Vehicle Ultrasonic Object Recognition Using Echo Classification
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Solution Overview
Problem
Existing ultrasonic sensors in vehicles primarily used for distance measurement lack the ability to accurately classify objects in the vicinity, limiting their application in driver assistance and autonomous driving systems.
Innovation Solution
Implementing a method that utilizes ultrasonic sensors to generate classification data, which is fed into a trained classifier to identify specific object classes such as pedestrians, vehicles, and obstacles, enhancing the sensor's semantic classification capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ultrasonic sensors are used for distance measurement only, then the device complexity is low and the sensor structure remains simple, but the object recognition capability and semantic classification are insufficient
Solution Approach 1:
The ultrasonic sensor system is extended to perform multiple functions: traditional distance measurement plus object classification. The sensor array evaluates echo characteristics (amplitude, frequency, temporal patterns) to distinguish between objects and noise, enabling the same hardware to serve both ranging and recognition purposes without adding separate sensing devices.
Solution Approach 2:
A classification device acts as an intermediary between the ultrasonic sensor and the control device. This intermediate component processes the raw sensor data, extracts features, and provides semantic information about detected objects, enabling higher-level recognition capabilities while keeping the sensor hardware itself relatively simple.
2Measurement precision
If classification data is processed through a trained classifier, then the object recognition accuracy is improved, but the processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary processing of the sensor signals by analyzing echo characteristics (amplitude, frequency content, temporal patterns) before feeding the data to the classifier. This pre-processing extracts meaningful features and reduces the dimensionality of the input data, making the subsequent classification faster and more efficient while maintaining high accuracy.
Solution Approach 2:
The system applies classification only when necessary - specifically when objects are detected in the near region. The sensor array continuously monitors the environment, and only triggers full classification processing when potential objects are identified, avoiding unnecessary computational resources during empty periods while ensuring rapid response when objects appear.
3Reliability
If the sensor array evaluates multiple echo characteristics, then the distinction between objects and noise is improved, but the sensor data processing complexity increases
Solution Approach 1:
The signal processing is divided into distinct segments: first evaluating echo characteristics (amplitude, frequency, temporal patterns), then making a binary decision (object vs. noise), and finally providing semantic information if an object is detected. This segmentation allows the system to handle complex evaluation logic in an organized manner, improving reliability while managing processing complexity through structured approach.
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
Enables precise and reliable object recognition, allowing for improved reaction times and expanded applications in driver assistance and autonomous driving by distinguishing between various objects and noise sources.
Implementation Method 1
the vehicle comprises an ultrasonic sensor for monitoring the surroundings of the vehicle for transmitting and receiving ultrasonic signals
Implementation Method 2
These have a high sensitivity, extending even to the range of a few centimeters, and thus enable a seamless acquisition of the region close to the vehicle
Data Source
AI summary
A method recognizes an object in a surroundings of a vehicle. The vehicle has an ultrasonic sensor for monitoring the surroundings of the vehicle and for the transmission and reception of ultrasonic signals. In the method, sensor data generated by the ultrasonic sensor are received. Classification data that contain features to be classified, are subsequently generated from the sensor data. The classification data and/or the sensor data are hereupon entered into a classifier that has been trained with training data for assigning the classification data and/or the sensor data to object classes. Object information that indicates at least one of the object classes is output.