Vehicle Event Trigger Feedback for Real-Time Safety Detection
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Solution Overview
Problem
Existing vehicle safety systems face challenges in providing real-time, accurate safety event detection due to the need for significant data storage and processing power, leading to delayed alerts and limited effectiveness in improving driver safety, as they rely on wireless data communications with varying bandwidth and speed.
Innovation Solution
A vehicle device equipped with a machine learning model feedback loop that compares probabilities from a local machine learning model and a backend server to adjust event detection, allowing for real-time adjustments without retraining, and includes a neural network to identify distracted states or events, enabling immediate actionable alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is transmitted to a server for event model application, then measurement precision of safety events is improved, but loss of time increases due to wireless communication delays
Solution Approach 1:
The system segments the event detection process into two parts: a lightweight model that runs locally on the vehicle device for immediate real-time detection and alert delivery, and a comprehensive model that runs on the backend server for refined analysis. This segmentation allows critical safety events to be detected and responded to immediately while still benefiting from server-based refinement when conditions permit.
Solution Approach 2:
A feedback mechanism acts as an intermediary between the local lightweight model and the backend server. The feedback loop transmits refined event probabilities from the server back to the vehicle device, allowing the local model to adjust its detections without requiring continuous server communication for every event assessment.
2Measurement precision
If a comprehensive machine learning model is deployed to the vehicle device, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The machine learning model is segmented into two versions: a lightweight version deployed on the vehicle device that requires minimal processing power and memory, and a comprehensive version running on the backend server. The lightweight version handles real-time inference with limited resources, while the comprehensive version provides refined analysis when full computational capacity is available.
3Measurement precision
If the machine learning model is adjusted to improve detection accuracy, then measurement precision is improved, but loss of time increases due to retraining requirements
Solution Approach 1:
The system performs preliminary refinement of event detection parameters on the backend server without requiring offline retraining of the entire model. The server generates refined probability thresholds and detection parameters in advance, which are then deployed to vehicle devices, allowing rapid adaptation to new event patterns without time-consuming retraining cycles.
Solution Approach 2:
Instead of retraining the entire machine learning model when detection accuracy needs improvement, the system changes specific parameters such as probability thresholds and detection sensitivity levels. These parameter adjustments are made on the backend server and deployed to vehicle devices, enabling rapid adaptation without the time and computational resources required for full model retraining.
Data Source
AI summary
A vehicle device may 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. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.