Camera-Based Rollover Prediction for Vehicles
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Solution Overview
Problem
Current solutions for preventing vehicle rollover are either passive or require vehicle-specific sensors, making them difficult to scale and integrate across different vehicles, especially in uneven terrain conditions.
Innovation Solution
A standalone system using cameras and a controller to analyze images of the vehicle and its surroundings, calculate the center of gravity, and determine the probability of rollover, alerting the operator or adjusting the vehicle's movements to prevent rollover without the need for vehicle-integrated sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated sensors are integrated into each vehicle to detect rollover scenarios, then measurement precision and reliability improve, but device complexity and ease of manufacture worsen
Solution Approach 1:
The system uses a universal camera-based platform that can be deployed across different vehicle types without vehicle-specific sensor integration. The same camera hardware and image processing algorithm serve multiple functions: detecting vehicle posture, estimating center of gravity position, and predicting rollover probability across diverse vehicle platforms.
Solution Approach 2:
The patent replaces mechanical/inertial sensors with an optical measurement system using cameras and image processing. Instead of using accelerometers, gyroscopes, or force sensors integrated into the vehicle structure, the system captures images and uses computer vision algorithms to infer vehicle state, substituting a mechanical sensing approach with an optical one.
2Measurement precision
If vehicle-specific sensors are used for each vehicle type, then measurement precision improves, but adaptability and ease of operation worsen
Solution Approach 1:
The system achieves universality by using a standardized camera platform that works across different vehicle types. The image processing algorithm adapts to different vehicles through parameter calibration (vehicle dimensions, camera position) without requiring hardware changes, enabling the same system to serve multiple vehicle platforms.
Solution Approach 2:
The system accommodates different vehicle types by changing software parameters rather than hardware configuration. Vehicle-specific parameters such as dimensions, center of gravity location, and camera mounting position are input as data, allowing the universal image processing system to accurately detect posture for each specific vehicle configuration.
3Ease of manufacture
If a standalone camera-based system is used without vehicle sensors, then ease of manufacture and adaptability improve, but measurement precision may worsen
Solution Approach 1:
The system replaces physical sensors with optical measurement and computational analysis. High-precision center of gravity location is achieved not through direct sensor measurement but through image processing that calculates position based on vehicle geometry and captured images, substituting mechanical sensing with computational geometry.
Solution Approach 2:
The system introduces image processing algorithms as an intermediary between the camera and the vehicle state measurement. The algorithms act as a mediator that transforms raw image data into precise measurements of vehicle posture and center of gravity position, enabling accurate measurement without direct physical contact or specialized sensors.
Data Source
AI summary
A system for preventing rolling-over of vehicles is disclosed: The system may include: at least one camera attached to a portion of the vehicle such that images capture by the camera include a portion of the vehicle and a portion of a surrounding area; a communication module; and a controller configured to: receive from the camera, via the communication module, at least one image; receive data related to the parameters of the vehicle; calculate a relative position between the vehicle and a ground based on the received at least one image; calculate a location of the vehicle's center of gravity based on the received at least one image and the data related to the parameters of the vehicle; and determine a probability of rolling-over the vehicle based on the calculated center of gravity and the relative position.