Autonomous Vehicle Snow Friction Sensing for Real-Time Driving Control
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Solution Overview
Problem
Existing autonomous vehicle technologies fail to accurately assess the slipperiness of snow on roadways, which is crucial for adjusting driving behavior to ensure safety and efficiency.
Innovation Solution
An autonomous vehicle equipped with various sensors (such as cameras, temperature sensors, and acoustic sensors) that estimate the instantaneous coefficient of friction between tires and a snow-covered roadway, allowing for real-time adjustments in driving behavior and sharing of slipperiness data with other vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing autonomous vehicle technologies use basic sensors to detect snow presence, then the device complexity is reduced, but the measurement precision of snow slipperiness is insufficient
Solution Approach 1:
The patent combines multiple sensor types (cameras, temperature sensors, acoustic sensors) into an integrated sensing system that collectively assesses snow slipperiness. This merging approach enables comprehensive measurement of multiple snow properties simultaneously, resolving the contradiction by achieving high measurement precision through coordinated sensor operations rather than relying on a single complex sensor.
Solution Approach 2:
The sensor system is designed to perform multiple functions: detecting snow presence, measuring snow temperature, analyzing snow texture through acoustic signals, and estimating slipperiness coefficients. This multi-functionality allows the system to achieve comprehensive snow assessment without requiring separate dedicated devices for each measurement, thereby maintaining relative simplicity while improving measurement precision.
2Measurement precision
If the autonomous vehicle uses multiple sensors to accurately determine snow slipperiness, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The sensing system is segmented into specialized sensor modules, each responsible for detecting specific snow properties (visual characteristics, temperature, acoustic signatures). This segmentation allows the complex task of slipperiness assessment to be divided into manageable detection tasks, where each sensor contributes a specific measurement that is then integrated by the processor to generate the overall coefficient of friction estimate.
Solution Approach 2:
The processor acts as an intermediary that receives data from multiple sensors and synthesizes this information into a unified coefficient of friction estimate. Rather than requiring direct complex interactions between sensors, the processor mediates the data fusion process, converting multiple sensor inputs into a single comprehensive slipperiness assessment, thereby managing system complexity.
3Reliability
If the autonomous vehicle adjusts driving behavior based on real-time snow slipperiness data, then the safety and efficiency improve, but the control complexity increases
Solution Approach 1:
The system implements continuous feedback by repeatedly measuring snow slipperiness coefficients and using these measurements to dynamically adjust driving behavior. The processor monitors changes in snow conditions in real-time and modifies acceleration, braking, and steering commands accordingly. This feedback mechanism enables the vehicle to adapt to varying snow conditions, improving safety through continuous adjustment rather than static control parameters.
Solution Approach 2:
The control system transitions from static driving parameters to dynamic adjustment based on real-time slipperiness measurements. The vehicle's acceleration rates, braking forces, and steering responses are continuously modified according to the measured coefficient of friction, allowing the control characteristics to adapt dynamically to changing snow conditions, thereby improving reliability without requiring overly complex predetermined control strategies.
Data Source
AI summary
A autonomous vehicle comprising a sensor for sensing a physical property indicative of snow slipperiness; and a processor communicatively connected to the sensor to receive a signal from the sensor indicative of the snow slipperiness, to process the signal and to generate an estimated instantaneous coefficient of friction between a tire of the vehicle and a snow-covered roadway.