Parking Zone Detection for Available Autonomous Vehicle Spaces
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Solution Overview
Problem
Autonomous vehicles face difficulties in identifying available and adequate parking spaces, especially in areas with parking lanes, due to the challenge of distinguishing between occupied and unoccupied spaces and determining the size of spaces that can accommodate their dimensions.
Innovation Solution
The autonomous vehicle employs sensor data and algorithms to analyze parking zones by generating line references, sampling points, and polygons to identify intersections and determine available spaces based on thresholds for length and width, considering a safety buffer for maneuverability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the autonomous vehicle uses simple parking detection methods, then the device complexity is reduced, but the measurement precision of parking space availability and adequacy deteriorates
Solution Approach 1:
The parking zone is segmented into multiple discrete parking spaces by generating a centerline and dividing the zone into segments along this centerline. Each segment represents a potential parking space, allowing the system to evaluate availability and adequacy of individual spaces without requiring complex object detection for each one.
Solution Approach 2:
The system transforms the 2D parking zone detection problem into a 1D analysis by creating a centerline representation. By sampling points along this centerline and creating polygons at these points, the system reduces computational complexity while maintaining accurate spatial analysis of parking spaces.
2Measurement precision
If the autonomous vehicle uses detailed spatial analysis with polygons and intersections, then the measurement precision of parking space identification is improved, but the computational use of energy increases
Solution Approach 1:
The parking zone is divided into discrete segments along a centerline, with each segment representing a potential parking space. This segmentation allows the system to perform focused computational analysis only on relevant areas rather than processing the entire zone uniformly, reducing overall energy consumption.
Solution Approach 2:
The system creates polygons at sampled points along the centerline and performs intersection tests only where necessary to determine space boundaries. By sampling at strategic intervals rather than continuously, the system achieves sufficient precision while minimizing computational energy expenditure.
3Reliability
If the autonomous vehicle increases the safety buffer for maneuverability, then the reliability of parking operations is improved, but the area of available parking spaces is reduced
Solution Approach 1:
The safety buffer is applied locally at the boundaries of each identified parking space rather than uniformly across the entire zone. By creating polygons at sampled points and using intersection analysis, the system determines the exact available area within each space, maximizing utilization while maintaining necessary safety margins for vehicle maneuverability.
Data Source
AI summary
Techniques and methods for identifying parking zones. For instance, a vehicle may identify a parking zone located near a destination location for the vehicle. The vehicle may then generate one or more lines representing the parking zone. Additionally, the vehicle may generate polygons representing objects located proximate to the parking zone. The vehicle may then determine whether the one or more lines intersect with one or more of the polygons. If the vehicle determines that the one or more lines intersect with one or more of the polygons, then the vehicle may identify one or more first portions of the parking zone that are occupied by one or more objects and as such, unavailable. Using the one or more first portions, the vehicle may identify one or more second portions of the parking zone that are not occupied by objects and as such, available.