Hazard Probability Boundary Generation Using Probe Data
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Solution Overview
Problem
Traditional weather stations may not provide accurate and granular weather data for areas lacking nearby stations, leading to incomplete and unreliable weather information, especially for hazardous conditions.
Innovation Solution
A method and apparatus that utilize probe data points from a region to generate boundaries and confidence bands indicating hazardous conditions, using a Gaussian Mixture Model to calculate probability densities and provide indications to autonomous vehicles or driver assistance systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional weather stations are used to gather weather data, then the system structure is simple and easy to maintain, but the measurement precision and reliability of hazardous condition detection deteriorate in areas lacking nearby stations
Solution Approach 1:
The patent combines data from multiple sources including traditional weather stations, crowd-sourced sensors from mobile devices, and satellite data into a unified hazard detection system. This merging of diverse data sources improves measurement precision for hazardous conditions while distributing system complexity across multiple independent components rather than requiring a single complex station.
Solution Approach 2:
The system employs multi-functional probe vehicles that serve both as transportation carriers and as mobile weather monitoring stations. These vehicles incorporate various sensors (precipitation, temperature, humidity, barometric pressure) and can function as portable weather stations, eliminating the need for dedicated fixed infrastructure in remote areas while maintaining high measurement precision.
2Measurement precision
If crowd-sourced sensors are used to provide granular weather estimations, then the measurement precision and granularity of hazardous condition detection improve, but the quantity of data and system complexity increase
Solution Approach 1:
The system extracts only the essential hazardous condition parameters (precipitation, temperature, humidity, barometric pressure) from the vast amount of data collected by crowd-sourced sensors. By focusing on extracting specific relevant features rather than processing all raw sensor data, the system achieves high measurement precision while managing data volume efficiently through targeted data extraction.
Solution Approach 2:
The patent segments the large volume of crowd-sourced data into discrete probe data points associated with specific locations and time stamps. Each probe vehicle generates structured data records that are segmented by geographic region and temporal intervals, making the data manageable and processable while maintaining granular precision for hazard detection.
3Reliability
If probe data from multiple sources is integrated to generate hazard boundaries, then the reliability of hazardous condition identification improves, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary filtering and validation of probe data points before integration, checking for data quality, consistency, and relevance to hazardous conditions. By pre-processing and validating data from multiple sources before combining them, the system improves the reliability of hazard boundary generation while reducing the computational burden of processing raw unvalidated data.
Solution Approach 2:
The patent introduces an intermediary processing layer that standardizes and harmonizes data from different probe sources before integration. This intermediary layer translates diverse sensor formats and measurement protocols into a unified data structure, enabling reliable hazard boundary generation from heterogeneous sources while simplifying the overall processing architecture through standardized intermediate representations.
Data Source
AI summary
Embodiments described herein may provide a method for generating a local hazard warning boundary and at least one confidence band therein. Methods may include: receiving a plurality of probe data points from a plurality of probes within a region, where each probe data point includes location information and an indication of a hazardous condition; generating, based on the plurality of probe data points, a boundary within the region identifying an area within which the hazardous condition is determined to exist with at least a first degree of confidence; generating, based on the plurality of probe data points, a confidence band within the boundary within which the hazardous condition is determined to exist with a second degree of confidence; and providing for an indication of the boundary and the confidence band to at least one of an autonomous vehicle control or to a driver assistance system.