Self-Generated Map Positioning for Low-GNSS Autonomous Driving
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Solution Overview
Problem
Existing self-driving systems face challenges in accurately estimating subject-vehicle positioning attitudes on high-precision maps, particularly in environments where GNSS signals are weak, and rely on expensive receivers or preceding vehicles for accurate navigation.
Innovation Solution
A self-position estimation device that includes a feature detection portion, a feature matching portion, a self-position estimation portion, a low-precision section detection portion, and a self-map generation portion, which uses sensors to estimate and generate a self-generated map with lane centerlines, stop lines, and traffic rules without relying on expensive GNSS receivers or preceding vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS receivers are used to estimate subject-vehicle positioning attitudes, then positioning accuracy is improved, but device cost increases
Solution Approach 1:
The patent creates a virtual copy of the physical environment by generating a three-dimensional map from sensor data (camera, LiDAR, radar). This digital twin or self-generated map serves as a substitute for expensive high-precision GNSS receivers, enabling accurate positioning through map matching and feature recognition rather than direct satellite signal reception.
Solution Approach 2:
The patent replaces the mechanical/electronic GNSS receiver system with a sensor-based perception and mapping system. Instead of relying on satellite signals and specialized receivers, the system uses standard sensors (camera, LiDAR, radar) combined with processing algorithms to achieve positioning, thereby reducing hardware costs while maintaining or improving accuracy.
2Measurement precision
If preceding vehicles are used to generate environmental maps, then positioning accuracy is improved, but system complexity increases
Solution Approach 1:
The patent enables each vehicle to independently generate its own environmental map using its onboard sensors (camera, LiDAR, radar). This self-generated map is created through autonomous feature detection, three-dimensional reconstruction, and map building processes within the individual vehicle, eliminating the need for complex inter-vehicle coordination and data sharing infrastructure.
Solution Approach 2:
The patent divides the mapping task into independent segments that can be performed by each vehicle individually. Rather than requiring a centralized system or coordinated fleet operation, each vehicle independently detects features, constructs three-dimensional models, and generates its own environmental map, simplifying the overall system architecture.
3Device complexity
If standard GNSS receivers are used, then device cost is reduced, but positioning accuracy deteriorates in shielded environments
Solution Approach 1:
The patent introduces a self-generated environmental map as an intermediary between the standard GNSS receiver and the positioning objective. The map serves as a reference framework that enables accurate positioning through map matching and feature recognition, compensating for GNSS signal degradation in shielded environments while using only standard, low-cost receivers.
Solution Approach 2:
The patent performs preliminary environmental mapping and feature detection before positioning is required. By pre-building a detailed three-dimensional map of the environment with recognizable features, the system prepares positioning reference data in advance, enabling accurate position estimation even when GNSS signals are unavailable or degraded.
Data Source
AI summary
An object is to provide a self-position estimation device capable of highly accurately estimating self-location and attitudes on a map containing information such as lane centerlines, stop lines, and traffic rules. A representative self-position estimation device according to the present invention includes a self-position estimation portion that estimates a self-location and attitude on a high-precision map from measurement results of a sensor to measure objects around a vehicle; a low-precision section detection portion that detects a low-precision section indicating low estimation accuracy based on the self-location and attitude estimated by the self-position estimation portion; and a self-map generation portion that generates a self-generated map saving a position and type of the object on the high-precision map in the low-precision section detected by the low-precision section detection portion.