Simulated Degraded Sensor Data for Autonomous Vehicle Perception
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Solution Overview
Problem
Autonomous vehicles face challenges in collecting and labeling degraded sensor data due to rare weather conditions and sensor degradations, which are essential for training and evaluating perception systems, but are difficult and costly to obtain, especially in dangerous or impractical scenarios.
Innovation Solution
Generating simulated degraded sensor data using physics-based forward models and machine learning approaches to approximate attenuation effects from conditions like fog, rain, and humidity, allowing for the creation of labeled data that can improve perception system performance without the need for actual degraded data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If actual degraded sensor data is collected from real-world weather conditions, then the perception system can be trained and evaluated with authentic degraded data, but the data collection becomes costly, time-consuming, and dangerous due to rare weather conditions and hazardous scenarios
Solution Approach 1:
The patent creates simulated degraded sensor data by applying physics-based attenuation models to clean sensor data, generating copies that mimic real degraded conditions without requiring actual collection from hazardous environments. This allows training and evaluation of perception systems using synthesized data that replicates fog, rain, and other degrading conditions.
Solution Approach 2:
The patent pre-computes attenuation effects and degradation parameters before actual data collection needs arise. By establishing physics-based models of how different weather conditions affect sensor data in advance, the system can generate degraded data on-demand without waiting for real weather events to occur during data collection.
2Measurement precision
If actual degraded sensor data is collected from real-world conditions, then the training data reflects authentic degradation patterns, but the cost and risk of data collection increase significantly
Solution Approach 1:
The patent introduces physics-based attenuation models as intermediaries between clean sensor data and degraded training data. These models mathematically simulate how atmospheric conditions like fog and rain attenuate sensor signals, providing a bridge that generates realistic degraded data without requiring direct collection from hazardous environments.
Solution Approach 2:
The patent modifies sensor data by applying parameter changes that simulate degradation effects. By adjusting attenuation parameters, backscatter coefficients, and other physical properties based on weather conditions, the system transforms clean sensor data into degraded versions that maintain physical accuracy while avoiding the need for dangerous data collection scenarios.
3Adaptability or versatility
If more degraded sensor data is collected to improve model performance, then the perception system becomes more robust, but the complexity and resources required for data collection and labeling increase
Solution Approach 1:
The patent creates a universal data generation system that can produce degraded sensor data for multiple weather conditions and sensor types using the same physics-based framework. This multi-functional approach allows the system to generate training data for fog, rain, humidity, and other conditions without requiring separate collection efforts for each scenario, reducing overall system complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides a cost-effective and efficient way to simulate degraded sensor data, improving the performance of autonomous vehicle perception systems in various conditions while reducing the risk and cost of data collection and human exposure to hazardous situations.
Implementation Method 1
converting, by the one or more processors, the first sensor data into simulated degraded sensor data for a particular degrading condition; converting the first sensor data includes using a physics-based forward model to approximate attenuation caused by the particular degrading condition
Implementation Method 2
the particular degrading condition is fog, and the simulated degraded sensor data simulates a backscatter effect of fog redirecting light back to the sensor
Data Source
AI summary
Simulated degraded sensor data may be generated for use in training a model. For instance, first sensor data collected by a sensor of a perception system of an autonomous vehicle may be received and converted into the simulated degraded sensor data for a particular degrading condition, such as a weather-related degrading condition. Then, the simulated degraded sensor data may be used to train a model for evaluating performance of the perception system to detect objects external to the autonomous vehicle under one or more conditions.