Probabilistic Parking Space Selection for Front-In Perpendicular Parking
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Solution Overview
Problem
Existing autonomous and automated parking systems typically rely on back-in parking techniques, which are inefficient in crowded or congested parking environments, as they require vehicles to pass through parking spaces to confirm availability and dimensions, limiting their ability to perform practical front-in perpendicular parking.
Innovation Solution
A probabilistic approach is used to select a parking space based on sensor data from vision-based, radar, lidar, or ultrasonic systems, determining parking-space characteristics such as width, entry turning radius, and longitudinal distance to enable either a single-turn or two-turn front-in parking maneuver without physically passing the space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If back-in parking technique is used to confirm parking space availability, then the system can verify space dimensions, but the vehicle must pass through the parking space which reduces parking efficiency in crowded environments
Solution Approach 1:
The system performs preliminary detection of parking space characteristics (width, length, occupancy) using sensors before the vehicle attempts to park. This allows the vehicle to identify suitable parking spaces and plan maneuvers in advance, eliminating the need to pass through spaces to verify availability and improving parking efficiency in crowded environments
2Reliability
If the vehicle passes through parking spaces to confirm availability, then accurate space detection is achieved, but the complexity of the parking maneuver increases
Solution Approach 1:
The system replaces the mechanical approach of physically passing through parking spaces with a sensor-based detection system. Sensors (cameras, LIDAR, ultrasonic sensors) detect parking space characteristics and occupancy status, allowing accurate space verification without complex manual maneuvering and reducing overall parking system complexity
3Ease of operation
If front-in perpendicular parking is implemented, then parking becomes more natural and practical, but the system needs advanced probabilistic selection capability
Solution Approach 1:
The system uses probabilistic modeling to evaluate multiple parking space parameters (width, length, occupancy probability, maneuverability) simultaneously. By calculating probability distributions for each parameter and combining them, the system selects the most suitable parking space for front-in perpendicular parking, making the operation more natural while managing selection complexity through mathematical modeling
Data Source
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AI summary
This document describes techniques and systems for selecting a parking space using a probabilistic approach. An example system includes a processor that can determine whether multiple parking spaces are available in proximity to a host vehicle using sensor data. Parking-space characteristics (e.g., a width, entry turning radius, and longitudinal distance to the parking space) of each available parking space are determined using the sensor data. The processor can then choose a selected parking space among the multiple parking spaces based on the parking-space characteristics. The processor or another processor can then control the host vehicle to park in the selected parking space using an assisted-driving or autonomous-driving system. In this way, the described system can select and navigate to a parking space using a probabilistic approach. The processor can choose the parking space and parking maneuver based on programmable and customizable parking-space characteristics in some implementations.