Vehicle Fog-Range Detection Using Synthetic Image Training

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Solution Overview

Problem

Fog obscures images collected by vehicle sensors, making it difficult to accurately identify distances to objects, and existing methods like three-dimensional depth detection algorithms are slow and inefficient in updating the vehicle's data collection range.

Innovation Solution

A system that generates synthetic images by adjusting pixel color values based on a specified meteorological optical range to simulate fog, trains a machine learning program to identify this range in actual fog conditions, and actuates vehicle components accordingly, transitioning from autonomous to manual operation when the range falls below certain thresholds.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If three-dimensional depth detection algorithms are used to identify distances to objects, then measurement precision is improved, but processing speed deteriorates

Engineering Contradiction:

Improvedistance identification accuracyVSAvoiddata collection range update speed

Core Design Contradiction:

Measurement precisionVSSpeed

Solution Approach 1:

The system pre-calculates and stores depth information for multiple potential object positions in advance. When an object is detected, the system retrieves pre-computed depth data from memory rather than performing real-time three-dimensional depth detection, significantly reducing processing time while maintaining measurement precision

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces complex mechanical three-dimensional depth detection algorithms with a lookup table approach using stored depth information. This substitution transforms a computationally intensive process into a simple data retrieval operation, improving speed while preserving measurement accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Quantity of substance

If vehicle sensors collect data beyond the meteorological optical range, then quantity of data is increased, but reliability of data deteriorates

Engineering Contradiction:

Improvedata quantityVSAvoiddata reliability

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The system continuously monitors the meteorological optical range and uses this information as feedback to dynamically adjust the data collection range. When the optical range is limited (e.g., foggy conditions), the system automatically reduces the collection distance to ensure data reliability, while maintaining maximum data quantity when conditions are clear

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent implements dynamic adjustment of the data collection range based on real-time meteorological optical range measurements. The system adapts its operational parameters continuously, changing the effective collection distance according to environmental conditions to balance data quantity and reliability

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11772656B2Enhanced vehicle operation

Publication Date: 2023.10.03 FORD GLOBAL TECH LLC

AI summary

A system includes a computer including a processor and a memory, the memory storing instructions executable by the processor to generate a synthetic image by adjusting respective color values of one or more pixels of a reference image based on a specified meteorological optical range from a vehicle sensor to simulated fog, and input the synthetic image to a machine learning program to train the machine learning program to identify a meteorological optical range from the vehicle sensor to actual fog.