Crowdsourced Database for Vehicle Pull-Off Area Detection
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Solution Overview
Problem
Vehicles often lack visibility to available pull-off and parking locations, and existing on-board sensors have limited look-ahead distance, making it difficult for drivers to find safe and beneficial pull-off areas, especially when these locations are not within line of sight or are obscured by obstacles.
Innovation Solution
A database management system that aggregates and crowdsources information from various vehicles and sources to create a centralized database of pull-off areas, using sensors and mobile devices to collect and correlate data on surface conditions, obstacles, and amenities, providing confidence rankings for potential pull-off areas accessible by geographic location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicles rely on on-board sensors to detect pull-off areas, then the vehicle can identify immediate safety concerns, but the look-ahead distance is limited and pull-off areas beyond sensor range cannot be detected
Solution Approach 1:
The system performs preliminary detection of pull-off areas using crowd-sourced data from multiple vehicles before the host vehicle reaches those locations. This allows the system to identify and rank suitable pull-off areas in advance, beyond the immediate sensor range, and provide this information to the driver or autonomous system for future maneuver planning.
Solution Approach 2:
A centralized server acts as an intermediary between multiple vehicles and the host vehicle. The server aggregates pull-off area data from crowd-sourced vehicles, processes this information to identify suitable areas, and transmits the processed data to the host vehicle. This intermediary enables information sharing across the vehicle fleet, extending the effective detection range beyond individual vehicle sensors.
2Adaptability or versatility
If the system provides comprehensive pull-off area information from distant locations, then the driver gains access to more options with additional services, but the system complexity increases due to data aggregation and processing requirements
Solution Approach 1:
The system extracts only the essential information needed for pull-off area identification from the crowd-sourced data, such as location coordinates, surface conditions, and obstacle presence. By extracting and storing only these critical parameters in a structured format, the system reduces the complexity of data management while maintaining comprehensive coverage of pull-off area options.
Solution Approach 2:
The system creates simplified digital representations (copies) of pull-off area characteristics based on crowd-sourced observations. Instead of processing raw sensor data from multiple vehicles, the system generates standardized data records that capture the essential features of each pull-off area, making the information easily accessible and reducing processing complexity for the host vehicle.
3Measurement precision
If the system aggregates data from multiple vehicles and sources, then the accuracy and reliability of pull-off area information improves, but the time required to collect and process data increases
Solution Approach 1:
The system performs data aggregation and processing in advance as crowd-sourced vehicles traverse different locations. Pull-off area information is collected, validated, and stored in the centralized server before the host vehicle needs it. This preliminary processing eliminates the need for real-time data collection when the host vehicle approaches potential pull-off areas, reducing latency.
Solution Approach 2:
The crowd-sourced data collection operates continuously as vehicles are deployed throughout the service area. Multiple vehicles constantly gather and update pull-off area information, ensuring that the database is continuously refreshed with current data. This continuous operation maintains high information accuracy without requiring intensive batch processing when needed.
Data Source
AI summary
Aspects of the present disclosure involve systems, methods, computer program products, and the like, for creating a database of pull-off areas for a vehicle based on aggregated pull-off information received from a plurality of vehicles and other sources. In one embodiment, the aggregated, crowd-sourced pull-off information may be available to a mobile device (such as an autonomous vehicle or mobile communication device) for reference by the device to understand the condition, location, and availability of potential pull-off areas near a roadway.