Autonomous Vehicle Snow Level Detection via Image Analysis
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Solution Overview
Problem
Autonomous vehicles face challenges in safely operating in adverse weather conditions, particularly snow-covered roadways, due to the need for accurate determination of snow levels and traction coefficients to predict vehicle movement and prevent skidding.
Innovation Solution
A method involving determining a best-fit snow boundary in images using reference objects like vehicle tires or vehicle parts, calculating snow levels, and adjusting vehicle operations based on traction coefficients derived from temperature, tire type, and condition to set speed and acceleration limits, and deciding whether to park or continue operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle sensors are used to detect snow conditions, then accurate snow level information can be obtained, but sensor accuracy deteriorates in adverse weather conditions
Solution Approach 1:
The patent introduces an intermediary processing system that captures images of vehicle components (tires, bumpers, lights) and uses image analysis to determine snow levels. This intermediary approach between the sensor and the control system allows for more reliable snow detection by processing visual data through multiple steps: capturing images, identifying vehicle components, detecting snow boundaries, and calculating snow levels based on component geometry.
2Reliability
If vehicle operations are adjusted based on snow level, then vehicle safety is improved, but operational flexibility is reduced
Solution Approach 1:
The patent implements dynamic adjustment of vehicle operations based on real-time snow level detection. The system continuously monitors snow conditions and dynamically modifies vehicle parameters such as maximum speed, acceleration rates, and steering angles. This dynamic approach allows the vehicle to adapt its operational characteristics to current snow conditions while maintaining safety, rather than imposing fixed operational constraints.
3Measurement precision
If image processing is used to determine snow boundary, then snow level measurement accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the image processing task into distinct stages: capturing images of specific vehicle components, identifying those components through pattern recognition, detecting snow boundaries along the components, and calculating snow levels based on component geometry. This segmentation of the processing pipeline reduces overall complexity by breaking down the complex task into manageable, specialized sub-tasks that can be executed more efficiently.
Data Source
AI summary
A system, including a processor and a memory, the memory including instructions to be executed by the processor to determine a best-fit snow boundary in an image including a reference object, determine a snow level based on comparing the best-fit snow boundary to the image of the reference object, and operate a vehicle by actuating vehicle components based on the determined snow level.