Vehicle-to-X Message Classification via Spatial Region Overlap
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Solution Overview
Problem
Existing vehicle-to-X message classification methods are inflexible and require significant computing power, leading to inefficient and less reliable classification.
Innovation Solution
A method that classifies vehicle-to-X messages based on the overlap of originator and recipient regions determined by their spatial probability of location within a specified time interval, using sensors and communication systems to enhance precision and reduce computing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional classification methods are used to classify vehicle-to-X messages, then all messages can be analyzed, but the computing power required is excessive and the classification is less reliable
Solution Approach 1:
The patent applies preliminary action by determining originator and recipient regions before performing message classification. The system pre-calculates spatial probability regions based on position, orientation, and velocity information, then uses these pre-determined regions to quickly filter messages. This preliminary spatial analysis enables the system to discard obviously irrelevant messages without performing computationally intensive signature verification, thereby reducing computing power consumption while maintaining classification reliability.
Solution Approach 2:
The patent replaces the traditional mechanical approach of verifying every message signature with a spatial probability-based filtering mechanism. Instead of uniformly applying cryptographic verification to all messages, the system substitutes this with a geometric approach that uses originator and recipient regions to probabilistically determine message relevance. This substitution dramatically reduces the number of messages requiring full verification, lowering computing power requirements while maintaining acceptable reliability.
2Use of energy by moving object
If distance-based classification is used, then computing power is reduced, but classification precision deteriorates
Solution Approach 1:
The patent transitions from one-dimensional distance-based classification to multi-dimensional spatial probability region analysis. Instead of considering only the distance between originator and recipient, the system incorporates position, orientation, velocity, and time interval information to create two-dimensional spatial regions. This dimensional expansion enables more precise classification by evaluating whether regions overlap, providing better precision while maintaining reduced computing power requirements compared to full message verification.
3Measurement precision
If probabilistic techniques are used for classification, then classification precision improves, but computing time increases significantly
Solution Approach 1:
The patent applies partial action by performing probabilistic region overlap analysis only for messages that potentially require verification. The system calculates originator and recipient regions for all messages but performs detailed overlap analysis selectively, and performs full signature verification only for a small subset of highly relevant messages. This partial application of computationally intensive techniques maintains high classification precision while significantly reducing overall computing time compared to applying probabilistic techniques to all messages.
Data Source
AI summary
A method for classifying a received vehicle-to-X message, wherein the vehicle-to-X message is sent by a sender and received by a receiver that performs the classification, wherein the vehicle-to-X message contains first information regarding the sender, and wherein first information regarding the receiver is sensorially determined, wherein a sender region is determined from the first information regarding the sender, and a receiver region is determined from the first information regarding the receiver, and the classification is determined according to an overlapping of the sender region and the receiver region.