Binocular Stereo Road Topography Detection for Real-Time Driving Warnings
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Solution Overview
Problem
There is a need for real-time monitoring of road topographic conditions, including fluctuations and their levels, to enhance the safety and comfort of automatic driving vehicles, which existing technologies fail to address effectively.
Innovation Solution
A topographic environment detection method and system using a binocular stereo camera that processes left-eye and right-eye images to generate a dense disparity map, converts image information into 3D point cloud data, fits a road surface model, and uses a trained semantic segmentation model to obtain and transmit topographic information to a vehicle assistant driving system for driving instructions or warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binocular stereo camera and semantic segmentation model are used for topographic detection, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the detection process into distinct modules: binocular stereo vision for 3D reconstruction, semantic segmentation model for road surface classification, and separate processing for different road conditions. This modular approach enables precise topographic detection while managing system complexity through functional decomposition
Solution Approach 2:
The patent uses an intermediary approach by introducing a semantic segmentation model as a mediator between the raw binocular stereo images and the final topographic analysis. This intermediary layer processes and interprets the visual data, enabling accurate road surface classification without requiring direct complex hardware modifications
2Reliability
If real-time topographic monitoring is implemented, then vehicle safety and comfort are improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training the semantic segmentation model with extensive road surface data before deployment. This pre-processing of training data and model preparation enables the system to perform real-time inference efficiently during actual vehicle operation, reducing processing time while maintaining high safety standards
Solution Approach 2:
The patent implements continuous monitoring and processing of topographic data through the binocular stereo camera and semantic segmentation system. The continuous flow of image capture, processing, and analysis ensures real-time safety monitoring without significant interruptions, maintaining steady vehicle operation and safety assessment
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 real-time detection and transmission of topographic information, improving the stability and comfort of vehicle operations by accurately determining road conditions and providing corresponding driving strategies.
Implementation Method 1
obtaining a left-eye image and a right-eye image about a same road scenario
Implementation Method 2
processing the left-eye image and the right-eye image to obtain a dense disparity map of the road scenario
Data Source
AI summary
The topographic environment detection method and a topographic environment detection system based on a binocular stereo camera, and an intelligent terminal are provided. The topographic environment detection method includes: obtaining a left-eye image and a right-eye image about a same road scenario, and processing the left-eye image and the right-eye image to obtain a dense disparity map of the road scenario; converting image information in a detection region into 3D point cloud information in a world coordinate system in accordance with the dense disparity map; fitting a road surface model in accordance with the 3D point cloud information; inputting an image in the detection region into a trained semantic segmentation model, and obtaining a segmentation result from the semantic segmentation model; and obtaining topographic information about the detection region in accordance with the segmentation result, and transmitting the topographic information to a vehicle assistant driving system.