Parking Space Opening Detection Using Decision Trees
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Solution Overview
Problem
Existing automated parking technologies face limitations in accuracy and generalization capability due to varying use scenarios, necessitating improved parking space opening detection methods.
Innovation Solution
A parking space opening detection method and apparatus utilizing a decision tree model to analyze feature information such as length, width, type, and obstacle information, combined with deep learning models for enhanced prediction accuracy and robustness, including a training data set enrichment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional parking space detection methods are used, then the system is simple to implement, but the accuracy and generalization capability are limited
Solution Approach 1:
The patent replaces traditional mechanical/geometric detection methods with a decision tree-based computational model. The system uses feature information (length, width, type, obstacle data) as inputs to a decision tree algorithm that outputs predicted parking space openings, substituting physical measurement systems with intelligent computational processing to achieve higher accuracy without proportional increases in hardware complexity
Solution Approach 2:
The patent transforms the detection approach by changing from direct geometric measurement to multi-parameter analysis. It considers multiple features including parking space length, width, type, and obstacle information simultaneously, using these parameter changes to feed into the decision tree model for more accurate prediction of parking space openings
2Adaptability or versatility
If a single scenario training model is used, then the model training is fast, but the generalization capability across different scenarios is poor
Solution Approach 1:
The patent creates a universal decision tree model that can handle multiple parking scenarios simultaneously. By designing the model to accept various feature information types (different parking space dimensions, types, and obstacle configurations) as inputs, it achieves multi-functionality that allows the same model structure to generalize across diverse scenarios without requiring scenario-specific model instances
Solution Approach 2:
The patent performs preliminary model training using comprehensive training data that encompasses multiple scenarios. By pre-training the decision tree model with diverse data including different parking space configurations and obstacle arrangements, the system prepares the model in advance to handle various real-world scenarios, reducing the need for extensive retraining when deployed in different environments
3Measurement precision
If feature information from multiple sources is integrated, then the detection accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the complex detection task into distinct feature extraction components. It separately processes different types of input data (parking space geometric features, type information, obstacle data) through dedicated extraction modules, then feeds these segmented features into the decision tree model. This segmentation reduces the complexity of handling integrated multi-source data by processing each feature type independently before integration
Data Source
AI summary
A method may include: obtaining at least one piece of feature information of a parking space; and determining parking space information of the parking space based on the at least one piece of feature information and a parking space detection model, where the parking space information includes a parking space opening of the parking space. According to the method, accuracy of parking space opening detection and a generalization capability of the parking space detection model can be improved.