AI Dash Cam Event Detection With Local Alerts and Selective Upload
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Solution Overview
Problem
Current systems for real-time safety event detection in vehicles face challenges due to high data storage and processing requirements, leading to delayed alerts and reduced effectiveness due to the need for wireless data transmission, which limits the accuracy and timeliness of safety alerts provided to drivers.
Innovation Solution
The implementation of an artificial intelligence dash cam that processes video data locally to detect safety events without transmitting data to a backend server, providing immediate actionable alerts, while a backend server optimizes data transmission and updates event models for improved accuracy and reduced bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is transmitted to a server for processing, then event detection accuracy can be improved through comprehensive analysis, but wireless communication delays reduce the timeliness of safety alerts
Solution Approach 1:
The system segments the event detection process into two parts: real-time processing of critical safety parameters (braking, acceleration, steering) is performed locally in the vehicle, while comprehensive analysis of additional sensor data and video footage is transmitted to the server for post-event investigation. This segmentation enables immediate safety responses without waiting for server processing.
Solution Approach 2:
The system performs preliminary processing and filtering of sensor data locally before transmission, pre-identifying potential safety events and extracting only relevant data for server analysis. This preliminary action reduces transmission time and enables faster initial response while maintaining comprehensive analysis capabilities.
2Measurement precision
If comprehensive sensor data is processed in real-time, then safety event detection accuracy is improved, but data storage and processing power requirements increase significantly
Solution Approach 1:
The system extracts only the most critical safety-related parameters (braking force, acceleration rates, steering angles, collision forces) for real-time processing, while storing comprehensive raw sensor data and video footage for later server analysis. This extraction approach maintains high detection accuracy for immediate safety responses while reducing real-time processing burden.
Solution Approach 2:
The server acts as an intermediary that receives and processes comprehensive data for detailed analysis, while the in-vehicle system handles real-time critical parameter monitoring. This intermediary architecture distributes processing complexity, enabling accurate event detection without overwhelming in-vehicle computational resources.
3Measurement precision
If wireless data transmission is used for server processing, then comprehensive event analysis is enabled, but bandwidth requirements and data transmission delays increase
Solution Approach 1:
The system transmits only partial data sets to the server based on event severity and type - critical safety events trigger transmission of comprehensive data, while routine monitoring transmits only aggregated statistics and key parameters. This partial action approach enables comprehensive analysis when needed while minimizing bandwidth consumption during normal operation.
Data Source
AI summary
A vehicle dash cam may be configured to execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. Detection of a safety event may trigger an in-cab alert to make the driver aware of the safety risk. The dash cam may include logic for determining which asset data to transmit to a backend server in response to detection of a safety event, as well as which asset data to transmit to the backend server in response to analysis of sensor data that did not trigger a safety event. The asset data transmitted to the backend server may be further analyzed to determine if further alerts should be provided to the driver and/or to a safety manager.