Parking Score Calculation System for Vehicle Space Selection
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Solution Overview
Problem
Drivers and automated vehicle systems lack information about parking difficulties in specific spaces, receiving only high-level feedback on parking accuracy, which complicates decision-making for selecting suitable parking spaces.
Innovation Solution
A system that calculates vehicle parking scores based on historical data using sensors and localization methods, providing drivers and autonomous systems with informed decisions on parking space selection by assessing distance, alignment, and potential damage risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If high-level feedback is provided to drivers about parking accuracy, then the system is simple to operate, but drivers lack detailed information needed for informed parking space selection decisions
Solution Approach 1:
The system creates a simplified copy of complex parking data by generating aggregate parking scores that represent historical parking difficulty. Instead of presenting raw sensor data and detailed metrics, the system computes a single score (e.g., 0-100) that copies the essential information about parking difficulty, making it easily consumable for drivers while preserving the underlying detailed analysis.
Solution Approach 2:
The parking score acts as an intermediary between the complex historical parking data and the driver's decision-making process. Rather than directly presenting raw data about vehicle positions, sensor readings, and parking outcomes, the system introduces a computed score that mediates this information, translating complex historical patterns into actionable guidance for space selection.
2Measurement precision
If detailed parking data is collected and analyzed, then parking score accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing by collecting and analyzing historical parking data in advance, computing parking scores before they are needed for decision-making. Historical parking outcomes, vehicle positions, and sensor data are processed beforehand to generate pre-computed scores that can be quickly retrieved and presented to drivers without requiring real-time computation during the parking selection process.
Solution Approach 2:
The parking score computation is segmented into distinct processing stages: data collection from multiple sources, historical outcome analysis, score computation, and validation. This segmentation allows the system to process different aspects of parking data independently and efficiently, reducing overall computational complexity while maintaining accuracy through systematic analysis of each component.
3Adaptability or versatility
If parking scores are calculated based on multiple factors including vehicle type and parking space characteristics, then the scoring system becomes more accurate, but the system complexity increases
Solution Approach 1:
The system applies local quality by tailoring parking scores to specific vehicle types and parking space characteristics. Instead of using a single universal scoring model, the system adjusts scoring criteria and weightings based on the particular vehicle (e.g., compact car vs. SUV) and space features (e.g., perpendicular vs. parallel parking), ensuring each score is optimized for its specific context while maintaining a unified overall framework.
Solution Approach 2:
The system changes parameters dynamically based on vehicle type and parking space characteristics. Different vehicle dimensions, weights, and maneuverability characteristics modify the scoring parameters, while space features like size, orientation, and surrounding obstacles adjust the evaluation criteria. These parameter changes allow the system to adapt to diverse conditions without requiring entirely separate scoring systems for each scenario.
Data Source
AI summary
This disclosure describes systems and methods for determining vehicle parking scores. An example method may include calculating, subsequent to a first vehicle parking within a first parking space of a parking lot and based on first sensor data obtained by the first vehicle, a first vehicle parking score. The example method may also include calculating, subsequent to a second vehicle parking within the first parking space of the parking lot and based on second sensor data obtained by the second vehicle, a second vehicle parking score. The example method may also include calculating a first average vehicle parking score for the first parking space based on the first vehicle parking score and the second vehicle parking score. The example method may also include presenting an indication of the first average vehicle parking score for the first parking space to a third vehicle within the parking lot.