Parking Row Orientation Detection via Entropy Analysis
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Solution Overview
Problem
Current vehicle environmental detection systems lack an efficient method to determine the orientation of parking rows, which is crucial for assisting drivers during parking operations, as they rely on manual identification or complex calculations.
Innovation Solution
A vehicle environmental detection system equipped with a detector arrangement and a control unit that calculates the slant angle of parking slots by rotating detections, creating a projection profile and determining entropy to identify the optimal slant angle, independent of the vehicle's movement direction and velocity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification or complex calculations are used to determine parking row orientation, then measurement precision may be improved, but device complexity and loss of time increase
Solution Approach 1:
The system automatically determines parking row orientation using entropy calculation on projection profiles generated from detector data. The control unit processes detections independently to compute entropy values for different slant angles, eliminating the need for manual identification or complex external calculations while achieving accurate orientation detection
Solution Approach 2:
The system transforms the detection data by calculating projection profiles at different slant angles and computes entropy values to identify the optimal orientation. By changing the parameter space from raw detector coordinates to entropy-based orientation classification, the system achieves simple and accurate parking row orientation determination
2Device complexity
If manual identification is used to determine parking row orientation, then device complexity is reduced, but loss of time increases
Solution Approach 1:
The control unit autonomously processes detector arrangements to automatically determine parking row orientation through entropy calculation. This self-service approach eliminates manual identification steps, significantly reducing the time required for orientation determination while maintaining acceptable system complexity through algorithmic efficiency
Solution Approach 2:
The system pre-calculates projection profiles and entropy values for different slant angles based on detector data. This preliminary processing enables rapid orientation determination without requiring complex real-time calculations or manual intervention, thus reducing time loss
3Measurement precision
If complex calculations are used to determine parking row orientation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system transforms the computational approach by changing from complex geometric calculations to entropy-based parameter evaluation. By computing projection profiles at discrete slant angles and evaluating entropy values, the system achieves accurate orientation detection with simpler, more efficient calculations that reduce processing time
Solution Approach 2:
The system replaces complex mechanical or geometric calculation methods with information-theoretic entropy calculation. This substitution enables accurate parking row orientation determination through statistical analysis of projection profiles, achieving both high precision and reduced computational time
Data Source
AI summary
A vehicle environmental detection system (3) in an ego vehicle (1) and having at least one detector arrangement (4, 7) and at least one control unit arrangement (15) configured to determine a slant angle of parking slots in a parking row. The detector arrangement (4, 7) is adapted to obtain a set of detections (d(k), k=1 . . . K). For each slant angle (αn) in a set of slant angles (αn, n=1 . . . N), the control unit arrangement (15) is adapted to calculate a set of slant rotated detections (dslant(k, αn), k=1 . . . K) by rotating coordinates of each detection in the set of detections (d(k), k=1 . . . K)) by the present slant angle (αn), calculate a projection profile (Prow(αn)) for the set of slant rotated detections (dslant(k,αn), k=1 . . . K) by determining a histogram of slant rotated detection coordinates, and to calculate an entropy (Hrow(αn)) associated with the calculated projection profile (Prow(αn)). The control unit arrangement (15) can determine the slant angle based on the calculated entropies (Hrow(αn), n=1 . . . N).