Speed Limit Validation Using Vehicle Dynamics Signals
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Solution Overview
Problem
Current speed limit information systems for road vehicles often provide incorrect information due to camera misinterpretation of road signs and outdated database data, especially in ambiguous or changing traffic conditions, leading to incongruent data sources that require heuristic conditional choices.
Innovation Solution
A method that receives signals from vehicle dynamics such as acceleration, pedal positions, and observations from surrounding vehicles to evaluate and validate candidate speed limits, outputting the highest confidence validated speed limit, incorporating image capture, database lookup, and centralized information processing for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If camera-based road sign recognition is used to determine speed limits, then real-time speed limit information can be obtained, but incorrect recognition occurs especially with supplementary signs, obscured signs, or ambiguous locations
Solution Approach 1:
The patent combines multiple independent speed limit determination methods (camera-based recognition, database lookup, vehicle dynamics analysis) into a unified system that cross-validates results. This merging allows the system to compensate for individual method weaknesses, particularly when camera recognition is ambiguous or database data is outdated.
Solution Approach 2:
The patent introduces vehicle dynamics data (acceleration, braking patterns, pedal positions) as an intermediary validation layer. This intermediary evidence indirectly confirms whether a detected speed limit change aligns with actual driver behavior, resolving ambiguities from direct camera recognition without requiring enhanced camera hardware.
2Loss of information
If on-board location referenced database is used for speed limit information, then speed limit data is available, but the information may be outdated after speed limit changes
Solution Approach 1:
The patent implements feedback loops where vehicle dynamics data continuously validates and updates the database information. When driver behavior (acceleration, braking) contradicts database speed limit data, the system triggers re-evaluation and updates, ensuring the database remains current without requiring constant manual updates.
Solution Approach 2:
The system performs preliminary validation of database speed limit data by comparing it with camera-based road sign recognition before fully trusting the database information. This preliminary cross-check prevents reliance on potentially outdated database entries while maintaining the benefit of having pre-stored speed limit data available.
3Quantity of substance
If multiple information sources (camera, database) are used to determine speed limits, then more data is available, but incongruent information requires heuristic conditional choices
Solution Approach 1:
The patent transforms the validation approach by changing the parameter being measured from direct speed limit value matching to indirect vehicle dynamics behavior analysis. Instead of comparing speed limit numbers from different sources (which may be incongruent), the system checks whether detected speed limit changes correlate with actual driver acceleration and braking patterns, simplifying the validation logic.
Solution Approach 2:
The system allows vehicle dynamics data to self-validate speed limit information without requiring complex external arbitration. The driver's own behavior serves as the validation mechanism, automatically confirming or rejecting speed limit changes detected by cameras or database without needing engineer-tuned heuristics to resolve conflicts.
4Measurement precision
If deep artificial neural networks are used for image classification to improve camera recognition, then automated image and video classification capabilities improve, but powerful or specialized computation hardware is required which increases cost
Solution Approach 1:
The patent uses vehicle dynamics data as an intermediary that validates camera recognition results without requiring enhanced camera or image processing capabilities. This intermediary layer allows standard cameras to achieve effective high accuracy by cross-validating their limited recognition with independent behavioral evidence from acceleration and braking sensors.
Solution Approach 2:
Instead of enhancing the camera system itself with expensive neural network hardware, the patent creates a parallel validation system using existing vehicle sensors (accelerometers, brake sensors) that copies the validation function from human driver judgment. This copying approach achieves improved accuracy using already-deployed hardware rather than requiring new specialized computation resources.
Data Source
AI summary
Described herein is a method of determining a current location speed limit in a road vehicle (1) speed limit information system (20). The method comprises receiving (6): one or more signals corresponding to respective candidate speed limits (7); and one or more signals related to observed vehicle dynamics affecting changes (8) in one or more vehicles (1, 5) pre and post a most recently passed anticipated speed limit change location (2). It further comprises evaluating (9) the confidence of the different candidate speed limits (7) based on the signals related to observed vehicle dynamics affecting changes (8) and validating (10a) or discarding (10b) candidate speed limits (7) based on the evaluated confidences. A signal (13) corresponding to a highest confidence validated speed limit is output.