Road Sign Placement Prediction Using Multi-Vehicle Sensor Data
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Solution Overview
Problem
Existing systems face challenges in reliably detecting and determining the placement of road signs due to variable sign locations, inconsistent posting methods, and the difficulty in distinguishing between signs intended for different road links, especially when vehicles detect signs from adjacent or overlapping roadways.
Innovation Solution
A system that collects data from multiple vehicles equipped with sensors and cameras to develop a model associating sign placement characteristics with environmental and vehicle data, allowing for real-time prediction of sign placement using machine learning algorithms, thereby simplifying the detection and verification of sign locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If road signs are placed at variable locations according to different rules and entities, then the system can adapt to diverse road conditions and sign types, but the reliability of identifying speed limits from map placement deteriorates
Solution Approach 1:
The patent introduces an intermediary system comprising multiple vehicles equipped with sensors and cameras that act as mobile detection platforms. These vehicles traverse roadways and capture sign placement data, serving as intermediaries between the variable sign placement conditions and the centralized processing system. The collected data is then used to generate and update probabilistic models that predict sign locations, thereby resolving the contradiction between adaptability to variable placements and reliable identification.
Solution Approach 2:
The system implements feedback mechanisms where detected sign placements from multiple vehicles are analyzed and used to refine probabilistic models. The models continuously learn from actual sign locations and adjust their predictions accordingly. This feedback loop enables the system to adapt to variable sign placement rules while maintaining high reliability in speed limit identification by constantly improving its predictive accuracy based on real-world observations.
2Measurement precision
If multiple detection systems are used to improve sign detection accuracy, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent merges multiple detection capabilities into a unified system. Multiple vehicles equipped with sensors and cameras collectively form an integrated detection network. Rather than requiring each individual system to be complex, the strengths of multiple simpler systems are combined through centralized processing, achieving high measurement precision while keeping individual device complexity manageable.
Solution Approach 2:
The vehicles in the system serve multiple functions: they detect sign placements, collect environmental data, navigate roadways, and communicate with the centralized processing system. This multi-functionality reduces the need for specialized dedicated equipment for each function, thereby reducing overall device complexity while maintaining high detection accuracy through the universal platform's capability to perform multiple measurement tasks.
3Measurement precision
If data from multiple vehicles is collected to improve model accuracy, then the measurement precision improves, but the loss of time increases
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing sign placement data from multiple vehicles during their routine operations. Rather than waiting for a specific measurement campaign, the data collection occurs in advance during normal vehicle operation. This preliminary data accumulation enables the probabilistic models to be generated and updated more efficiently, reducing the time required for subsequent analysis while maintaining high precision.
Solution Approach 2:
The data collection process operates continuously as vehicles traverse the road network, constantly updating the probabilistic models with new sign placement observations. This continuous action eliminates gaps in data collection and allows the models to be maintained at high precision levels without requiring periodic interruptions or lengthy dedicated measurement phases, thereby reducing overall time loss.
Data Source
AI summary
Systems, methods, and apparatuses are described for predicting the placement of road signs. A device receives data depicting road signs from multiple vehicles. The device analyzes a detected placement of the road signs and at least one characteristic of a collection of the data. The characteristic describes the road upon which the data was collected, an operation of the vehicle from which the data was collected, or an environment in which the data was collected. The device generates a model that associates values for the detected placement of the road signs with values for the at least one characteristic. The model may be later accessed to interpret subsequent sets of data describing one or more road signs.