Shared Vehicle Location Fusion Using Camera and Sensor Data
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Solution Overview
Problem
Service providers face challenges in accurately locating shared vehicles due to environmental obstacles like building reflections and the urban canyon effect, which affects GPS and GPRS positioning, leading to user inconvenience and increased costs for both users and service providers.
Innovation Solution
A method that combines image-based location data from a camera sensor with other location data sources to determine the accurate position of a shared vehicle, using augmented reality and sensor fusion to improve location accuracy, especially in areas where GPS or GPRS signals are unreliable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS or GPRS positioning is used to locate shared vehicles, then the positioning system is simple and low-cost, but the location accuracy deteriorates in urban environments with building reflections and urban canyon effects
Solution Approach 1:
The patent combines multiple positioning methods (GPS, GPRS, and image-based location recognition) into a fused location data system. The camera captures images of the shared vehicle and surrounding environment, which are then processed to determine location by matching with pre-stored reference images. This merged approach compensates for GPS/GPRS inaccuracies in urban environments while maintaining reasonable system complexity through modular architecture.
2Reliability
If image-based location data is captured and processed to improve location accuracy, then the positioning reliability improves in challenging environments, but the device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-capturing and storing reference images of shared vehicles at known locations before actual positioning is needed. These reference images, along with their associated location data, are stored in a database for later comparison. When positioning is required, the system only needs to capture a current image and match it against the pre-stored references, significantly reducing real-time processing complexity while maintaining high reliability.
3Measurement precision
If multiple sources of location data are fused to determine vehicle position, then the measurement precision improves, but the data processing complexity and time consumption increase
Solution Approach 1:
The system implements partial action by selectively using location data sources based on environmental conditions. When GPS/GPRS signals are available and environmental factors (building reflections, urban canyon effects) are not present, the system uses only these simpler positioning methods. Image-based location recognition and data fusion are activated only when GPS/GPRS accuracy is insufficient or environmental conditions indicate potential positioning errors, thus reducing unnecessary processing time while maintaining precision when needed.
Data Source
AI summary
An approach is provided for determining a location of a shared vehicle based on fused location data. The approach includes initiating a capture of an image of a shared vehicle using a camera sensor of a device. The approach also includes processing the image to determine an image-based location of the shared vehicle. The approach also includes fusing the image-based location with at least one other source of location data indicating a position of the shared vehicle to determine the location of the shared vehicle.