Vehicle Collision Detection Using Bounding Box Rate Change
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Solution Overview
Problem
Existing systems lack effective methods for detecting vehicle collisions and near-miss situations in real-time, which are crucial for improving fleet safety and enabling immediate alerts and responses.
Innovation Solution
A machine-learning model is trained using a library of image data to detect collisions and near-misses by analyzing the rate of change of spatial parameters of bounding boxes, with loss functions adjusted to minimize errors, and deployed on vehicles to provide immediate alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring systems are used to track vehicle positioning, then basic location data can be obtained, but real-time collision detection capability is insufficient
Solution Approach 1:
The patent replaces traditional mechanical monitoring systems with machine learning models that process image data from cameras. The collision detection system uses neural networks to analyze spatial parameters and rates of change from visual data, substituting complex mechanical sensors and processors with software-based AI analysis that achieves higher accuracy without proportional increases in hardware complexity
Solution Approach 2:
The system detects collisions by monitoring changes in spatial parameters (position, size, orientation) of vehicles in image sequences and calculating their rates of change. By transforming the detection problem from direct collision sensing to analyzing parameter dynamics over time, the system achieves reliable real-time detection while maintaining manageable system complexity
2Speed
If real-time monitoring is implemented to improve safety response, then immediate alerts can be provided, but processing speed and accuracy requirements increase system demands
Solution Approach 1:
The system segments the collision detection task into distinct processing stages: extracting spatial parameters from individual images, calculating rates of change between consecutive frames, and applying machine learning classification. This segmentation allows each stage to be optimized independently, achieving both high processing speed for real-time response and high precision through specialized algorithms at each step
Solution Approach 2:
The system performs preliminary processing by pre-calculating spatial parameters and storing them for rapid comparison. By preparing and organizing data in advance (extracting position, size, orientation parameters before collision analysis), the system enables faster real-time detection without sacrificing precision, as the heavy computational work is distributed across preprocessing and real-time comparison stages
Data Source
AI summary
Systems, methods, models, and training for collision detection and near miss detection are discussed. Models are trained to identify collisions and near misses between two vehicles, based on how quickly a bounding box size changes for a vehicle represented in image data. Rapidly changing bounding box size is indicative of extreme proximity between vehicles, and thus indicative of collision or near-miss scenarios. Identifications of collisions or near misses can be reported to a remote operator, to send emergency assistance, perform driver evaluation, or alert other drivers in the area of risks.