Vehicle Model Identification via Network Message Similarity
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Solution Overview
Problem
Existing vehicle identification systems face limitations in accuracy and accessibility due to reliance on limited databases and the VIN parameter, which may be incorrect or inaccessible for certain vehicle models, restricting their use across a wide range of vehicles.
Innovation Solution
A system comprising an electronic device connected to a vehicle network that acquires and processes data messages to calculate a similarity rate between identification parameters, using a Sorensen-Dice coefficient, to accurately identify vehicle models by matching them against a correspondence table, thereby improving reliability and compatibility with a large number of vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If license plate databases are used for vehicle identification, then identification can be performed, but the system is limited in the number of identifiable vehicles and requires user intervention to retrieve information
Solution Approach 1:
The electronic device automatically retrieves message identification parameters from the vehicle network without requiring user intervention. The system self-services by autonomously acquiring data from OBD connectors or wireless communication and performing identification operations independently.
Solution Approach 2:
The system uses multiple data sources including license plate databases, VIN parameters, and message identification parameters from vehicle networks. This multi-functional approach allows the system to identify a broader range of vehicles across different countries and models, overcoming the limitations of single-database systems.
2Extent of automation
If VIN parameter is used for vehicle identification, then automatic identification can be achieved, but the parameter may be incorrect for certain vehicle models and is only accessible via particular data acquisition protocols
Solution Approach 1:
The system calculates a similarity rate between the acquired message identification parameters and reference parameters stored in the database. This feedback mechanism allows the system to verify the correctness of identification results and detect potential errors, thereby improving reliability while maintaining automation.
Solution Approach 2:
The system transitions from relying solely on VIN parameters to using message identification parameters from vehicle networks. It implements a similarity calculation approach that compares multiple parameters (message identifiers, data lengths, signal types) rather than depending on a single parameter, thus improving identification accuracy across different vehicle models.
3Extent of automation
If VIN parameter is used for vehicle identification, then identification can be performed, but accessibility to this parameter is limited for certain vehicle models
Solution Approach 1:
The system implements multiple data acquisition methods including wired OBD connectors and wireless communication protocols. It can extract message identification parameters from various vehicle networks (CAN, LIN, FlexRay), making the system compatible with a wide range of vehicle models and manufacturers beyond what single-protocol systems can achieve.
Solution Approach 2:
The electronic device acts as an intermediary between the vehicle network and the identification database. It automatically acquires message identification parameters, processes them through similarity calculations, and retrieves corresponding vehicle information, thereby bridging the gap between different vehicle protocols and the identification system.
4Measurement precision
If similarity rate calculation is implemented, then identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The system calculates similarity rates based on key message identification parameters such as message identifiers and data lengths, rather than analyzing every possible parameter. This partial action approach achieves sufficient identification accuracy without the excessive computational burden of complete parameter analysis, balancing precision and complexity.
Data Source
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Figure 2
AI summary
The present invention relates to a system (1) for the automatic identification of a vehicle model (2), the vehicle (2) comprising at least one network (4), the system (1) comprising: • an electronic device (5) having means (16) for acquiring data (17) circulating on the network or each network (4) of the vehicle (2), said data (17) comprising messages and message identification parameters; and a data processing module (18) suitable for developing a request (28) for identification of the vehicle model (2), the request (28) comprising at least one identification parameter;• a data processing equipment (6) comprising storage means (38) storing a lookup table (52) between a list of identification parameters and a list of vehicle models, and an application comprising program instructions capable of calculating, for the network (4) of the vehicle (2), a similarity rate between a sample formed from the identification parameters contained in the query (28) and corresponding to that network (4), and samples each formed from a set of parameters from the lookup table (52), and of identifying the corresponding vehicle model according to the result of the similarity rate calculation.