Speed Limit Data Matching for Multi-Mode Traffic Signs
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Solution Overview
Problem
Conventional methods for detecting traffic sign boards and generating speed limit data are prone to errors, resulting in inaccurate speed limit data that affects navigation, travel times, and autonomous vehicle control.
Innovation Solution
A system and method that receive sign data from traffic entities, match speed values with corresponding transportation mode values, and generate accurate speed limit data based on predefined conditions, using techniques such as clustering and machine-learning based computational networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used for detecting traffic sign boards and generating speed limit data, then the process is simple, but the accuracy of speed limit data becomes poor
Solution Approach 1:
The patent segments the speed limit data generation process into multiple independent modules: traffic sign board detection module, speed value extraction module, transportation mode identification module, and data matching module. Each module handles a specific aspect of data processing, improving overall accuracy while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
The patent introduces an intermediary matching process that connects speed values with transportation modes through predefined conditions. This intermediary layer validates and cross-references data from multiple sources before generating final speed limit data, significantly improving accuracy without requiring complete system redesign.
2Reliability
If multiple traffic sign boards are used to generate speed limit data, then more comprehensive coverage is achieved, but error propagation increases
Solution Approach 1:
The patent implements feedback mechanisms where detected traffic sign boards are validated against previously detected signs and predefined conditions. Speed values and transportation modes are cross-checked for consistency, and anomalies are flagged for re-evaluation. This feedback loop prevents error propagation while maintaining comprehensive coverage from multiple sign boards.
Solution Approach 2:
The patent applies beforehand cushioning by pre-defining valid speed value ranges, transportation mode categories, and consistency rules before data collection. These predefined conditions act as a buffer that prevents erroneous data from being processed further, cushioning the system against error propagation from multiple traffic sign boards.
3Measurement precision
If speed limit data is generated without matching transportation modes, then processing is faster, but data accuracy decreases
Solution Approach 1:
The patent performs preliminary action by pre-categorizing transportation modes and pre-defining matching conditions between speed values and modes. During actual data generation, the system only needs to check against these pre-established categories and conditions, significantly reducing processing time while maintaining high accuracy through proper mode-speed matching.
Data Source
AI summary
A system is disclosed for generating speed limit data. The system may include a memory configured to store computer executable instructions and one or more processor configured to execute the instructions to receive sign data relating to one or more traffic entities. The sign data comprises at least one of: a set of speed values, a set of transportation mode values, and one or more entity parameters associated with the corresponding one or more traffic entities. The processors are further configured to generate matching data based on a matching between the set of speed values and the set of transportation mode values, based on the one or more entity parameters. The processors are further configured to generate speed limit data based at least on: the matching data, the sign data and a predefined condition associated with transportation mode.