Obstacle Classification Using Spatial Coordinate Transformation
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Solution Overview
Problem
Existing adaptive cruise control systems using radar or infrared sensors struggle to accurately identify and classify objects with significant elevation angles relative to the vehicle, leading to erroneous speed estimation and potential unwanted braking interventions, especially when encountering bridges, road sign gantries, or tunnels.
Innovation Solution
The method involves transforming detected measurement values from a vehicle-fixed coordinate system to a spatial coordinate system, subdividing the detection area into overlapping partial areas, and analyzing the number and statistical dispersion of echo signals to distinguish between relevant and non-relevant objects based on characteristic patterns, thereby improving object classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adaptive cruise control systems use radar or infrared sensors to detect objects, then the system can maintain speed and trigger emergency brakes, but objects with large elevation angles (bridges, road signs, tunnels) are erroneously classified as relevant moving objects leading to unwanted braking interventions
Solution Approach 1:
The detection area is subdivided into multiple partial areas, and the number and statistical dispersion of measurement values are determined for each partial area. This segmentation allows the system to analyze spatial distributions of echo signals more precisely, distinguishing between objects on the roadway plane and objects with elevation angles, thereby improving classification accuracy and preventing erroneous braking interventions.
2Device complexity
If the system assumes objects are situated in a plane parallel to or in the plane of the roadway, then distance and speed estimation is simplified, but objects with significant elevation angles cannot be properly identified or classified
Solution Approach 1:
The patent transforms measurement values from a vehicle-fixed coordinate system to a spatial coordinate system, enabling the system to detect and analyze the elevation dimension. By examining the spatial distribution of measurement values across multiple partial areas and comparing statistical dispersions, the system can identify objects with significant elevation angles without overly complicating the overall detection process.
3Measurement precision
If the system transforms measurement values to spatial coordinates and analyzes statistical dispersion across partial areas, then object classification accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The detection area is divided into multiple partial areas, allowing the system to process and analyze measurement values in smaller, manageable segments. By determining the number and statistical dispersion of measurement values for each partial area and comparing these with characteristic patterns, the system achieves improved object classification accuracy while keeping computational complexity manageable through structured data organization.
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 enhances the accuracy of object identification and classification, preventing unnecessary braking interventions by effectively differentiating between moving vehicles and stationary structures like bridges or tunnels, thereby improving traffic safety.
Implementation Method 1
The sensor apparatus usually contains an infrared laser or a millimeter radar which scans the region in front of the vehicle
Implementation Method 2
radiation is emitted by the sensor apparatus and the echo radiation reflected at objects is received as measurement values
Data Source
AI summary
A method is described for identifying and classifying objects, detected by a sensor apparatus which actively emits radiation, in terms of the relevance thereof to a driving situation of a moving vehicle, wherein radiation is emitted by the sensor apparatus and the echo radiation reflected at objects is received as measurement values, including: detecting measurement values in relation to the driving situation of the vehicle, performing an analysis of the driving situation represented by the measurement values and identifying at least one possible object, classifying the at least one identified object in an object class of a plurality of object classes, in which performing the analysis of the measurement values includes: transforming the detected measurement values from a coordinate system fixed in terms of the vehicle into a coordinate system fixed in terms of space for generating measurement values fixed in terms of spatial coordinates, wherein this transformation is based on the vehicle speed and the yaw rate of the vehicle in the determined driving situation, subdividing at least one total area, which is situated in the detection region of the sensor apparatus emitting radiation and which is coplanar or parallel with the roadway surface, into a plurality of partial areas, wherein partial areas adjoining one another partly overlap, determining the number and/or the statistical dispersion of the detected measurement values fixed in terms of spatial coordinates for each one of these partial areas, and in which performing the analysis of the driving situation represented by the measurement values and identifying at least one possible object includes: comparing the number and/or and statistical dispersion of the detected measurement values fixed in terms of spatial coordinates for each one of these partial areas, in each case with characteristic patterns, and identifying at least one object possibly present in a partial area and classifying the at least one identified object in an object class of a plurality of object classes depending on this comparison.