Distance-Biased Confidence Weighting for Autonomous Vehicle Simulation
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Solution Overview
Problem
Simulations used to test and validate autonomous vehicle systems often face challenges due to noisy, inconsistent, and incomplete data, making it difficult to create realistic scenarios and test updated algorithms effectively.
Innovation Solution
The system generates synthetic data for simulation scenarios by capturing sensor data from a physical environment, using distance measurements and confidence values to weight attribute data and resolve inconsistencies, thereby improving the accuracy and realism of simulated environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is used to create simulations, then realistic scenarios can be generated, but the data is noisy, inconsistent, and incomplete
Solution Approach 1:
The patent introduces an intermediary processing system that acts as a mediator between raw sensor data and simulation generation. This system applies distance-biased confidence weighting to filter and refine sensor data, using confidence scores adjusted by distance metrics to select the most reliable attribute values, thereby resolving inconsistencies before data enters the simulation pipeline
Solution Approach 2:
The patent transforms the raw sensor data by changing its parameter representation through confidence weighting. Each attribute value is associated with a confidence score that is modified based on distance from the sensor, converting noisy raw data into weighted, reliability-adjusted parameters that can be consistently used for simulation generation
2Adaptability or versatility
If static data is used for testing, then simulations can be created, but testing updated system algorithms becomes impossible
Solution Approach 1:
The patent transforms static simulation data into a dynamic system by implementing confidence-based attribute selection that can adapt to different sensor readings and conditions. The system dynamically adjusts which attribute values to use based on real-time confidence scores and distance measurements, enabling the simulation to reflect updated system algorithms while maintaining consistency
3Manufacturing precision
If distance measurements and confidence values are used to weight attribute data, then inconsistencies are resolved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by treating different attribute values with different levels of processing based on their local characteristics. Each attribute is evaluated individually with its own confidence score and distance weighting, applying the appropriate level of refinement locally rather than uniformly processing all data, which reduces overall computational complexity
Data Source
AI summary
A vehicle can capture data that can be converted into a synthetic scenario for use in a simulator. Objects can be identified in the data and attribute data associated with the objects can be determined. Updated attribute data may be determined based on confidence values and/or distance measurements associated with the attribute data. The object and attribute data may be used to generate synthetic scenarios of a simulated environment, including simulated objects that traverse the environment and perform actions based on the attribute data associated with the simulated objects, the captured data, and/or interactions within the simulated environment. The scenarios can be used for testing and validating interactions and responses of a vehicle controller within the simulated environment.