Vehicle Trajectory Classification Using Relative And Absolute References
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Solution Overview
Problem
Current vehicle trajectory systems face challenges such as using trajectories with no fixed location reference, imprecise or temporary location references, inability to compare and synchronize trajectories from different sources, and insufficient map data in closed environments, leading to incomplete or unusable trajectory data.
Innovation Solution
A method that enhances trajectory clusters by detecting location-specific and vehicle-specific characteristics during trajectory negotiation, assigning these characteristics to trajectories, and comparing them to improve their informational content, allowing for classification and merging to create more comprehensive and usable trajectories across a vehicle fleet.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If trajectories are stored without fixed location reference to enable flexibility in different environments, then adaptability is improved, but measurement precision of location deteriorates
Solution Approach 1:
The patent segments location reference into multiple types: absolute location references (for precise positioning) and relative location references (for flexibility). Trajectories are divided into segments with different reference types, allowing the system to use absolute references when available and relative references when needed, thus resolving the contradiction between precision and adaptability
Solution Approach 2:
The system dynamically selects and switches between absolute and relative location references based on environmental conditions and trajectory requirements. The location reference system is made adaptive rather than static, allowing the same trajectory data to function in both precise and flexible scenarios depending on what is needed at the moment
2Quantity of substance
If trajectory data is collected from multiple sources without standardized characteristics, then quantity of trajectory data increases, but manufacturing precision of trajectory quality deteriorates
Solution Approach 1:
The patent introduces standardized parameters and characteristics that are assigned to trajectories regardless of their source. By normalizing trajectory data through consistent parameter definitions (such as location reference types, characteristic markers, and data structures), the system can aggregate large volumes of trajectory data from multiple sources while maintaining synchronization precision through these unified parameters
3Device complexity
If conventional localization methods are used in environments with GPS shadowing or tunnels, then device complexity is reduced, but reliability of trajectory usage deteriorates
Solution Approach 1:
The patent introduces relative location references as an intermediary mechanism that bridges the gap when absolute localization (GPS) fails. Instead of relying solely on complex infrastructure or multiple redundant systems, the system uses relative positioning as a mediator that works in conjunction with absolute positioning when available, and independently when absolute positioning fails, thus maintaining reliability without significantly increasing device complexity
Data Source
AI summary
Technologies and techniques for expanding an information cluster of a trajectory class. A trajectory is taken for a vehicle and a location-specific characteristic of the vehicle is acquired, and/or a vehicle-specific characteristic of the vehicle is acquired when taking the trajectory. The location-specific characteristic of the vehicle is provided and/or the vehicle-specific characteristic of the vehicle is provided. Additional steps include generating an actual trajectory by assigning the location-specific characteristic and/or the vehicle-specific characteristic to the taken trajectory, and comparing the actual trajectory with a comparison trajectory. The actual trajectory is assigned to one of two different trajectory classes depending on the comparison, such that the trajectory class into which the actual trajectory is classified is expanded in relation to its information cluster.