Automated Crash Detection Using Video and IMU Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current collision detection systems in vehicles often generate false positives and false negatives, misinterpreting events like hard braking or minor triggers, which affects the accuracy of accident analysis and characterization.
Innovation Solution
A system comprising a camera and video processors with neural networks and machine learning models that capture and analyze video feeds to determine accident types by extracting motion features and spatial orientation of objects, combined with IMU sensor data analytics to improve anomaly detection and characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional telemetry and video recording systems are used to detect accidents, then the system can capture basic accident data, but the system generates false positives and false negatives due to inability to accurately distinguish actual accidents from normal driving events
Solution Approach 1:
The patent combines multiple data sources including video feeds from dash cameras, telemetry data from sensors (accelerometers, gyroscopes, GPS), and audio data from microphones into a unified analysis system. This multi-modal data fusion enables more accurate distinction between actual accidents and normal driving events by cross-validating information across different sensor types
Solution Approach 2:
The system implements feedback mechanisms where the analysis results from neural networks and machine learning models continuously refine the detection algorithms. The system learns from labeled accident data and adjusts its thresholds and parameters to reduce false positives and false negatives over time, improving both precision and reliability
2Productivity
If simple trigger-based event detection is used, then the system can quickly identify potential accidents, but the system misinterprets normal events like hard braking as accidents
Solution Approach 1:
The patent segments the event detection process into multiple stages: initial trigger detection using simple thresholds, followed by detailed analysis using neural networks and machine learning models. This multi-stage approach maintains quick initial response while improving final characterization accuracy through progressive refinement
Solution Approach 2:
The system introduces an intermediary analysis layer between simple trigger detection and final accident classification. Neural networks and machine learning models act as intermediaries that process the initial trigger signals along with additional sensor data to make more accurate determination of whether an event constitutes an actual accident
3Measurement precision
If detailed video analysis with neural networks is implemented, then the system can accurately characterize accident types, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary filtering and preprocessing of video data using traditional image processing techniques before applying complex neural network analysis. This preliminary action reduces the computational burden by eliminating obviously non-accident frames and preparing data in an optimized format for deep learning models
Solution Approach 2:
The patent implements dynamic processing where the level of analysis applied to video frames adjusts based on the detected event likelihood. Frames with high accident probability receive full neural network analysis, while frames with low probability undergo simpler processing, optimizing the balance between accuracy and computational resources
Data Source
AI summary
A deep learning based computer vision system to detect and analyses automobile crash videos comprises a crash detection module and a crash analysis module. The crash detection module uses dashcam video and or telemetry to detect the same. The usage of computer vision algorithm alongside the IMU sensor data make the system robust. The system uses a deep learning model which looks for an anomalous pattern in the video and/or the IMU signal to detect a crash. The system comprises different machine learning and deep learning based computer vision algorithm to analyze the crash detected videos. The system automates the labor-intensive task of reviewing crash videos and generates a crash report comprising accident type, road surface, weather condition and accident scenario among other things. The accident detection and analysis modules can reside on the vehicle as well as on the cloud based on the compute capabilities of the edge device.