Digital Map Creation Using Inter-Vehicle Distance Data
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Solution Overview
Problem
Existing methods for creating digital maps for automated vehicles using SLAM techniques face challenges in accuracy due to sparse landmark coverage, leading to poor localization quality in areas with few recognizable landmarks.
Innovation Solution
The method involves identifying and transmitting data between vehicles, including distance information, to enhance the SLAM graph optimization, allowing for improved digital map creation without requiring localization data, using various sensors like video, radar, or ultrasonic systems for vehicle detection and GPS for further enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SLAM methods are used to create digital maps from vehicle sensor observations, then digital maps can be generated for automated vehicles, but localization accuracy deteriorates in areas with sparse landmark coverage
Solution Approach 1:
The patent combines data from multiple vehicles (first vehicle and second vehicle) to create a more comprehensive digital map. By merging sensor data, landmark observations, and localization information from multiple sources, the system overcomes the limitation of sparse landmarks in any single vehicle's field of view, thereby improving localization accuracy without requiring dense landmark coverage in every area.
2Manufacturing precision
If multiple vehicles transmit sensor data to a server for fleet mapping, then digital map quality improves through data aggregation, but data transmission time and processing time increase
Solution Approach 1:
The patent performs preliminary data processing and filtering in the vehicles before transmission to the server. By pre-processing sensor data, selecting relevant landmarks, and preparing localization information in advance, the system reduces the amount of data that needs to be transmitted and processed centrally, thereby maintaining high digital map quality while reducing data transmission time and server processing load.
3Measurement precision
If landmark correlations are performed to optimize digital maps, then localization precision improves, but processing complexity and time increase
Solution Approach 1:
The patent applies landmark correlations and optimization locally to relevant data subsets rather than processing all data globally. By focusing computational resources on local landmark correlations in specific geographic areas or for specific vehicle contexts, the system achieves high localization precision while reducing overall processing complexity and computational burden.
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
This approach provides more data for improved digital map optimization, reducing the need for landmark correlations and processing time, resulting in enhanced localization accuracy and map quality, especially in areas with sparse landmark coverage.
Implementation Method 1
using various sensors like video, radar, or ultrasonic systems for vehicle detection
Implementation Method 2
using various sensors like video, radar, or ultrasonic systems for vehicle detection
Data Source
AI summary
A method for creating a digital map for an automated vehicle, including identifying a first vehicle by a second vehicle or the second vehicle by the first vehicle, the first vehicle and/or the second vehicle being identified as data-detecting members of a creation process of the digital map; ascertaining a distance of the first vehicle from the second vehicle and/or a distance of the second vehicle from the first vehicle; and transmitting defined data of the vehicles and the distance between the vehicles to a creation unit for creating the digital map.