Connected Vehicle Road Positioning in Congested Urban Networks
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Solution Overview
Problem
Existing navigation systems in congested urban areas provide inaccurate vehicle positioning due to complex road networks and GPS errors, leading to incorrect navigational information.
Innovation Solution
A method that calculates a vehicle's precise position by integrating real-time data from the target vehicle and surrounding vehicles, considering speed, traffic congestion, and historical data to determine the elapsed time difference, thereby enhancing the accuracy of road detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional GPS-based navigation systems are used in congested urban areas, then the system structure remains simple, but the positioning accuracy deteriorates due to complex road networks and overlapping roads
Solution Approach 1:
The patent combines GPS positioning data with vehicle sensor data (odometry, inertial measurement units) and map matching algorithms to create a hybrid positioning system. This merging of multiple data sources compensates for GPS inaccuracies in urban canyons while maintaining reasonable system complexity through integrated processing.
Solution Approach 2:
The patent introduces map data as an intermediary layer between raw GPS coordinates and final position determination. By matching GPS points with predefined road segments and using map-based constraints, the system resolves positioning ambiguities in complex urban environments without requiring fundamentally more complex hardware.
2Measurement precision
If more vehicles are used for data collection to improve positioning accuracy, then the positioning precision improves, but the data processing complexity and time requirements increase
Solution Approach 1:
The patent performs preliminary processing of vehicle data by pre-calculating trajectory segments, speed profiles, and positional estimates before final road detection. This allows real-time or near-real-time processing of multiple vehicle datasets without linearly increasing computation time, as basic transformations are pre-computed.
Solution Approach 2:
The patent processes data from multiple vehicles but focuses computational resources on a subset of high-quality trajectories or uses sampling strategies. By processing partial data sets strategically or using excessive data with selective filtering, the system achieves high accuracy without processing every available data point in full detail.
3Measurement precision
If real-time data from multiple vehicles is integrated to determine precise position, then the positioning accuracy improves, but the computational complexity and data requirements increase
Solution Approach 1:
The patent segments the positioning problem into independent modules: individual vehicle trajectory estimation, relative position calculation, consensus algorithms for multi-vehicle data fusion, and final road detection. This segmentation allows each module to be optimized independently and reduces overall system complexity through modular architecture.
Solution Approach 2:
Each vehicle in the system independently computes its own position and trajectory using its sensors and algorithms, then shares results with the network. This self-service approach distributes computational burden across vehicles rather than requiring a central system to process all raw data, reducing overall system complexity.
Data Source
AI summary
A method, computer system, and a computer program product are provided for determining precise location of a target vehicle dynamically. Data relating a target vehicle is obtained to determine the target vehicle's position and route. The data relates to said target vehicle's speed and general moving and directional position. An estimate is calculated based on information about target vehicle movement using speed and traffic congestion and based on position of the target vehicle. Information is obtained about movement of a plurality of other vehicles in proximity of the target vehicle. An elapsed time difference is determined between progress of movement of said target vehicle and a real-time progress of said plurality of vehicles progress. An effectiveness estimate is calculated and the target vehicle's precise position is determined based on the effectiveness estimate.