Rideable Vehicle Location Accuracy via Multi-Device Data Fusion
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Solution Overview
Problem
Dynamic transportation matching systems face challenges in accurately determining the location of rideable vehicles, leading to inefficient matching and user frustration due to imprecise location data.
Innovation Solution
The system improves location accuracy by utilizing data from multiple devices, such as scooters and mobile devices, and employing methods like satellite detection for confidence assessment, thereby refining location estimates and enhancing matching decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If location data is obtained solely from rideable vehicles, then device complexity is reduced, but measurement precision deteriorates leading to inaccurate location determination
Solution Approach 1:
The patent combines location data from multiple sources including rideable vehicles, mobile devices, and infrastructure elements (such as poles or signs) to determine the location of rideable vehicles. This data fusion approach improves measurement precision by cross-validating and supplementing location information from diverse sources, thereby resolving the contradiction between accuracy and system complexity.
Solution Approach 2:
The patent introduces infrastructure elements (poles, signs, or other fixed objects) as intermediaries to assist in location determination. These infrastructure elements provide reference points that can be detected by both the rideable vehicles and mobile devices, enabling more accurate triangulation and location estimation without requiring direct communication between all devices.
2Measurement precision
If multiple data sources are integrated to improve location accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system employs feedback mechanisms where location data from multiple sources is continuously cross-checked and validated. The system adjusts its location determination algorithms based on the consistency and reliability of data from different sources, improving precision while managing processing complexity through adaptive feedback loops.
Solution Approach 2:
The patent segments the location determination task into separate modules: data collection from rideable vehicles, data collection from mobile devices, data collection from infrastructure elements, and a central processing unit that integrates these sources. This segmentation allows for more manageable data processing and improves measurement precision through specialized processing of each data type.
Data Source
AI summary
The disclosed computer-implemented method may include using location data from multiple devices improve the accuracy of location estimates for a rideable vehicle and/or provide calibration for location information. In some examples, at the end of a ride the system may record the location of the rider's device to augment the location data from the rideable vehicle. In another example, when a batch of rideable vehicles are delivered to a location, the system may use the location of the operator's device and/or the locations of other rideable vehicles in the batch to increase the accuracy of data for the location of any given rideable vehicle. In some embodiments, the system may detect the number of functioning location sensors to determine the confidence in the location of the vehicle. This confidence information can then be used in network decisions such as matching. Various other methods, systems, and computer-readable media are also disclosed.