Vehicle Localization Using Radar Impulse Classification
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Solution Overview
Problem
Current vehicle localization methods, such as GPS and camera-based systems, are inaccurate and unreliable, especially in changing environments and low-visibility conditions, failing to meet the demands of autonomous navigation and assistance systems.
Innovation Solution
The method employs radar measurements to identify object positions and properties, creating an environment map that is continuously updated and compared to a reference map, using classification to improve localization accuracy and independence from external data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS or camera-based systems are used for vehicle localization, then the system can operate with simple infrastructure, but the localization accuracy deteriorates and reliability decreases in changing environments and low-visibility conditions
Solution Approach 1:
The patent combines radar measurements with classification methods to create a hybrid localization system. Radar provides robust object detection in various weather conditions while classification adds semantic understanding of objects, together achieving both high accuracy and reliability that neither method could achieve alone.
Solution Approach 2:
The system changes the parameter of measurement type from optical (camera) or satellite-based (GPS) to electromagnetic wave-based radar measurement. This parameter change enables reliable operation in low-visibility conditions and changing environments where optical and satellite systems fail.
2Reliability
If radar measurements are used to identify object positions, then measurement reliability improves in various weather conditions, but the complexity of the measurement and classification system increases
Solution Approach 1:
The system segments the complex task into distinct modules: radar signal transmission, impulse response reception, object position identification, and classification. This segmentation manages complexity by organizing functions into separate, manageable components that can be processed independently.
Solution Approach 2:
The patent introduces classification as an intermediary step between raw radar measurements and final localization results. This intermediary process filters and interprets radar data, managing the complexity of raw signal processing while enhancing the reliability of object identification.
3Measurement precision
If classification of object positions is performed to improve localization accuracy, then the precision of vehicle positioning improves, but the processing time and computational complexity increase
Solution Approach 1:
The system performs classification selectively on critical objects that most impact localization accuracy, rather than classifying all detected objects. This partial action approach maintains high positioning precision while reducing unnecessary processing time spent on less relevant objects.
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 accurate vehicle localization, enhances map resolution, and operates autonomously, even in challenging weather conditions, by integrating radar impulse responses and classification to refine object feature identification and vehicle positioning.
Implementation Method 1
Performance of radar measurements at successive points of a vehicle trajectory, wherein when performing radar measurement, in each case a radar impulse is emitted and subsequently a radar impulse response is measured angle-resolved and time-resolved
Implementation Method 2
a radar impulse is emitted and subsequently a radar impulse response is measured
Data Source
AI summary
A method and a device for locating a vehicle from a fixed reference map in which objects are assigned one or more positions in the reference map. At successive points of a vehicle trajectory, in each case a radar impulse is emitted and subsequently, angle-resolved and time-resolved measurements of the radar impulse response are performed. Object positions are identified in the environment surrounding the vehicle from the radar impulse response, the current identified object positions forming an environment map. The vehicle position is identified in the reference map by comparing the environment map to the reference map, the reference map being created from the identified positions and is continuously updated. An object classification for the identified object positions in the current environment map and/or reference map is performed and the identification of the vehicle position is performed while taking the object classification into account.