Vehicle Trajectory Evaluation Without GPS Using Singular Values
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Solution Overview
Problem
In underground parking environments without GPS signals, determining whether a vehicle is on a straight or turning section is inaccurate due to reliance on image data or additional sensors, affecting efficiency and increasing costs.
Innovation Solution
A method using singular value feature matrices and eigenvalues from existing vehicle sensors, such as inertial sensors and speedometers, to differentiate between straight and turning sections by determining if the target eigenvalue is below a preset threshold, allowing for efficient and cost-effective trajectory evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large amount of image data is added to accurately determine whether the section is a straight section or a turning section, then the measurement precision is improved, but the device complexity and loss of time increase
Solution Approach 1:
The patent extracts only the essential coordinate information from image data to construct a simplified trajectory model. By focusing on key geometric features rather than processing all image data, the system achieves accurate trajectory section determination while significantly reducing computational complexity and processing time.
Solution Approach 2:
The patent transforms the trajectory determination problem from image space to coordinate space by changing the representation parameters. Instead of analyzing complex image patterns, the system uses coordinate geometry and mathematical models to determine whether sections are straight or turning, simplifying the computational task while maintaining precision.
2Measurement precision
If additional sensors are disposed to accurately determine whether the section is a straight section or a turning section, then the measurement precision is improved, but the device complexity and cost increase
Solution Approach 1:
The patent makes existing vehicle sensors serve multiple functions. The same sensors used for basic vehicle operation and navigation are leveraged for trajectory section determination, eliminating the need for additional dedicated sensors while achieving accurate differentiation between straight and turning sections.
Solution Approach 2:
The system uses the vehicle's own existing sensor data and trajectory information to determine section types. By self-serving the trajectory analysis function using already-collected operational data, the system avoids additional sensor costs and complexity while maintaining determination accuracy.
3Adaptability or versatility
If image sensing devices are used to perform inference mapping through dead reckoning in underground environments, then the adaptability is improved, but the measurement precision deteriorates due to inability to accurately determine straight or turning sections
Solution Approach 1:
The patent performs preliminary construction of a simplified trajectory model using coordinate information before attempting to determine section types. By pre-processing the trajectory data into a mathematical representation, the system enables more accurate subsequent analysis of whether sections are straight or turning, improving precision in GPS-denied environments.
Solution Approach 2:
The patent replaces image-based visual analysis with a mathematical modeling approach. By substituting the mechanical/image processing system with a coordinate geometry and mathematical optimization system, the method achieves better precision in determining trajectory sections while maintaining adaptability to underground environments.
Data Source
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AI summary
A method and an apparatus for evaluating a running trajectory of a vehicle without a GPS signal are provided, which belong to the technical field of vehicles. The method includes: obtaining coordinate information of a running trajectory of a vehicle in a running process of the vehicle; after N pieces of coordinate information are obtained, determining a singular value feature matrix; obtaining a target eigenvalue of the singular value feature matrix, and determining whether the target eigenvalue is less than a straight line determining threshold; and when the target eigenvalue is less than the straight line determining threshold, determining that a section on which a running trajectory corresponding to the N pieces of coordinate information is located is a straight section; or when the target eigenvalue is not less than the straight line determining threshold, determining that a section on which a running trajectory corresponding to the N pieces of coordinate information is located is a turning section. The running trajectory of the vehicle is evaluated with low costs and high efficiency when there is no GPS signal. In addition, applying the low-cost and high-efficiency method for evaluating a running trajectory of a vehicle to a mapping scenario for autonomous driving can reduce mapping costs and improve mapping efficiency.