaviors with those of human exemplars, using log data to document human driving behaviors and simulate outcomes, thereby validating autonomous vehicle systems holistically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional validation methods using hand-tuned, hard-coded complex costs and arbitrary thresholds are used, then validation can be performed, but the system complexity and development time increase significantly
Solution Approach 1:
The patent uses simulation environments to create virtual copies of autonomous vehicles and their operational systems. Instead of directly testing and validating complex real-world systems with hand-tuned parameters, the invention validates by comparing simulated vehicle behavior against established benchmarks and safety standards in a controlled virtual environment, thereby reducing the need for complex manual validation procedures
Solution Approach 2:
The patent performs validation activities in advance through simulation before deploying autonomous vehicles in real-world conditions. By conducting comprehensive validation simulations that include edge cases and failure scenarios beforehand, the system identifies and resolves issues prior to actual deployment, reducing the need for complex post-deployment validation and manual parameter tuning
2Measurement precision
If extensive open-ended accumulation of actual or simulated driving miles and data samples is performed, then comprehensive validation data can be obtained, but resource consumption and time requirements increase considerably
Solution Approach 1:
The patent pre-generates comprehensive validation datasets through simulation environments that can efficiently create diverse driving scenarios, including rare edge cases and failure conditions, without requiring lengthy real-world data collection. This preliminary data generation through targeted simulation scenarios provides comprehensive validation coverage much faster than accumulating real-world driving miles
Solution Approach 2:
The invention uses simulation environments to create virtual replicas of driving scenarios that can be repeatedly instantiated and manipulated. Instead of accumulating unique real-world data over time, the system generates comprehensive validation datasets by copying and varying simulation scenarios, allowing rapid exploration of diverse conditions without additional real-world time investment
3Reliability
If comprehensive holistic evaluation of the end-to-end autonomy computing pipeline is performed, then system performance can be validated, but computational resources and processing time increase
Solution Approach 1:
The patent segments the holistic validation process into distinct simulation modules that can independently evaluate different components of the autonomy computing pipeline (perception, planning, control). This segmentation allows targeted validation of specific system aspects without requiring full-system simulation for every validation check, thereby reducing overall computational resource consumption while maintaining comprehensive coverage
Solution Approach 2:
The invention uses simplified virtual models and simulation environments that replicate essential system behaviors without requiring full-fidelity computational resources. By creating lightweight copies of the autonomy pipeline components in simulation, the system can perform repeated holistic evaluations at fraction of the computational cost of real-world testing
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.