Object Detection Device Road Surface Reflection Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object detection systems face accuracy issues in estimating absorption and attenuation values for ultrasonic waves due to unknown temperature and humidity conditions, leading to low accuracy in correcting CFAR signals for noise reduction.
Innovation Solution
An object detection device with a transmitter, receiver, CFAR processor, and estimator that uses a road surface reflection estimation expression to accurately estimate absorption and attenuation values, enabling high-accuracy CFAR processing and distance detection by accounting for environmental conditions through a road surface reflection estimation expression and distance characteristic expression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If humidity outside the vehicle interior is estimated based on humidity inside the vehicle interior to correct absorption and attenuation, then the correction can be performed without an external humidity sensor, but the accuracy of absorption and attenuation correction is low
Solution Approach 1:
The patent introduces road surface reflection as an intermediary element to indirectly obtain absorption and attenuation information. By measuring the reflection characteristics of the road surface (which has known and stable acoustic properties), the system can derive the absorption and attenuation values of the medium between the sensor and road surface, without directly measuring external humidity or temperature.
Solution Approach 2:
The system uses its own transmission wave and the known properties of the road surface to self-determine the absorption and attenuation characteristics of the environment. The road surface acts as a reference target with predictable reflection characteristics, allowing the system to calibrate itself without requiring external environmental sensors.
2Adaptability or versatility
If absorption and attenuation correction is performed without knowing temperature and humidity of the target environment, then the system can operate in unknown environments, but the accuracy of CFAR signal threshold setting is reduced
Solution Approach 1:
The system establishes a feedback loop where the measured road surface reflection signal is used to continuously update and correct the absorption and attenuation values. The estimator module processes the reflection signal and feeds back corrected parameters to the CFAR processor, enabling adaptive threshold adjustment based on actual environmental conditions rather than fixed or estimated values.
Solution Approach 2:
The patent dynamically changes the absorption and attenuation parameters based on the measured road surface reflection characteristics. Instead of using fixed parameters or parameters estimated from internal vehicle conditions, the system continuously adjusts these parameters according to the actual external environment as revealed by the road surface reflection measurements.
3Object-generated harmful factors
If CFAR processing uses moving average of signal levels to remove clutter, then noise reduction is achieved, but accuracy is affected by inaccurate absorption and attenuation values
Solution Approach 1:
The system performs preliminary correction of the processing target signal by applying absorption and attenuation compensation before executing CFAR processing. By pre-correcting the signal using the estimated absorption and attenuation values (derived from road surface reflection), the subsequent CFAR clutter removal operates on already-corrected data, improving the overall accuracy of object detection.
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 solution allows for precise estimation and correction of absorption and attenuation values, enhancing the accuracy of CFAR signal threshold setting and distance measurement, even in environments with unknown temperature and humidity, thereby improving the reliability of object detection systems.
Implementation Method 1
a transmitter configured to transmit a transmission wave to an outside including a road surface
Implementation Method 2
a receiver configured to receive a reflected wave of the transmission wave being reflected by an object as a reception wave
Implementation Method 3
CFAR processing is known as processing for reducing noise, called clutter, which is generated due to a reflection by an object that is not a detection target
Implementation Method 4
transmission and reception waves (a transmission wave and a reception wave) are absorbed and attenuated due to a temperature and humidity of a medium through which the transmission wave and the reception wave propagate
Data Source
AI summary
An object detection device includes: a transmitter transmitting a transmission wave to an outside including a road surface; a receiver receiving a reflected wave of the transmission wave being reflected by an object as a reception wave; a CFAR processor acquiring a CFAR signal at a predetermined detection timing by CFAR processing based on a value of a first processing target signal based on a reception wave received at the detection timing and an average value of values of second processing target signals based on the reception waves received in predetermined sections before and after the detection timing; and an estimator estimating an absorption and attenuation value corresponding to the average value based on a road surface reflection estimation expression that defines a relation between the average value and the absorption and attenuation value in advance.