Vehicle Positioning via Sensor Data Fusion
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Solution Overview
Problem
Current global positioning systems for road vehicles, especially autonomous driving vehicles, face inaccuracies due to reliance on GPS coordinates, which may not reflect the actual vehicle position, necessitating improved methods for precise positioning.
Innovation Solution
A method involving a data server that acquires and processes data from onboard sensors on a road vehicle and neighboring vehicles using a data fusion algorithm, incorporating relative positions, heading angles, and velocities to calculate adjusted global positioning data, enhancing positioning accuracy through cooperative vehicle networking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS coordinates are used for vehicle positioning, then the positioning system is simple and widely available, but the positioning accuracy is insufficient for autonomous driving requirements
Solution Approach 1:
The patent combines GPS positioning with onboard sensor data (cameras, radar, LIDAR) and data from neighboring vehicles to create a fused positioning system. The server integrates multiple data sources including global positioning data, sensor data about surrounding objects, and motion models to calculate adjusted positioning data that achieves centimeter-level accuracy while maintaining system feasibility through coordinated operation of multiple components
Solution Approach 2:
The patent introduces a server as an intermediary that processes and fuses positioning data from multiple sources. The server receives GPS coordinates, sensor data from the ego-vehicle and neighboring vehicles, and computational results from motion models, then outputs adjusted positioning data. This intermediary coordinates the complex interactions between multiple positioning systems without requiring each vehicle to independently manage all processing logic
2Measurement precision
If onboard sensors are used to detect surrounding objects, then the positioning accuracy can be improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent makes onboard sensors serve multiple functions: they detect surrounding objects for collision avoidance, provide data for positioning correction, and enable cooperative positioning with neighboring vehicles. The same camera, radar, and LIDAR systems used for basic autonomous driving functions are leveraged to enhance positioning accuracy, eliminating the need for separate dedicated positioning sensors and reducing overall system complexity
Solution Approach 2:
The system uses the vehicle's own onboard sensors to generate positioning correction data without requiring external infrastructure. The sensors detect surrounding objects and provide range and bearing information that feeds into motion models and data fusion algorithms, allowing the vehicle to self-correct its GPS positioning errors using its existing sensor suite rather than relying on additional specialized equipment
3Measurement precision
If data from multiple neighboring vehicles is processed, then the positioning accuracy is enhanced, but the data processing load and communication requirements increase
Solution Approach 1:
The patent performs preliminary processing of sensor data and motion models on individual vehicles before sending results to the server. Each vehicle pre-processes its own sensor data and neighboring vehicle data using local motion models, then sends intermediate results to the server for final fusion. This distributed preliminary processing reduces the computational burden on the central server and improves overall data processing efficiency while maintaining high positioning accuracy
Data Source
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AI summary
Method for improving global positioning performance of a first road vehicle (10), the method comprising, by means of a data server (3, 4, 4"): acquiring data from onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g) arranged on the first road vehicle (10) and on at least two neighbouring road vehicles (10', 10", 10'"), the data comprising data on relative positions and data on heading angle and velocity of the road vehicles (10, 10', 10", 10'"), and acquiring global positioning data of at least two of the road vehicles (10, 10', 10", 10'"), processing (102) data comprising the global positioning data, the data, with corresponding timestamp, acquired from the onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g), and a motion model for each of the first road vehicle (10) and the at least two neighbouring road vehicles (10', 10", 10"') using a data fusion algorithm, calculating adjusted global positioning data for the first road vehicle (10) and communicating (104) the adjusted global positioning data to a positioning system (6) of the first road vehicle (10).