Road Sign Detection Using Vehicle Probe Data
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Solution Overview
Problem
Current traffic reporting systems often suffer from infrequent updates, data entry errors, and delayed data input, leading to inaccurate or timely reporting of traffic incidents and congestion, which is critical for autonomous vehicle navigation and lane positioning.
Innovation Solution
A system that uses vehicle probe data from sensors, including GPS, LIDAR, and cameras, to identify and geographically locate road signs along a roadway segment through logistic regression for identification and linear regression for placement, enabling real-time updates and accurate lane positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If government agencies and online traffic reporting resources are used to provide traffic information, then traffic reporting coverage is achieved, but update frequency is low and data accuracy deteriorates due to infrequent updates, data entry errors, and delayed data input
Solution Approach 1:
The system enables vehicles to automatically collect and report their own probe data (location, speed, sensor information) without requiring manual data entry by traffic reporters. This self-service mechanism eliminates data entry errors and enables real-time reporting of traffic conditions, resolving the contradiction between accuracy and update timeliness
Solution Approach 2:
The system implements continuous feedback loops where probe data from multiple vehicles is constantly collected, processed, and used to update traffic information in real-time. This feedback mechanism ensures that traffic information remains accurate and current, preventing the time delays and accuracy losses associated with periodic manual updates
2Measurement precision
If traditional traffic reporting methods are used, then infrastructure complexity is reduced, but measurement precision of road conditions and sign locations deteriorates
Solution Approach 1:
The system uses multi-functional probe vehicles that simultaneously perform navigation, road condition monitoring, and road sign detection using integrated sensors (cameras, LIDAR, GPS). This multi-functionality achieves high measurement precision without requiring separate dedicated infrastructure for each function
Solution Approach 2:
The patent introduces machine learning algorithms as intermediaries that process raw sensor data from multiple sources and translate it into accurate road sign location information. These algorithms mediate between the complex sensor inputs and the required precise measurements, managing system complexity while maintaining high precision
3Measurement precision
If vehicle probe data from multiple sensors is collected and processed using machine learning algorithms, then detection precision of road signs and conditions is improved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the complex processing task into distinct stages: data collection from multiple sensors, preliminary filtering of probe data, machine learning-based road sign identification, and final location determination. This segmentation reduces computational complexity by handling different aspects separately rather than processing all data simultaneously
Data Source
AI summary
Systems, methods, and apparatuses are disclosed for identifying road signs along a roadway segment from vehicle probe data. Probe data is received from vehicle sensors at a road segment, wherein the probe data includes observed static objects along the road segment. Road signs are identified within the observed static objects of the probe data using a logistic regression algorithm. The geographic location of the identified road signs are determined using a linear regression algorithm.