Road Condition Classification Using Radar Channel Images
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Solution Overview
Problem
Existing techniques for classifying road conditions using sensor data in vehicles are not optimal, particularly in certain environments, leading to subpar performance in accurately determining road conditions such as wetness, dryness, or snow coverage.
Innovation Solution
A method and system that utilize ultra-short range radar sensors and a neural network model to generate road surface channel images, fuse them with vehicle speed data, and classify road conditions, enabling precise determination of road conditions and subsequent vehicle actions like automatic braking or steering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing sensor techniques are used for road condition classification, then the system is simple and easy to implement, but the classification accuracy is subpar particularly in certain environments
Solution Approach 1:
The patent combines multiple sensor types (ultra-short range radar sensors and other vehicle sensors) to create a fused sensor data system. This merging of different sensing modalities enables accurate road condition classification by compensating for the limitations of individual sensors, particularly in challenging environments where single-sensor approaches fail.
Solution Approach 2:
The patent transforms sensor data into image representations (road surface channel images) by organizing sensor readings into spatial and temporal dimensions. This dimensional transformation allows the application of image processing and neural network techniques to sensor data, significantly improving classification accuracy while providing a structured approach to handling complex multi-sensor inputs.
2Measurement precision
If multiple sensor data facets are processed to improve classification accuracy, then measurement precision improves, but data processing complexity increases
Solution Approach 1:
The patent organizes multiple sensor data facets (returned energy, Z coordinate, Doppler value, sensor index) into structured image channels, transforming complex multi-dimensional sensor data into a format that can be processed by neural networks. This dimensional organization simplifies the processing of multiple data facets by providing a unified computational framework.
Solution Approach 2:
The patent introduces road surface channel images as an intermediary representation between raw sensor data and neural network classification. These images serve as a mediator that structures and pre-processes sensor data, making it more suitable for neural network input and reducing the direct processing complexity of raw multi-facet sensor data.
3Measurement precision
If sensor data is transformed into multiple channel images to capture different properties, then classification accuracy improves, but computational requirements increase
Solution Approach 1:
The patent segments sensor data into multiple distinct channel images, each capturing a specific facet of road surface properties (returned energy channel, Z coordinate channel, Doppler value channel, sensor index channel). This segmentation allows the neural network to process different physical properties separately and independently, improving classification accuracy while enabling modular computational processing.
Solution Approach 2:
The patent transforms temporal sensor data streams into spatial image representations with multiple channels. This dimensional transformation enables the use of efficient image processing algorithms and neural network architectures that can handle multi-channel data more efficiently than raw temporal sequences, balancing computational requirements with improved classification capability.
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 system effectively classifies road conditions with high accuracy, enabling vehicles to respond appropriately to different surfaces, enhancing safety and operational efficiency by providing precise data on road conditions.
Implementation Method 1
obtaining first sensor data as to a surface of the road from one or more first sensors onboard the vehicle, wherein the first sensor data includes returned energy, a Z coordinate, a Doppler value, and a sensor index value
Implementation Method 2
the plurality of facets of properties of the first sensor data, as reflected in the surface channel images, include a Doppler value at an (x,y) position from the first sensors
Data Source
AI summary
Methods and systems are provided for controlling a vehicle action based on a condition of a road on which a vehicle is travelling, including: obtaining first sensor data as to a surface of the road from one or more first sensors onboard the vehicle; obtaining second sensor data from one or more second sensors onboard the vehicle as to a measured parameter pertaining to operation of the vehicle or conditions pertaining thereto; generating a plurality of road surface channel images from the first sensor data, wherein each road surface channel image captures one of a plurality of facets of properties of the first sensor data; classifying, via a processor using a neural network model, the condition of the road on which the vehicle is travelling, based on the measured parameter and the plurality of road surface channel images; and controlling a vehicle action based on the classification of the condition of the road.