UAV Video Road Slope Estimation Without High-Precision GPS
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Solution Overview
Problem
Existing methods for estimating road longitudinal slope are costly, require high precision GPS or expensive devices, and fail to accurately capture rapid slope changes due to reliance on CAN bus data or additional sensors.
Innovation Solution
A method using unmanned aerial vehicle (UAV) aerial photography video to extract road trajectory and vehicle distribution data, employing image processing and Bayesian networks to estimate slope values, reducing the need for manual intervention and specialized devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS elevation information is used to estimate road slope, then measurement precision is improved, but device complexity and cost increase due to high precision GPS requirements
Solution Approach 1:
The patent introduces an intermediary computational model that uses easily obtainable variables (vehicle speed, acceleration, engine parameters from CAN bus) to indirectly estimate road slope. Instead of directly measuring slope with complex high-precision GPS, the system uses the vehicle's dynamic response as an intermediary to infer slope conditions, thereby achieving accurate slope estimation without requiring complex measurement devices.
Solution Approach 2:
The patent replaces the mechanical/GPS-based direct measurement system with a computational model based on vehicle dynamics equations. The system substitutes physical measurement complexity with mathematical computation, using the vehicle's own operational data to calculate slope, thereby eliminating the need for high-precision GPS while maintaining measurement accuracy.
2Measurement precision
If additional sensors are added to estimate road slope, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent enables the vehicle's existing systems to serve the additional function of slope estimation. The vehicle's own sensors (speed sensors, acceleration sensors, engine management system) and control units are utilized to gather necessary data, eliminating the need for external dedicated slope sensors. The vehicle essentially measures its own operational state to infer environmental conditions.
Solution Approach 2:
The patent makes the existing vehicle sensors and control systems multi-functional. The same sensors that monitor vehicle speed, acceleration, and engine performance for normal operation are also used for road slope estimation, thereby achieving enhanced measurement capability without adding dedicated hardware for this specific function.
3Device complexity
If CAN bus data is used to estimate road slope, then device complexity is reduced, but measurement precision deteriorates due to limited precision and noise
Solution Approach 1:
The patent implements a feedback mechanism where the estimated slope information is continuously refined based on the vehicle's actual dynamic response. The system uses the vehicle's speed, acceleration, and engine parameters as feedback signals to adjust and improve the slope estimation accuracy, compensating for the limited precision of CAN bus data through iterative optimization.
Solution Approach 2:
The patent performs preliminary data processing and filtering on the CAN bus data before using it for slope estimation. By pre-processing the noisy sensor data through filtering and validation routines, the system prepares cleaner input data that improves the accuracy of subsequent slope calculations, thereby overcoming the inherent limitations of CAN bus precision.
4Measurement precision
If traditional trajectory acquisition methods are used, then measurement precision is improved, but loss of time increases due to manual labeling and deep learning processes
Solution Approach 1:
The patent extracts only the essential trajectory information needed for slope estimation directly from video frames using simple image processing techniques. Instead of performing comprehensive deep learning analysis or manual labeling of entire scenes, the system selectively extracts vehicle position and motion data from key features in the video, achieving necessary precision with significantly reduced processing time.
Solution Approach 2:
The patent applies partial action by focusing only on the specific trajectory elements required for slope calculation rather than processing complete scene understanding. The system extracts sufficient trajectory data from video without performing exhaustive analysis, obtaining the necessary precision for slope estimation while avoiding the time cost of complete deep learning processing.
Data Source
AI summary
A road longitudinal slope estimation method based on unmanned aerial vehicle aerial photography video includes: collecting and correcting the traffic flow video of the road; taking the center line of the road as the reference line, extracting pixels on the reference line, and outputting the pixel gray space-time image on the reference line; performing the contour extraction of pixel gray space-time image; through the trajectory contour information, obtaining the complete trajectory data set; identifying the vehicle speed change point by using the energy distribution of wavelet transform; constructing the data set of road slope estimation, and based on the assumption of the actual length distribution of road pixels and the value of road longitudinal slope, applying a Bayesian network and a machine learning algorithm to iteratively obtain an expected value and variance of the actual length of the road pixel point and an estimated value of the road longitudinal slope.