Camera-Based Vehicle Navigation with Landmark Ranking
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Solution Overview
Problem
Existing vehicle navigation systems rely on GPS and road sign recognition, which can be inaccurate due to obscured signs or incorrect distance estimation, leading to driver confusion and navigation errors.
Innovation Solution
A camera-based vehicle navigation system that uses a vehicle camera, GPS, and machine learning models to detect multiple objects in the image, rank them based on landmark scoring criteria, and display the highest ranked object on the user interface to guide the driver.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and road sign recognition are used for vehicle navigation, then the system can provide basic navigation guidance, but the accuracy is reduced due to obscured signs or incorrect distance estimation
Solution Approach 1:
The patent introduces visual landmarks detected by camera-based object detection as an intermediary reference system. Instead of relying solely on GPS coordinates and road signs, the system uses naturally occurring visual landmarks (buildings, trees, signs) captured by the vehicle camera to create visual guidance cues that supplement and verify GPS-based navigation instructions
Solution Approach 2:
The patent replaces the mechanical/GPS-based navigation system with a vision-based system using machine learning models. The object detection model processes camera images to identify and rank visual landmarks, substituting the traditional GPS-coordinate-matching approach with AI-based visual recognition and ranking to determine the most useful navigation cues
2Measurement precision
If multiple objects are detected and ranked using machine learning models, then the navigation guidance becomes more accurate and intuitive, but the system complexity increases
Solution Approach 1:
The patent performs preliminary object detection and ranking before generating navigation guidance. The machine learning model pre-processes the camera image to detect multiple objects, rank them by relevance to upcoming turns, and identify the most useful visual landmarks in advance, so that only processed and prioritized information is presented to the driver
Solution Approach 2:
The system uses the vehicle's existing camera infrastructure to capture images for navigation, making the navigation system self-sufficient. The machine learning model automatically detects and ranks objects without requiring external inputs, and the system integrates this visual information with GPS data autonomously to generate comprehensive navigation guidance
3Ease of operation
If visual landmarks are used to supplement GPS navigation, then driver compliance improves, but the processing time and computational resources increase
Solution Approach 1:
The patent detects and ranks multiple objects beyond what is strictly necessary, then selects only the top-ranked relevant landmarks for navigation guidance. This partial action approach processes more data than minimally required (detecting all objects rather than just navigation-relevant ones) but filters to use only the most useful subset, improving driver compliance while managing processing loads
Solution Approach 2:
The system extracts only the essential navigation-relevant information from the full object detection results. After detecting multiple objects and ranking them, the system extracts and displays only the top-ranked landmarks that are most useful for the upcoming turn, separating critical navigation cues from other detected objects that are less relevant
Data Source
AI summary
A vehicle control system for camera-based vehicle navigation includes at least one vehicle camera configured to capture an image of a front view from a vehicle, a global positioning system (GPS) receiver configured to obtain a current location of the vehicle, a vehicle user interface including a display, and a vehicle control module. The vehicle control module is configured to obtain the current location of the vehicle via the GPS receiver, identify a sequence of vehicle navigation steps to a target destination, capture the image via the at least one vehicle camera, process the image with a machine learning model to detect multiple objects in the image, rank the multiple objects according to landmark scoring criteria indicative of an object recognition likelihood by a driver of the vehicle, and display a highest ranked one of the multiple objects in association with a next vehicle navigation step.