Event Sequence Tunnels for Consistent Autonomous Agent Testing
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Solution Overview
Problem
Current methods for testing autonomous vehicle systems are inefficient, requiring manual frame-by-frame review and being unable to consistently identify simulated agents across multiple frames, leading to time-consuming and costly evaluations.
Innovation Solution
The implementation of techniques that define and test evolving event sequences by specifying consistent characteristics for simulated agents within an event sequence tunnel, allowing for automated validation of autonomous vehicle behavior across larger data slices without human observation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual frame-by-frame review is used to test autonomous vehicle systems, then measurement precision of agent identification is improved, but productivity is worsened due to time-consuming evaluations
Solution Approach 1:
The patent creates simulated agents that replicate real-world agent characteristics and behaviors in a virtual environment. These simulated agents serve as copies that can be consistently identified and tracked across multiple frames without requiring manual review, thereby maintaining measurement precision while dramatically improving productivity through automated testing
Solution Approach 2:
The patent replaces the manual mechanical process of frame-by-frame review with an automated computational system. The simulation engine automatically generates, tracks, and evaluates simulated agents across frames, substituting human observation with algorithmic processing that maintains identification consistency while enabling high-speed automated testing
2Productivity
If automated testing without human observation is implemented, then productivity is improved through faster evaluation, but measurement precision is worsened due to inability to consistently identify agents across frames
Solution Approach 1:
By creating simulated agents that are exact replicas of real-world agents with consistent characteristics, the system enables automated tracking without human intervention. The simulated agents maintain stable identifiers and predictable behaviors that allow automated systems to consistently identify and track them across frames, achieving both high productivity and measurement precision
Solution Approach 2:
The patent changes the parameters of the testing system by introducing simulated agents with controlled, consistent characteristics rather than relying on variable real-world agents. This parameter change enables automated systems to reliably identify and track agents across frames, resolving the measurement precision issue while maintaining automated high-speed testing
3Measurement precision
If manual review processes are used to ensure accurate agent identification, then measurement precision is improved, but loss of time increases due to extensive manual evaluation requirements
Solution Approach 1:
The patent performs preliminary actions by pre-defining simulated agent characteristics, behaviors, and trajectories before the actual testing begins. This preliminary setup ensures that agents have consistent, predictable properties that can be automatically identified and tracked, eliminating the need for time-consuming manual review while maintaining identification accuracy
4Productivity
If automated evaluation over larger data slices is implemented, then productivity is improved through reduced manual review, but reliability is worsened due to inconsistent agent identification across frames
Solution Approach 1:
The simulated agents serve as reliable copies with consistent identifiers and characteristics that can be automatically tracked across large data slices. This copying approach enables automated evaluation of extensive datasets while maintaining reliable, consistent agent identification throughout, resolving the contradiction between productivity and reliability
Data Source
AI summary
Provided are methods, systems, and computer program products for defining and testing evolving event sequences. Some methods include specifying an event sequence tunnel in a scenario, wherein an entry space and an exit space of the event sequence tunnel are identified for a simulated agent. Dimensions of the event sequence tunnel are determined, and at least one factor is applied to dimensions of the event sequence tunnel at the entry space and propagated through the event sequence tunnel. The simulated agent is evaluated at the entry space until the exit space of the event sequence tunnel in a simulation. At least one consistent characteristic associated with the simulated agent is determined at the entry space, evolved, and replicated throughout respective event sequence tunnels. A response of an autonomous system to simulations of the scenario is evaluated in view of the at least one consistent characteristic of the simulated agent.