Autonomous Vehicle Controller Validation Using Targeted Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicle systems face challenges in validating updates to controllers, as mere differences in performance metrics do not guarantee improvements over previous versions, and existing methods lack comprehensive evaluation of behavioral changes.
Innovation Solution
A simulation computing system generates simulations based on previous vehicle operations to validate updated controllers by comparing performance metrics and ensuring behavioral changes are within threshold limits, using user interfaces for input and data processing to determine scenario sets and evaluate component performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive simulations are conducted to evaluate all performance metrics of updated controllers, then validation accuracy and safety are improved, but computational resources and time requirements increase
Solution Approach 1:
The patent segments the controller into multiple components (perception component, prediction component, planning component, control component) and evaluates each component separately through targeted simulations. This allows comprehensive validation of controller updates while reducing overall computational burden by focusing resources on specific components that require validation rather than re-simulating entire system behavior.
Solution Approach 2:
The patent performs partial simulations that focus only on specific performance metrics or scenarios relevant to the controller update being validated, rather than conducting exhaustive simulations of all possible operating conditions. This selective approach maintains validation accuracy for critical functions while reducing unnecessary computational resource consumption.
2Measurement precision
If detailed performance metrics are collected and analyzed to detect unintended changes, then validation thoroughness is improved, but data processing complexity and time increase
Solution Approach 1:
The patent applies different evaluation criteria and measurement approaches to different controller components based on their specific functions and failure modes. For example, the perception component may be evaluated using metrics relevant to object detection accuracy, while the planning component uses metrics related to trajectory optimization. This localized quality approach improves detection precision for each component while avoiding the complexity of applying a single universal evaluation framework to all components.
3Productivity
If threshold-based validation is applied to determine acceptability of controller updates, then decision-making speed is improved, but risk of accepting suboptimal updates increases
Solution Approach 1:
The patent implements a feedback mechanism where simulation results are compared against threshold values, and the outcome feeds into iterative refinement of the validation process. If controller component performance falls below thresholds, the system provides feedback indicating which specific component or metric requires adjustment, enabling targeted improvements while maintaining efficient threshold-based decision-making for overall validation acceptance.
Data Source
AI summary
Techniques for generating simulations to evaluate an update to a controller. The controller may be configured to control one or more functionalities of an autonomous and/or a semi-autonomous vehicle. A simulation computing system may receive a request to evaluate a first controller. The simulation computing system may generate a simulation based on data associated with a previous operation of the vehicle in an environment, the previous operation being controlled by a second controller (e.g., standard for evaluation, control version, etc.). The simulation computing device may cause the first controller to control a simulated vehicle in the simulation and may determine whether to validate the update to the controller based on a difference between first metrics associated with a control of the simulated vehicle by the first controller and second metrics associated with a control of the vehicle by the second controller.