Perception Error Model for Autonomous Vehicle Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Running multiple iterations of simulations for autonomous systems increases computational use, time, and energy significantly, making it inefficient for thorough testing of autonomous vehicles.
Innovation Solution
A perception error model is used to control and modify autonomous systems on-the-fly, reducing computational loads by estimating performance metrics through simulations, allowing for quicker determination of reliability and failure rates, and tuning vehicle components for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple iterations of simulations are run to ensure thorough testing of autonomous systems, then reliability and safety of the system are improved, but computational use, time, and energy increase by multiple orders of magnitude
Solution Approach 1:
The patent creates a simplified perception error model that copies the essential error characteristics of the full perception system but operates with significantly reduced computational resources. This model replicates perception failures without requiring the full computational overhead of the original system, enabling efficient simulation testing.
Solution Approach 2:
The perception error model is designed as a lightweight, computationally inexpensive approximation that can be rapidly instantiated and discarded during simulations. Instead of running expensive full perception systems multiple times, the patent uses this cheaper model to generate perception errors for testing, dramatically reducing energy consumption while maintaining testing effectiveness.
2Reliability
If multiple iterations of simulations are run to ensure thorough testing of autonomous systems, then reliability and safety of the system are improved, but time required for testing increases significantly
Solution Approach 1:
The perception error model serves as a computational copy that mimics perception system failures without requiring the full processing time of the original system. This allows numerous simulation iterations to be executed in the time it would take to run fewer full-system simulations.
Solution Approach 2:
The patent changes the computational parameters of the perception system by creating a simplified error model with reduced complexity. This parameter change enables faster execution while preserving the essential failure modes needed for safety testing, thus reducing overall testing time.
3Measurement precision
If the same scenario is run with slight variations to test system safety, then measurement precision of system reliability is improved, but device complexity and computational resources increase
Solution Approach 1:
Instead of creating multiple complex simulation variations, the patent uses a single perception error model that can generate diverse perception failures across different scenarios. This copying approach maintains measurement precision while avoiding the complexity of managing multiple simulation configurations.
Solution Approach 2:
The perception error model is designed as a universal component that can be applied across multiple scenarios and variations. Rather than creating specialized simulation setups for each test case, this single model serves multiple functions by generating appropriate perception errors for different testing situations, reducing overall system complexity.
Data Source
AI summary
Fast simulation of a scenario (e.g., simulating the scenario once as opposed to multiple times) to determine performance metric(s) of a configuration of one or more components of an autonomous vehicle may include training a perception error model based at least in part on a difference between a prediction output by a perception component associated with a future time and a perception output associated with that future time once that future time has arrived. A contour or heat map output by the perception error model may be used to determine one or more performance metric(s) associated with a component of the autonomous vehicle and identify which component may cause a degradation of a performance metric.