Autonomous Vehicle Control Using Predicted Road Surface Conditions
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Solution Overview
Problem
Existing autonomous vehicles face challenges in accurately determining road surface conditions due to limitations in real-time weather data updates and the inability of sensors to differentiate between ongoing rainfall and wet environmental conditions, leading to sub-optimal operation.
Innovation Solution
The solution involves using weather data from radar and satellite systems to estimate road surface conditions through interpolation and extrapolation processes, combining optical flow fields with weather data to predict future conditions, and controlling vehicle operations based on these estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used to detect rainwater on the vehicle, then real-time weather information is obtained, but the sensors cannot differentiate between ongoing rainfall and wet environmental conditions
Solution Approach 1:
The patent introduces an intermediary processing system that combines sensor data with weather data from external sources (radar, satellite) to differentiate between ongoing rainfall and wet environmental conditions. This intermediary layer processes and correlates multiple data sources to resolve the ambiguity that single sensors cannot distinguish
Solution Approach 2:
The system integrates multiple data sources (vehicle sensors, radar, satellite weather data) into a unified processing framework that performs multiple functions: detecting rainwater, differentiating weather conditions, and predicting future conditions. This multi-functional approach resolves the limitation of single-sensor detection
2Reliability
If weather data updates are used to determine road surface conditions, then environmental information is obtained, but real-time updates are insufficient for accurate current condition assessment
Solution Approach 1:
The system performs preliminary actions by obtaining weather data at multiple future time points and using interpolation to estimate current conditions. Instead of waiting for real-time updates, it proactively calculates current road surface conditions by interpolating between known weather data points, effectively looking backward in time to determine present state
Solution Approach 2:
The system uses feedback loops where detected road surface conditions and vehicle sensor data are continuously fed back into the processing system to refine and update weather condition assessments. This continuous feedback mechanism improves accuracy over time and allows for dynamic adjustment of vehicle operation
3Reliability
If vehicle operation is adjusted based on weather data, then safety is improved, but the data may not reflect actual current road surface conditions
Solution Approach 1:
The patent merges multiple data sources including vehicle sensor data, radar weather data, and satellite weather data into a comprehensive assessment of road surface conditions. By combining these diverse sources and interpolating between them, the system creates a more accurate and reliable picture of actual current conditions than any single source could provide
Data Source
AI summary
A method comprising obtaining weather data relating to an area of an environment, wherein the weather data is associated with a first precipitation rate in the area and is associated with a first time; determining, based at least in part on the weather data, a second precipitation rate at a second time different from the first time and wherein the weather data does not contain the second precipitation rate; determining, based at least in part on the second precipitation rate, a road surface value associated with an amount of precipitation on a surface of a road in the area; and controlling an autonomous vehicle based at least in part on the road surface value.