Autonomous Vehicle Parking Scoring System
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Solution Overview
Problem
Autonomous vehicles face challenges in selecting optimal parking locations due to the lack of efficient systems that consider multiple factors such as distance to the destination, lane width, parking laws, detected objects, traffic flows, and ride comfort, which can lead to suboptimal parking decisions.
Innovation Solution
A system and method for selecting a parking location for an autonomous vehicle that involves obtaining data on current ride conditions, determining proximity to a destination, identifying potential parking locations, calculating scores based on various factors like distance, lane width, parking laws, detected objects, traffic flows, and ride comfort, and automatically selecting the best location for parking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If autonomous vehicles use simple parking selection methods, then the system complexity is low, but the parking location selection quality deteriorates
Solution Approach 1:
The parking selection system is segmented into multiple independent evaluation modules, each responsible for assessing a specific factor (distance to destination, lane width, parking laws, detected objects, traffic flows, ride comfort). Each module processes one aspect and contributes to the overall scoring, allowing the system to maintain high reliability through specialized evaluation while keeping individual module complexity manageable.
Solution Approach 2:
The parking selection system is designed as a multi-functional evaluation framework that simultaneously considers six different factors (distance, lane width, laws, objects, traffic, comfort). This universal approach enables a single system to handle diverse parking scenarios and make comprehensive decisions, improving selection quality without requiring separate specialized systems for each factor.
2Reliability
If multiple factors are considered in parking location selection, then the parking decision quality improves, but the calculation complexity increases
Solution Approach 1:
The calculation system is divided into separate scoring modules, each handling a specific factor (distance scoring, lane width scoring, legal compliance scoring, etc.). This segmentation allows each module to perform focused calculations independently, improving overall decision quality through comprehensive factor consideration while managing computational complexity through modular architecture.
Solution Approach 2:
The system transforms multiple qualitative factors (lane width adequacy, legal compliance, traffic conditions) into quantitative scores through parameter transformation. Each factor is converted into a numerical value that can be systematically weighted and aggregated, enabling complex multi-factor evaluation through standardized mathematical operations rather than complex logical reasoning.
3Measurement precision
If comprehensive data collection is performed for parking selection, then the selection accuracy improves, but the data processing time increases
Solution Approach 1:
The system performs preliminary data collection and evaluation of parking factors before the vehicle actually needs to park. By continuously monitoring and pre-processing information about destination proximity, lane characteristics, parking laws, and traffic conditions, the system prepares evaluation data in advance, enabling accurate parking decisions to be made quickly when the parking event occurs.
Solution Approach 2:
Data processing is segmented into parallel streams for each evaluation factor (distance calculation, lane width measurement, legal rule checking, object detection, traffic flow analysis, comfort assessment). This parallel processing architecture allows comprehensive data collection and analysis to occur simultaneously rather than sequentially, improving selection accuracy while minimizing processing time through concurrent evaluation of all factors.
Data Source
AI summary
In one embodiment, a method for selecting a parking location for an autonomous vehicle includes: obtaining data pertaining to a current ride of the autonomous vehicle during operation of the autonomous vehicle; determining, by a processor using the data, when the autonomous vehicle is proximate a destination; and, when the autonomous vehicle is proximate the destination: identifying, by the processor using the data, a plurality of potential parking locations proximate the destination; calculating, by the processor using the data, a respective score for each of the potential parking locations using a plurality of factors; and selecting, by the processor using the data, a selected parking location of the potential parking locations based on the respective score of each of the potential parking locations.