Camera-Based Route Misalignment Detection for Automated Vehicles
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Solution Overview
Problem
Current systems for detecting route misalignment in vehicles rely on additional hardware like laser light sources, are prone to human error, and may not function correctly during power outages, leading to safety risks due to misaligned tracks or guide lanes.
Innovation Solution
A method and system using cameras mounted on vehicles to capture images of the route while moving, comparing them to benchmark visual profiles to identify misalignments, and modifying vehicle operations accordingly without the need for additional hardware like laser light sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laser light sources and additional hardware are used to detect route misalignment, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential function of route detection from complex laser-based systems and implements it using only the vehicle's existing camera and image processing capabilities. By removing the laser light source and specialized detection hardware, the system achieves misalignment detection using standard imaging equipment already present in modern vehicles.
Solution Approach 2:
The patent makes the vehicle's existing camera serve multiple functions: it continues to provide standard video feed for operators while simultaneously enabling automated route misalignment detection. This multi-functionality eliminates the need for separate detection hardware, reducing device complexity while maintaining measurement capabilities.
2Device complexity
If manual inspection of video feed is used for collision avoidance, then device complexity is reduced, but reliability decreases due to human error
Solution Approach 1:
The patent implements automated feedback mechanisms where image processing algorithms continuously analyze video feed from the camera, automatically detect route misalignments and foreign objects, and provide real-time warnings to operators. This closed-loop feedback system eliminates human error in detection while maintaining system simplicity by using the existing camera infrastructure.
Solution Approach 2:
The patent replaces the mechanical human inspection process with automated image processing and computer vision algorithms. The system substitutes human visual analysis with computational methods that can reliably detect misalignments and obstacles without fatigue or error, while keeping the hardware simple by using only the existing camera.
3Measurement precision
If vehicles travel slowly over the route for marker examination, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary processing of the video feed through automated image processing algorithms that can detect misalignments and markers at high speeds. By preparing and analyzing images in real-time during normal vehicle operation, the system eliminates the need for slow, deliberate marker examination while maintaining detection accuracy through computational methods.
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 response to route misalignments, enhancing safety by allowing vehicles to adjust operations automatically or alert operators without additional hardware, ensuring continuous operation during power outages.
Implementation Method 1
obtaining one or more images of a segment of a route from a camera while a vehicle is moving along the route
Data Source
AI summary
A method includes obtaining one or more images of a segment of a route from a camera while a vehicle is moving along the route. The segment of the route includes one or more guide lanes. The method also includes comparing, with one or more computer processors, the one or more images of the segment of the route with a benchmark visual profile of the route based at least in part on an overlay of the one or more images onto the benchmark visual profile or an overlay of the benchmark visual profile onto the one or more images. The one or more processors identify a misaligned segment of the route based on one or more differences between the one or more images and the benchmark visual profile and respond to the identification of the misaligned segment of the route by modifying an operating parameter of the vehicle.