Vehicle-to-X Message Handling for Self-Learning Map Updates
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Solution Overview
Problem
Current vehicle-to-X communication systems face challenges in accurately interpreting received messages due to limited and outdated electronic road maps, which can lead to incorrect message interpretation and high computational costs, especially in vehicles without permanent maps or sufficient computing capacity.
Innovation Solution
A method for handling vehicle-to-X messages that classifies messages for simplified processing, forwarding only the header to a self-learning map when not relevant to applications, thereby saving computational resources and allowing real-time map updates using position data from vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the body of received vehicle-to-X messages is fully decoded and processed to create accurate self-learning maps, then the accuracy of road information is improved, but the computational cost and processing time increase significantly
Solution Approach 1:
The message processing is segmented into two paths: full decoding for messages requiring detailed information (body processed) and simplified processing for messages where header information suffices (body skipped). This segmentation allows the system to balance accuracy requirements against computational constraints by selectively applying processing depth based on message characteristics and current system state.
2Reliability
If electronic road maps are permanently stored in the vehicle for accurate message evaluation, then the reliability of message interpretation is improved, but the device complexity and cost increase
Solution Approach 1:
The system implements self-service by automatically creating and maintaining its own road map data structure (self-learning map) through processing position data from received messages. Instead of relying on pre-stored electronic road maps that require external updates and maintenance infrastructure, the system serves itself by learning road information directly from vehicle position data and message content, adapting to new roads and construction projects automatically.
3Loss of time
If all received vehicle-to-X messages are fully processed to ensure complete map updates, then the up-to-dateness of road information is improved, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by processing only the necessary portion of each message (header only or header plus body) based on what is needed to maintain an accurate self-learning map. This selective processing approach ensures that the map remains up-to-date with current road conditions and new constructions while avoiding the computational overhead of fully decoding every single message, thereby maintaining both timeliness and processing efficiency.
Data Source
AI summary
The invention relates to a method for handling a received vehicle-to-X message in a vehicle, said message having at least a header and a body, and only the header without the body being forwarded to a self-learning map, in particular if it is established that the message is suitable for simplified processing. The invention also relates to a vehicle-to-X communications module and a storage medium for carrying out the method.