Vehicle Positioning by Radar Map Matching in Sensor Space
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Solution Overview
Problem
Existing vehicle positioning and motion data determination methods, such as GPS and radar systems, face challenges in accuracy and reliability, especially in areas with weak or absent satellite signals and in autonomous driving applications, due to noise and the need for significant processing resources.
Innovation Solution
A computer-implemented method that directly matches radar sensor data samples with transformed map elements in a sensor data representation, reducing processing effort by avoiding the conversion to full spatial representations, and using influence parameters to control the matching process, thereby improving accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS is used for vehicle positioning, then position data can be determined with fair degree of accuracy, but the method fails in areas with weak or absent satellite signals such as road tunnels, buildings, and subterranean garages
Solution Approach 1:
The patent combines GPS positioning with map data matching and sensor data processing to create a hybrid positioning system. When GPS signals are available, the system uses GPS data; when signals are weak or absent, it transitions to matching sensor scans with predefined map data, ensuring continuous positioning capability across all environments.
Solution Approach 2:
The patent introduces map data as an intermediary between the vehicle and the positioning system. By storing predefined map data representing the vicinity and matching it with sensor scans, the system creates an intermediate reference framework that enables positioning without direct satellite signal dependency.
2Measurement precision
If radar sensors are used for determining position and motion data, then measurement capability is improved, but significant processing resources are required particularly for determining radar detection points from raw sensor data
Solution Approach 1:
The patent performs preliminary actions by preprocessing radar sensor data into a simplified representation format before matching. Instead of processing complete raw sensor data to determine all detection points, the system preprocesses data to extract essential features (range and Doppler information) that are sufficient for map matching, reducing subsequent processing complexity.
Solution Approach 2:
The patent extracts only the necessary components from raw sensor data for the specific purpose of map matching. By taking out only the essential range and velocity information needed for comparing with map data, the system avoids the complex processing required for full radar detection point determination while maintaining sufficient measurement precision for positioning.
3Loss of information
If complete raw sensor data is processed to determine radar detection points, then full spatial representation is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent applies partial action by processing only the portion of sensor data that is necessary for map matching purposes. Instead of converting all sensor data to full spatial representations, the system processes data to the extent needed for comparing range and Doppler characteristics with map data, achieving sufficient spatial information without excessive processing time.
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
The method enables accurate and reliable determination of vehicle position and motion data with reduced processing overhead, even in complex driving conditions, by leveraging ground-truth data and influence parameters to enhance matching precision.
Implementation Method 1
Vehicles known from the art are capable of determining their current position on the basis of at least one sensor mounted on the vehicle. Modern vehicles, for example upper-class cars, are equipped with radar and/or LiDAR (light detection and ranging) systems.
Implementation Method 2
The raw sensor data is usually given as sensor data samples with a radial distance component and a rate of change of the distance (velocity in the radial distance direction). Such sensor data can be denoted as Doppler-sensor data or range-Doppler data delivered by a Doppler sensor.
Implementation Method 3
Modern vehicles, for example upper-class cars, are equipped with radar and/or LiDAR (light detection and ranging) systems.
Data Source
AI summary
A computer-implemented method for determining a position of a vehicle is disclosed, wherein the vehicle is equipped with a sensor for capturing scans of a vicinity of the vehicle, wherein the method comprises at least the following steps carried out by computer-hardware components: capturing at least one scan by means of the sensor with a plurality of sensor data samples given in a sensor data representation; determining, from a database, a predefined map with at least one element is given in a map data representation; determining a transformed map by transforming the at least one element of the predefined map from the map data representation into the sensor data representation; matching at least a subset of the sensor data samples of the at least one scan and the at least one element of the transformed map; and determining the position of the vehicle based on the matching.