Modular Neural Network Layer Tuning for In-Vehicle Event Detection
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Solution Overview
Problem
Current machine learning models for real-time safety event detection in vehicles require significant data storage and processing power, and retraining these models is often time-consuming and costly due to limited compute capabilities, leading to inefficiencies in updating and redeploying models for detecting events like distracted driving.
Innovation Solution
Implementing a modular neural network that segments into independent layers or models, allowing for independent tuning and retraining of each layer, and using an ensemble neural network to combine outputs from multiple models for enhanced detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single-shot event detector is used to detect safety events in real-time, then the system can provide immediate safety monitoring, but the retraining and fine-tuning of the detector is limited due to lack of compute capabilities and becomes time and cost-prohibitive
Solution Approach 1:
The neural network is divided into multiple independent layers, where each layer can be trained and fine-tuned separately. This segmentation allows the system to update individual layers without retraining the entire network, significantly reducing retraining time and computational resource requirements while maintaining detection reliability.
2Reliability
If a single-shot event detector is used for real-time safety monitoring, then immediate detection is achieved, but the compute capabilities required for retraining are insufficient and the process becomes cost-prohibitive
Solution Approach 1:
By segmenting the neural network into independent layers, the computational complexity of retraining is distributed across multiple smaller, manageable units. Each layer can be fine-tuned with reduced compute resources compared to training the entire network, making the system more cost-effective and feasible for deployment in vehicles with limited computing power.
3Measurement precision
If the entire neural network is retrained to improve detection accuracy, then model performance increases, but the downtime required for updating the model increases significantly
Solution Approach 1:
The neural network is divided into multiple independent layers that can be trained and updated separately. This allows the system to fine-tune individual layers to improve detection accuracy without requiring downtime for the entire network, as updates can be applied incrementally to specific layers while the rest of the system remains operational.
Solution Approach 2:
Instead of retraining the entire neural network, the system applies partial action by fine-tuning only the specific layers that need improvement. This selective approach achieves the necessary accuracy enhancement while minimizing update time and computational overhead, avoiding the excessive action of complete network retraining.
Data Source
AI summary
A vehicle device may execute one or more neural networks (and/or other artificial intelligence), based on input from one or more of the cameras and/or other sensors, to intelligently detect safety events in real-time. The one or more neural networks may be an ensemble neural network that includes neural networks for detecting a head and hand of a user, neural networks for detecting hand actions of the user, neural networks for detecting the head pose of the user, neural networks for predicting an occurrence of an event, and neural networks for predicting a start time and end time of the event. Further, the neural networks can be segmented into a modular neural network based on metadata. The segmentation of the neural network can define a thin layer of the modular neural network to enable independent tuning of the thin layer of the modular neural network.