Vehicle Accident Detection Using Multi-Stage Sensor and Video Analysis
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Solution Overview
Problem
Current techniques do not fully utilize vehicle sensor data and video data for effective vehicle accident detection, leading to wastage of computing resources and inefficiencies in investigating accidents for liability purposes.
Innovation Solution
A multi-stage approach using rule-based, anomaly detection, decision, and machine learning models to filter out false positives and accurately identify vehicle accidents based on sensor and video data, conserving resources by performing processor-intensive functions only when a threshold probability of an accident is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current techniques are used for vehicle accident detection, then the detection process is simple, but computing resources are wasted and detection accuracy is insufficient
Solution Approach 1:
The accident detection system is divided into multiple processing stages: initial sensor data filtering, intermediate analysis with multiple models, and final determination. Each stage processes only relevant data and triggers the next stage only when specific conditions are met, preventing unnecessary processing of normal driving events while maintaining high detection accuracy for actual accidents.
Solution Approach 2:
The system performs preliminary filtering of sensor data using rule-based detectors before applying more computationally intensive machine learning models. This preliminary action eliminates obvious false positives early in the process, so that resource-intensive processing is applied only to events that warrant further investigation, reducing overall computing resource consumption.
2Measurement precision
If processor-intensive functions are performed continuously, then accident detection accuracy is high, but computing resources are consumed unnecessarily
Solution Approach 1:
The system dynamically adjusts the level of processing applied to each event based on initial sensor data characteristics. For normal driving events, only lightweight rule-based filtering is applied. For events that exhibit accident-like characteristics, the system dynamically activates more intensive processing stages including multiple detector models and machine learning classifiers, optimizing resource usage based on actual event characteristics.
Solution Approach 2:
The system replaces continuous mechanical processing (continuous intensive computation) with a conditional processing approach. Instead of always applying full processing power, the system substitutes continuous computation with event-triggered computation, where intensive processing is activated only when sensor data patterns suggest a potential accident, thereby improving computing resource efficiency while maintaining detection precision.
3Loss of information
If manual investigation of accidents is performed, then detailed analysis is possible, but time and resources are wasted
Solution Approach 1:
The system performs self-service by automatically detecting, classifying, and filtering accident events using multiple detector models and machine learning algorithms. The system autonomously determines which events warrant further investigation and prepares preliminary analysis results, eliminating the need for manual review of every recorded event and significantly reducing investigation time while maintaining thorough analysis for actual accidents.
Solution Approach 2:
The system implements feedback mechanisms where detection results from earlier stages inform subsequent processing. Events that pass initial filtering are fed into intermediate analysis stages, and only those meeting specific criteria are passed to final determination stages. This feedback-based filtering reduces the volume of events requiring manual or intensive processing, improving investigation efficiency without losing critical information.
Data Source
AI summary
A device may receive sensor data and video data associated with a vehicle, and may process the sensor data, with a rule-based detector model, to determine whether a probability of a vehicle accident satisfies a first threshold. The device may preprocess acceleration data of the sensor data to generate calibrated acceleration data, and may process the calibrated acceleration data, with an anomaly detector model, to determine whether the calibrated acceleration data includes anomalies. The device may filter the sensor data to generate filtered sensor data, and may process the filtered sensor data and anomaly data, with a decision model, to determine whether the probability of the vehicle accident satisfies a second threshold. The device may process the filtered sensor data, the anomaly data, and the video data, with a machine learning model, to determine whether the vehicle accident has occurred, and may perform one or more actions.