Real-Time Road Anomaly Detection Using Camera and Time of Flight
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Solution Overview
Problem
Conventional systems fail to navigate vehicles in real-time based on current road conditions, leading to potential damage and accidents due to undetected potholes, speed breakers, and other surface anomalies, especially in poor light conditions or during rains.
Innovation Solution
A method and system that utilize a camera and Time of Flight technique to detect edges, vanishing points, and surface anomalies, creating a digital elevation model processed by a Convolutional Neural Network to generate a digital elevation image for real-time road condition assessment and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional navigation systems are used, then basic route guidance is provided, but real-time road condition detection and vehicle path determination are not achieved
Solution Approach 1:
The system segments the road surface into multiple grids, with each grid independently analyzed for elevation and anomalies. This segmentation allows parallel processing of different road sections, improving detection accuracy while managing computational complexity through distributed analysis.
Solution Approach 2:
The system transitions from 2D image data to 3D spatial understanding by creating digital elevation models and assigning elevation values to each grid. This dimensional transformation enables detection of surface anomalies like potholes and speed breakers that are invisible in standard 2D images.
2Measurement precision
If real-time road condition analysis is implemented, then accurate vehicle path determination is achieved, but computational processing time and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images to detect edges and vanishing points before full analysis. Road perspectivity is corrected in advance, and grids are pre-segmented, allowing the main anomaly detection to proceed faster with prepared data structures.
Solution Approach 2:
The system replaces traditional mechanical or manual road inspection methods with automated image processing and machine learning algorithms. Convolutional neural networks automatically detect surface anomalies, eliminating the need for physical measurement devices or human inspectors.
3Loss of information
If digital elevation modeling with grid-based analysis is used, then comprehensive road condition mapping is achieved, but data processing complexity and computational resources increase
Solution Approach 1:
The road surface is divided into a grid system where each cell represents a discrete analysis unit. This segmentation transforms continuous road surface data into discrete, manageable units that can be processed independently, reducing overall computational complexity while maintaining comprehensive coverage.
Solution Approach 2:
The system applies partial analysis by focusing computational resources on grids where anomalies are detected, rather than uniformly processing all grids at maximum detail. This selective approach reduces unnecessary computation in normal road sections while maintaining detection sensitivity in problematic areas.
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 vehicles to determine optimal paths and speeds in real-time, reducing the risk of accidents and damage by providing accurate road condition data for both human drivers and autonomous vehicles.
Implementation Method 1
determine surface anomalies in the road based on a set of parameters, based on the second dataset and a Time of Flight technique (ToF)
Data Source
AI summary
A method and system for navigating vehicles based on road conditions determined in real-time is disclosed. The method includes the steps of receiving a first dataset including an image of a section of a road within a Field of View (FOV) of a camera attached to a vehicle and a second dataset associated with the road. The method further includes detecting edges and a vanishing point in the image, correcting road perspectivity in the image, and determining surface anomalies in the road based on a set of parameters, the second dataset and a Time of Flight technique (ToF), creating a digital elevation model for the image, and assigning a value, in real-time, from a predefined value range to each of a plurality of grids in the image based on a digital elevation model to generate a digital elevation image. The set of parameters includes volume associated with the surface anomalies.