Surrounding Vehicle Identification Using Trajectory Pattern Matching
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Solution Overview
Problem
Existing vehicle-to-vehicle (V2V) communication systems face challenges in accurately identifying surrounding vehicles due to GPS errors, which affect the precision of position detection.
Innovation Solution
An apparatus and method that utilize sensors to measure position coordinates and yaw rates of a subject vehicle, combine this data with coordinate history and speed received from surrounding vehicles through V2V communication, and generate traveling trajectories and speed patterns to compare and accurately identify surrounding vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS data is used to detect position of surrounding vehicles, then the system can obtain position information, but the detection accuracy is reduced due to GPS errors
Solution Approach 1:
The patent combines multiple data sources including GPS coordinates, V2V communication data (coordinate history and speed), and sensor measurements (yaw rate, speed) to create a comprehensive identification system. By merging these different data streams, the system compensates for GPS errors and achieves more accurate vehicle identification than any single source could provide alone.
Solution Approach 2:
The patent introduces an intermediary verification mechanism using trajectory prediction and pattern matching. Instead of directly trusting GPS coordinates, the system uses predicted trajectories based on accumulated vehicle behaviors as an intermediary to verify and correct position data, thereby improving identification accuracy.
2Measurement precision
If only GPS data from surrounding vehicles is used for identification, then the system is simple to operate, but identification accuracy is insufficient
Solution Approach 1:
The patent segments the identification process into distinct functional modules: a sensor unit for measuring current position and behavior, a V2V communication unit for receiving coordinate history and speed data, and a controller for generating trajectories and performing pattern matching. This segmentation allows each module to specialize in specific tasks, improving overall accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system performs preliminary actions by pre-calculating and accumulating vehicle behavior patterns (trajectory predictions based on historical data) before actual identification is needed. This preliminary preparation of reference data enables faster and more accurate real-time identification without adding excessive complexity during critical detection moments.
Data Source
AI summary
An apparatus and method for identifying surrounding vehicles is provided. The apparatus includes a sensor that measures position coordinates of a first surrounding vehicle and a yaw rate and a speed of a subject vehicle and a V2V communication unit that receives a coordinate history and a speed from the plurality of surrounding vehicles. Additionally, a controller generates a traveling trajectory and a speed pattern as first identification information by applying accumulated behaviors of the subject vehicle to the position coordinates of the first surrounding vehicle and calculate each traveling trajectory and speed pattern as n-th identification information based on the coordinate history and the speed received via the V2V communication unit. The controller then compares the first identification information with the n-th identification information to recognize the surrounding vehicle corresponding to identification information most similar to the first identification information as the first surrounding vehicle.