Neural Network Radar Parking Boundary Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current parking space detection systems, especially radar-based systems, face challenges in accurately estimating available parking spaces, particularly in complex environments with uneven terrain and moving objects, making it difficult for drivers to maneuver vehicles into spaces safely and efficiently.
Innovation Solution
A deep learning-based technique utilizing radar sensors and neural networks to process data and estimate parking space boundaries as splines, providing real-time, scalable, and accurate detection of available parking spaces by differentiating between relevant and spurious objects, enabling vehicles to navigate and park autonomously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If radar-based detection systems are used to detect parking spaces, then real-time detection capability is improved, but measurement precision deteriorates in complex environments with uneven terrain and moving objects
Solution Approach 1:
The patent introduces an intermediary processing layer between raw radar detections and parking space boundary estimation. A neural network model acts as a mediator that processes radar coordinate data, filters out spurious objects, and generates refined boundary estimates. This intermediary processing resolves the contradiction by maintaining real-time detection while improving precision through intelligent data filtering and interpretation.
Solution Approach 2:
The patent replaces traditional mechanical/geometric boundary estimation methods with a neural network-based system. Instead of using simple radar point cloud processing or geometric algorithms, the system employs machine learning to automatically distinguish relevant from spurious objects and estimate boundaries. This substitution enables real-time operation while achieving high precision in complex environments.
2Device complexity
If traditional radar processing methods are used, then system complexity is reduced, but detection reliability deteriorates in the presence of spurious objects and uneven terrain
Solution Approach 1:
The neural network model serves as an intermediary that enhances reliability without significantly increasing system complexity. By training the network on diverse parking scenarios including spurious objects and uneven terrain, the system achieves robust detection. The network processes radar data in real-time and outputs reliable boundary estimates, resolving the contradiction between simplicity and reliability.
Solution Approach 2:
The patent changes the processing parameters from raw radar coordinates to neural network-estimated boundary parameters. This transformation allows the system to filter out spurious objects and adapt to varying terrain conditions. The parameter change from raw detections to refined boundary estimates improves reliability while maintaining manageable system complexity through efficient network architecture.
3Measurement precision
If detailed radar coordinate data is processed to estimate parking space boundaries, then measurement precision is improved, but loss of time increases due to complex data processing
Solution Approach 1:
The neural network model is pre-trained on extensive radar data from various parking scenarios before deployment. This preliminary training allows the network to quickly process new radar inputs in real-time without requiring complex computational operations during actual parking detection. The pre-learned features enable fast and precise boundary estimation, resolving the contradiction between precision and processing time.
Solution Approach 2:
The patent replaces computationally intensive geometric processing methods with a neural network-based approach. Although the network requires initial training, during operation it processes radar data faster than traditional methods while achieving superior precision. The substitution of mechanical/geometric algorithms with learned patterns enables real-time processing with high measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables robust and efficient detection of available parking spaces, overcoming the limitations of existing systems by providing stable and instantaneous boundary estimation, improving safety and repeatability of automated parking maneuvers.
Implementation Method 1
radar sensors and neural networks to process data and estimate parking space boundaries
Data Source
AI summary
The present invention extends to methods, systems, and computer program products for detecting available parking spaces in a parking environment. Radar systems are utilized to gather data about a parking lot environment. The radar data is provided to a neural network model as an input. Algorithms employing neural networks can be trained to recognize parked vehicles and conflicting data regarding debris, shopping carts, street lamps, traffic signs, pedestrians, etc. The neural network model processes the radar data to estimate parking space boundaries and to approximate the parking space boundaries as splines. The neural network model outputs spline estimations to a vehicle computer system. The vehicle computer system utilizes the spline estimates to detect available parking spaces. The spline estimates are updated as the vehicle navigates the parking environment.