Vehicle Road Anomaly Detection from Front Vehicle Motion
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Solution Overview
Problem
Existing vehicle systems for detecting road anomalies are either expensive, bulky, and unreliable, as they rely on high-resolution cameras or GPS, which can fail to detect recent changes or provide false alarms, making it difficult for drivers to navigate safely, especially in heavy traffic or low-light conditions.
Innovation Solution
A system using sensors and a processor to generate images of the road ahead, detect a front moving vehicle, analyze its motion relative to an expected path, and determine road anomalies such as bumps, potholes, diversions, or inclinations, using cameras or LiDAR sensors and machine learning algorithms, and provide alerts through light, sound, or haptic indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution cameras are used to track and identify potholes, then detection accuracy is improved, but cost and device size increase
Solution Approach 1:
The system uses a standard camera to capture images of the road, then creates a digital copy or representation of the road surface through image processing. This digital copy is analyzed to detect anomalies like potholes, cracks, and debris, eliminating the need for specialized high-resolution cameras while maintaining detection accuracy.
Solution Approach 2:
The patent replaces the mechanical/optical approach of using high-resolution cameras with a computational approach using standard cameras combined with image processing algorithms. The detection is achieved through software analysis of image data rather than relying on hardware with superior optical resolution.
2Reliability
If additional sensors such as ultrasound sensors are mounted on the vehicle, then detection capability is improved, but cost and space requirements increase
Solution Approach 1:
The system uses a standard camera that serves multiple functions: capturing road surface images for anomaly detection, providing visual feedback to drivers, and potentially integrating with other vehicle systems. This multi-functional approach eliminates the need for dedicated ultrasound sensors or other specialized detection devices.
Solution Approach 2:
The camera system serves itself by using the same imaging device for both standard road monitoring and anomaly detection. The system processes the captured images through algorithms that automatically identify road surface irregularities, eliminating the need for separate detection sensors.
3Area of stationary object
If GPS is used to track road anomalies, then coverage area is improved, but reliability decreases due to false alarms
Solution Approach 1:
The system performs preliminary detection of road anomalies by capturing and analyzing images of the road surface in real-time before the vehicle reaches the anomaly. This allows the system to identify and classify potential hazards proactively, providing reliable advance warning to the driver without the false alarms associated with GPS-based systems.
Solution Approach 2:
The system provides immediate feedback to the driver through visual displays showing the detected road anomalies and their locations. This real-time feedback loop allows the driver to see the actual road conditions captured by the camera, verifying the authenticity of detected anomalies and eliminating false alarms.
Data Source
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AI summary
The present invention relates to a system (100) in a vehicle (250) for detecting road anomalies (260, 270, 280, 290). The system (100) includes one or more sensors (110), and a processor (120). The one or more sensors (110) are configured to generate one or more images (200) of a view in front of the vehicle (250). The processor (120) in communication with the one or more sensors (110). The processor (120) is configured to receive the one or more images (200) from the one or more sensors (110); detect a front moving vehicle (210) in the one or more images (200); analyse motion of the front moving vehicle (210) in comparison with an expected path (240) of the front moving vehicle (210); and determine a form of the road anomaly (260, 270, 280, 290) based on the analysed motion of the front moving vehicle (210).