Driving Assistance Video Analysis for Moving Object Intention Prediction
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Solution Overview
Problem
Current vehicles can only recognize safety-related scenes as they happen, failing to predict the intentions of moving objects, such as overtaking trucks or cut-in vehicles, which limits their ability to anticipate and prevent potential hazards.
Innovation Solution
A method and apparatus that capture video frames using a camera, identify moving objects, extract features like velocity and distance, and predict motion intentions using pre-trained models to anticipate actions like overtaking or cut-in, allowing for proactive safety measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If vehicles use video capture devices to record driving scenes, then the amount of video data recorded increases, but the ability to efficiently analyze and interpret the video data for driving assistance remains insufficient
Solution Approach 1:
The system performs preliminary actions by extracting motion characteristics and predicting motion intentions of moving objects from video frames before actual driving hazards occur. This advance analysis enables the vehicle to anticipate potential risks and prepare appropriate responses, transforming passive video recording into proactive safety assistance.
Solution Approach 2:
The system extracts key motion characteristics (velocity, acceleration, distance, orientation) from video data and separates the prediction of motion intentions from the overall video analysis process. This extraction approach focuses computational resources on critical safety-related features rather than analyzing all video content equally.
2Device complexity
If vehicles only recognize safety-related scenes as they happen, then the complexity of the recognition system is reduced, but the ability to predict and prevent potential hazards is limited
Solution Approach 1:
The system performs preliminary prediction of motion intentions by analyzing current motion characteristics and extrapolating future behaviors of moving objects. This allows the vehicle to anticipate hazards before they materialize, transitioning from reactive recognition to proactive prediction while maintaining manageable system complexity through focused feature analysis.
Solution Approach 2:
The system introduces motion characteristics (velocity, acceleration, distance, orientation) as intermediary parameters between raw video data and hazard prediction outcomes. These intermediaries bridge the gap between simple video recognition and complex hazard prediction, enabling reliable hazard prevention through structured analysis.
3Measurement precision
If the system extracts multiple motion characteristics features from video frames, then the accuracy of motion intention prediction improves, but the computational processing requirements increase
Solution Approach 1:
The system extracts only the most critical motion characteristics (velocity, acceleration, distance, orientation) needed for motion intention prediction, rather than analyzing all possible video features. This selective extraction maintains high prediction accuracy while minimizing computational overhead by focusing on safety-relevant parameters.
Solution Approach 2:
The system applies different analysis depths to different aspects of video data, performing detailed motion characteristic extraction only on regions containing moving objects while using simpler methods for background analysis. This local quality approach optimizes computational resources by allocating processing power where it is most needed for safety prediction.
Data Source
AI summary
A method and apparatus for assisting driving include: identifying one or more set of video frames from captured video regarding surrounding condition of a vehicle, wherein the one or more set of video frames comprise a moving object; extracting one or more features indicating motion characteristics of the moving object from the one or more set of video frames; and predicting motion intention of the moving object in the one or more set of video frames based on the one or more features.