Front Vehicle Blind-Spot Warning Using Turning Path Prediction
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Solution Overview
Problem
Traffic accidents involving motorcycles and bicycles are frequently caused by blind spots created by the radius difference between the inner wheels of large vehicles, as drivers of these vehicles are not adequately warned of the potential danger, despite the installation of surround view systems in large vehicles.
Innovation Solution
A prewarning system for blind spots that includes a photographing unit to capture real-time images, a computing unit to recognize target vehicle features and predict turning path information, and a warning unit to generate alerts based on the radius difference between inner wheels, using image recognition models like YOLOv4-Tiny to assist drivers in avoiding these blind spots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surround view systems are installed in large vehicles, then drivers can observe situations around the vehicle, but drivers still do not pay attention to the monitor and rely on subjective consciousness, so motorcycle drivers or bicycle drivers driving next to the large vehicles are not protected
Solution Approach 1:
Instead of relying on the large vehicle driver to pay attention to the monitor and take action, the system inverts the approach by actively monitoring the driver's behavior and automatically issuing warnings when dangerous situations are detected, shifting the initiative from the driver's voluntary attention to an automated protective mechanism
Solution Approach 2:
The system implements continuous feedback by monitoring the monitor's display status and the driver's attention level, then providing real-time warnings when the driver fails to pay attention to dangerous situations, creating a closed-loop safety mechanism that actively responds to driver behavior
2Reliability
If the photographing unit and computing unit continuously process real-time images to recognize target vehicles and compute radius difference, then blind spots can be detected, but the system complexity increases
Solution Approach 1:
The computing unit performs multiple functions including image recognition, turning path prediction, and radius difference computation, consolidating what could be separate complex subsystems into a single multi-functional processing unit that handles all detection and analysis tasks
Solution Approach 2:
The system pre-computes turning path information and radius difference ranges based on recognized target vehicle features before actual blind spot incidents occur, allowing the warning unit to provide immediate alerts without requiring complex real-time calculations during critical moments
Data Source
AI summary
A prewarning system for blind spots of a front vehicle comprises a photographing unit, a computing unit, and a warning unit. A photographing direction of the photographing unit is a traveling direction of a user. The photographing unit captures images of the front of the user to output a real-time image. The computing unit continuously receives the real-time image and executes program data of an image recognition model. The computing unit recognize multiple target features of a target vehicle in the real-time image through the image recognition model. The computing unit predicts a turning path information of the target vehicle according to the multiple target features and computes a range of radius difference between inner wheels according to the turning path information. The computing unit generates a warning signal according to the range of radius difference between inner wheels to control a warning unit, so that the warning unit issues a warning.