Sensor Data Processing With Velocity Dimension for Object Detection
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Solution Overview
Problem
Existing driver assistance and autonomous driving systems face challenges in accurately detecting and recognizing environment objects, particularly those in need of protection, due to their non-rigid and varying velocity patterns, which current sensors and processing methods fail to adequately capture.
Innovation Solution
A method involving environmental sensors that provide multi-dimensional data structures incorporating spatial and velocity information, processed by trained neural networks and convolutional techniques, to enhance detection and recognition of environment objects, especially those with movable parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensor processing methods are used, then processing complexity is reduced, but detection precision of environment objects with non-rigid velocity patterns deteriorates
Solution Approach 1:
The patent embeds velocity information as a separate dimension in the data structure, creating a multi-dimensional representation that includes spatial coordinates and velocity components. This dimensional expansion allows the neural network to process velocity patterns independently, improving detection precision for objects with non-rigid motion without overwhelming the processing system.
Solution Approach 2:
The patent segments the detection task by separating spatial processing from velocity processing through distinct data dimensions. The neural network can then apply different processing strategies to spatial features and velocity features independently, managing complexity while improving overall detection accuracy for environment objects.
2Reliability
If velocity information is embedded in its own dimension in the data structure, then detection reliability of environment objects with movable parts improves, but data structure complexity increases
Solution Approach 1:
The patent adds a velocity dimension to the data structure, creating a multi-dimensional space where spatial information and velocity information are represented as separate but related dimensions. This allows the neural network to learn patterns in velocity independently while maintaining the overall structure needed for reliable detection of environment objects with movable parts.
Solution Approach 2:
The patent changes the parameter representation by introducing velocity as an additional parameter dimension. This transformation from traditional spatial-only representation to spatial-velocity representation improves detection reliability while the structured approach to organizing these parameters manages the complexity increase.
3Measurement precision
If multi-dimensional data structures with velocity dimension are used, then recognition accuracy of micro-Doppler signatures improves, but computational requirements increase
Solution Approach 1:
The patent segments the feature extraction process by organizing data into distinct spatial and velocity dimensions. This segmentation allows the neural network to process micro-Doppler signature patterns in the velocity dimension separately from spatial patterns, improving recognition accuracy while managing computational load through structured processing.
Solution Approach 2:
By embedding velocity information in its own dimension, the patent enables the neural network to specifically target and process micro-Doppler signatures without processing the entire sensor data cube. This dimensional separation improves recognition accuracy for subtle velocity patterns while reducing unnecessary computational requirements.
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 the detection and recognition of environment objects, particularly those in need of protection like pedestrians, bicycles, and animals, by embedding the spatial dimension and the velocity information in the data structure, allowing for more accurate and reliable detection of environment objects, and the environment objects, especially those with movable parts, such as pedestrians, bicycles, and animals, by embedding the velocity information in its own dimension in the data structure, thus improving their detection and recognition.
Implementation Method 1
sensor data contain at least spatial information obtained through reflections of the environment object
Implementation Method 2
velocity information obtained through Doppler shifting
Data Source
AI summary
A method for sensor data processing of sensor data of at least one environmental sensor, a method for environment object detection, a method for control of at least one operating parameter, and a processing device are described.