Automated Incident Detection Using Sensor Fusion and Machine Learning
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Solution Overview
Problem
Conventional incident detection and reporting in vehicles are largely manual processes that require human intervention and are insufficient for real-time detection and reporting of external incidents, such as traffic stops or accidents, especially when the vehicle is not directly involved or when occupants are unaware of the incident.
Innovation Solution
A computer-implemented method and system that uses sensors like cameras and microphones to automatically detect incidents, initiate recording of audio and video data, overlay relevant information, and take control actions, such as alerting operators or third parties, using machine learning models like federated learning to determine incident occurrence and trigger responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If manual incident detection and reporting processes are used, then system complexity is reduced, but detection speed and real-time response capability deteriorate
Solution Approach 1:
The system enables automated incident detection and reporting through self-service mechanisms. Sensors automatically detect incidents, the processing system analyzes sensor data to determine incident occurrence, and the system generates and transmits reports without human intervention. This automation resolves the contradiction by achieving fast real-time detection while maintaining manageable system complexity through integrated automated workflows.
Solution Approach 2:
The patent replaces manual mechanical reporting processes with automated electronic systems. Instead of occupants manually detecting and reporting incidents, the system uses sensors, machine learning models, and automated communication protocols to detect incidents and generate reports. This substitution achieves rapid automated detection while keeping system complexity controlled through standardized electronic architectures.
2Extent of automation
If automated sensor-based incident detection is implemented, then real-time detection capability is improved, but device complexity increases
Solution Approach 1:
The processing system performs multiple functions within a single integrated architecture: it receives and processes sensor data, determines incident occurrence using machine learning models, initiates recording of incident data, transmits reports to remote systems, and controls vehicle actions. This multi-functionality achieves high automation while managing complexity by consolidating diverse functions into a unified system rather than requiring separate systems for each function.
Solution Approach 2:
The system employs a nested architecture where the machine learning model is embedded within the processing system, which in turn is integrated into the vehicle's existing sensor and communication infrastructure. The ML model nestles within the processing logic, which nests within the overall vehicle system. This nested structure achieves sophisticated automated detection while managing complexity through hierarchical integration.
3Reliability
If comprehensive sensor data recording is initiated during incidents, then evidence quality is improved, but data storage requirements and processing load increase
Solution Approach 1:
The system extracts and transmits only the most critical incident data to remote systems for storage and analysis. Rather than storing all sensor data locally, the processing system identifies relevant incident information, extracts it, and transmits it to remote data stores. This extraction approach ensures reliable incident evidence is preserved while reducing the data storage burden on the vehicle system.
Solution Approach 2:
The system initiates recording of incident data immediately upon detecting an incident, before the incident concludes. This preliminary action ensures comprehensive evidence capture while limiting the total data volume to only the incident period rather than continuous recording. The system proactively starts recording when needed and stops when the incident is resolved, optimizing both evidence reliability and data management.
Data Source
AI summary
Examples described herein provide a computer-implemented method that includes receiving first data from a sensor of a vehicle. The method further includes determining, by a processing device, whether an incident external to the vehicle has occurred by processing the first data using a machine learning model. The method further includes, responsive to determining that an incident external to the vehicle has occurred, initiating recording of second data by the sensor. The method further includes, responsive to determining that an incident external to the vehicle has occurred, taking an action to control the vehicle.