Cloud Vehicle Positioning via Lidar Map Matching
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Solution Overview
Problem
Existing vehicle navigation systems face inaccuracies in positioning due to environmental factors, particularly in areas with high-rise buildings or shadows, leading to unnecessary path re-searches and user inconvenience, as GPS signals can include significant errors.
Innovation Solution
A method utilizing cloud computing to precisely measure a vehicle's position by extracting feature points with Lidar and employing map matching techniques, where surrounding and driving information are transmitted to a remote server for calculation, allowing for accurate positioning without the need for large-capacity maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS signal is used for vehicle positioning, then positioning function is provided, but positioning precision deteriorates in areas with high-rise buildings or shadows
Solution Approach 1:
The patent introduces map data as an intermediary reference system. Instead of relying solely on direct GPS signals that are blocked or reflected by buildings, the system uses pre-stored map data containing road information and landmarks as a mediator to calculate and correct vehicle position, thereby resolving the positioning accuracy problem in shadow areas and urban canyons
Solution Approach 2:
The patent implements a feedback mechanism where the calculated position based on map matching is continuously compared with GPS position data. The system uses this feedback to identify and correct GPS errors, particularly in environments where GPS signals are unreliable, thereby improving overall positioning precision
2Measurement precision
If map matching technique is used to improve positioning accuracy, then positioning precision is improved, but device complexity increases due to requirement of large-capacity map data
Solution Approach 1:
The patent extracts only the essential map data required for positioning (road information and landmark coordinates) from the complete map database, storing only these critical elements in the vehicle's memory. This extraction approach reduces the map data size and system complexity while maintaining the positioning accuracy benefits of map matching
Solution Approach 2:
The patent segments the map data into different functional components: road network data for path matching, landmark data for position verification, and feature point data for correction. This segmentation allows the system to process and store only the necessary portions of map data, reducing overall system complexity while preserving positioning precision
3Productivity
If GPS position data is used for path guidance, then navigation function is provided, but unnecessary path re-search occurs due to position errors
Solution Approach 1:
The patent implements a feedback mechanism where the map-matched position is continuously compared with the GPS position. When the GPS position deviates from the expected path based on map data, the system uses the map-matched position as feedback to correct the navigation calculation, preventing unnecessary path re-search and improving navigation efficiency
Solution Approach 2:
The patent performs preliminary position calculation using map matching data before relying on GPS position for path guidance. By pre-establishing the expected vehicle position based on map features and movement information, the system can anticipate and correct GPS errors before they cause unnecessary path re-search, thereby improving navigation efficiency
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise real-time vehicle positioning, reducing errors and unnecessary re-searches, even in complex environments, by processing high-capacity data in the cloud and synchronizing information for accurate feature point matching.
Implementation Method 1
extracting a feature point using Lidar
Data Source
AI summary
A method of measuring a position of a vehicle using a cloud computing includes obtaining surrounding information according to a driving of the vehicle and driving information of the vehicle. The obtained surrounding information and the driving information of the vehicle are transmitted to a server which is remotely located from the vehicle and equipped with map data. A position of the vehicle is calculated through the surrounding information and the driving information of the vehicle by the server. The calculated position of the vehicle is transmitted to the vehicle. The calculated position of the vehicle is outputted.