Vehicle Radar Object Classification via Spectrogram Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing radar systems in vehicles face limitations in accurately classifying objects, such as vehicles and pedestrians, which can impact features like automatic braking and adaptive cruise control.
Innovation Solution
A method and system that utilize spectrogram data from radar signals to classify objects by aggregating data into a computer vision model, incorporating a transmitter, receiver, and processor to enhance object recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional radar signal processing methods are used, then the system is simple to operate, but object classification accuracy is insufficient
Solution Approach 1:
The patent replaces traditional radar signal processing methods with computer vision models and spectrogram analysis techniques. The radar return signals are transformed into spectrogram images and processed using convolutional neural networks, substituting conventional signal processing algorithms with advanced machine learning approaches to improve classification accuracy.
Solution Approach 2:
The patent transforms radar signal parameters into a different representation format by converting time-domain radar returns into frequency-domain spectrograms. This parameter transformation allows the application of image processing techniques to radar data, enabling more accurate object classification through visual pattern recognition.
2Measurement precision
If spectrogram data aggregation into computer vision models is implemented, then object classification accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary transformation of radar signals into spectrogram representations and pre-processes the data into image-like formats before classification. By preparing the data in advance in a suitable format for computer vision models, the system optimizes the classification process and reduces real-time processing 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
Improves the accuracy of object classification, enabling better vehicle control systems by effectively distinguishing between various road objects, thereby enhancing safety features.
Implementation Method 1
a transmitter, a receiver, and a processor. The transmitter is configured to transmit radar signals. The receiver is configured to receive return radar signals after the transmitted radar signals are deflected from an object proximate the vehicle
Data Source
AI summary
Methods and systems are provided for object classification for a radar system of a vehicle. The radar system includes a transmitter that transmits radar signals and a receiver that receives return radar signals after the transmitted radar signals are deflected from an object proximate the vehicle. A processor is coupled the receiver, and is configured to: obtain spectrogram data from a plurality of spectrograms pertaining to the object based on the received radar signals; aggregate the spectrogram data from each of the plurality of spectrograms into a computer vision model; and classify the object based on the aggregation of the spectrogram data from each of the plurality of spectrograms into the computer vision model.