Vehicle Radar Parking Row Detection via Recursive RANSAC
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Solution Overview
Problem
Current vehicle radar systems are inefficient in detecting available parking spaces and defining the boundaries of parked vehicle rows, especially when these are partially covered or merged with road borders or other obstacles, and lack a straightforward method to identify parking rows without reference points.
Innovation Solution
A vehicle radar system with a control unit that processes radar detections using a recursive RANSAC algorithm to determine dominating lines and remove associated detections, analyzing radar detection density distributions to identify parallel lines and define parking row boundaries, allowing for robust and uncomplicated detection of parking rows without external references.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a vehicle radar system uses conventional obstacle detection methods, then it can detect objects in the surroundings, but it is inefficient in detecting available parking spaces and defining boundaries of parked vehicle rows
Solution Approach 1:
The patent segments the detection process into distinct phases: obtaining raw radar detections, determining dominating lines through iterative processing, removing associated detections, and analyzing density distributions. This segmentation transforms the complex task of parking space detection into manageable sequential steps, improving detection efficiency without requiring a completely new system architecture.
Solution Approach 2:
The patent performs preliminary actions by first determining dominating lines from radar detections before proceeding to identify parking spaces. The iterative process of finding and removing detections associated with dominating lines prepares the data structure in advance, making the subsequent parking space identification more efficient and accurate.
2Reliability
If the radar system uses a straightforward method to identify parking rows, then the detection process is simple, but it cannot handle cases where parking rows are partially covered or merged with road borders or other obstacles
Solution Approach 1:
The patent employs a dynamic iterative algorithm that adapts to different detection scenarios. The process repeatedly determines dominating lines, removes associated detections, and re-evaluates until convergence, allowing the system to dynamically adjust to complex environments where parking rows are partially covered or merged with obstacles, thereby improving detection reliability.
Solution Approach 2:
The patent introduces the dimension of density distribution analysis perpendicular to the dominating lines. By analyzing radar detection density distributions in spatial slots running parallel to the lines, the system can identify peaks that indicate parking row boundaries, adding a new analytical dimension that improves reliability in complex environments.
3Measurement precision
If the system processes all radar detections to identify parking rows, then comprehensive detection is achieved, but the processing time increases
Solution Approach 1:
The patent extracts and removes radar detections that are associated with already identified dominating lines from the dataset. This extraction process eliminates redundant processing of detections that have already been accounted for, reducing processing time while maintaining comprehensive detection of all parking rows through subsequent iterative processing.
Solution Approach 2:
The patent implements a stopping criterion that allows the iterative process to terminate when a sufficient number of parking rows have been identified or when further iterations yield diminishing returns. This partial action approach balances processing time against detection precision, avoiding unnecessary excessive processing while ensuring adequate detection accuracy.
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
Enables fast, robust, and accurate detection of parking rows, even in complex environments, without the need for reference points, allowing for effective identification of available parking spaces.
Implementation Method 1
one or more radar systems as well as other vehicle environmental detection systems such as for example Lidar (Light radar detection and ranging) and camera images, are often used in vehicles in order to detect obstacles in the surroundings
Implementation Method 2
A vehicle radar system is usually arranged to distinguish or resolve single targets from the surroundings by using a Doppler effect in a previously well-known manner
Data Source
AI summary
A vehicle radar detection system arranged to be mounted in an ego vehicle and including at least one detector arrangement and at least one control unit arrangement. The detector arrangement is adapted to obtain a dataset initially including a number K of radar detections. The control unit arrangement is adapted to repeatedly determine a dominating line from the dataset of radar detections, remove radar detections associated with the dominating line from the dataset of radar detections until a first stopping criterion is fulfilled, thereby determining a plurality of lines from the number K of radar detections.