Single-Frame Vehicle Collision Prediction Using Orientation Detection
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Solution Overview
Problem
Existing collision prediction methods require images from multiple frames, leading to delayed collision prediction when vehicles are traveling at high speeds, as they take time to obtain captured images.
Innovation Solution
A collision prediction device and method that detect vehicles from a single frame image, determining the likelihood of collision based on the presence and orientation of vehicles in the frame, allowing for immediate prediction and appropriate collision avoidance support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If captured images of multiple frames are used for collision prediction, then the accuracy of collision prediction is improved, but the time required for prediction increases
Solution Approach 1:
The invention extracts only the essential information needed for collision prediction from the captured images - specifically detecting the front, rear, and side portions of other vehicles in a single frame. By taking out only the critical detection elements rather than analyzing multiple complete frames, the system achieves collision prediction without the time penalty of multi-frame processing.
Solution Approach 2:
The detection unit is designed to preliminarily identify and classify other vehicles by their detectable portions (front, rear, side) in advance within a single frame. This preliminary action enables the determination unit to quickly assess collision likelihood without waiting for additional frames, thus reducing prediction time while maintaining accuracy.
2Loss of information
If captured images of multiple frames are obtained, then more information about other vehicles is available, but the response time for high-speed travel conditions deteriorates
Solution Approach 1:
The system extracts the necessary vehicle information (front, rear, side detection status) from a single frame rather than requiring multiple frames. This extraction approach provides sufficient information for collision assessment while enabling immediate response, thus maintaining information completeness without sacrificing response speed in high-speed conditions.
Solution Approach 2:
The invention changes the detection parameter from requiring temporal sequences (multiple frames) to spatial configuration analysis (single frame with front/rear/side detection). This parameter change allows the system to obtain complete vehicle orientation information instantly from one frame, maintaining information quality while dramatically improving response speed for high-speed travel.
3Loss of time
If only single frame images are used, then the prediction time is shortened, but the ability to detect vehicle orientation may be insufficient
Solution Approach 1:
The detection unit segments the vehicle detection task into three distinct portions: front detection, rear detection, and side detection. By dividing the orientation detection into these segments within a single frame, the system can accurately determine vehicle orientation without requiring multiple frames, thus maintaining precision while reducing prediction time.
Solution Approach 2:
Instead of using the temporal dimension (multiple frames over time) to detect vehicle orientation, the invention transitions to analyzing spatial dimensions within a single frame by detecting which portions (front, rear, side) are visible. This dimensional shift enables accurate orientation detection instantaneously without the time delay of sequential frame analysis.
Data Source
AI summary
To shorten a time required to predict collision with other vehicles, a collision prediction device includes: a detection unit configured to detect each of a front, a rear, and a side of other vehicles located around a vehicle from a captured image of one frame in which surroundings of the vehicle are imaged; and a determination unit configured to determine a likelihood of collision with the other vehicles based on a detection result of the detection unit. The determination unit determines that the likelihood of collision with the other vehicle of which only the front or only the rear is detected is high in the other vehicles of which one or more of the front, the rear, and the side are detected by the detection unit.