Autonomous Driving Map Generation Using Hidden Fixed Object Markers
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Solution Overview
Problem
Existing autonomous driving systems face challenges in accurately measuring vehicle position when external identifiers are hidden by structures, leading to positioning inaccuracies.
Innovation Solution
An autonomous driving map generation device that utilizes fixed and moving object-based LiDAR systems to generate high-precision maps, reflecting the relative positions of fixed and moving object markers, thereby improving positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external identifiers are used for position measurement, then positioning can be performed, but positioning accuracy deteriorates when external identifiers are hidden by structures
Solution Approach 1:
The patent introduces fixed objects (such as buildings or infrastructure elements) as intermediary reference points. Instead of directly measuring the vehicle's position using external identifiers that may be hidden, the system uses fixed objects that are reliably detectable to establish reference positions, then calculates vehicle position based on these intermediaries. This resolves the contradiction by providing a stable measurement basis that doesn't depend on potentially hidden external identifiers.
Solution Approach 2:
The patent transitions from two-dimensional external identifier recognition to three-dimensional spatial relationship analysis. By incorporating fixed objects with known spatial positions and using multi-point detection, the system creates a volumetric reference framework. This dimensional expansion allows the system to maintain positioning accuracy even when traditional external identifiers are obscured, as it can triangulate position through multiple fixed reference points in space.
2Stability of the object's composition
If only fixed object-based maps are generated, then map stability is improved, but positioning accuracy in dynamic environments deteriorates
Solution Approach 1:
The patent merges fixed object-based maps with moving object-based maps to create an integrated high-precision map. The fixed object-based component provides stable reference framework, while the moving object-based component (using LiDAR to detect fixed object markers and moving object markers) adds dynamic positioning accuracy. This combination resolves the contradiction by integrating both stability and precision in a unified mapping system.
Solution Approach 2:
The patent creates a multi-functional mapping system that serves multiple purposes: fixed objects provide stable geographic reference, while moving objects with LiDAR sensors provide real-time position verification and dynamic environment adaptation. This universal system handles both static map stability requirements and dynamic positioning accuracy requirements through a single integrated approach.
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
Enhances positioning accuracy by reflecting relative positions of hidden fixed and moving object markers in high-precision maps, enabling stable autonomous driving even in environments where external identifiers are obscured.
Implementation Method 1
a first moving object-based map generating part that recognizes a fixed object marker corresponding to the fixed object on the basis of a first moving object LiDAR provided in a first moving object
Data Source
AI summary
Disclosed is an autonomous driving map generation device, wherein the autonomous driving map generation device reflects, in generating a high-precision map for autonomous driving in a terminal, relative positions of a fixed object marker of a hidden fixed object and a moving object marker corresponding to a moving object in a high-precision map on the basis of a fixed position of the fixed object to improve positioning accuracy in an autonomous vehicle in the terminal.