Neural Network Vehicle Role Detection
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Solution Overview
Problem
Existing technologies struggle to accurately determine whether individuals are operators or passengers of vehicles, leading to inaccurate policy processing and benefits attribution.
Innovation Solution
A computer-implemented method and system that utilize neural networks to analyze sensor data from electronic devices associated with individuals, determining probabilities indicative of their role in a vehicle, thereby accurately distinguishing between operators and passengers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from electronic devices is collected and analyzed to determine individual roles in vehicles, then measurement precision of individual role detection is improved, but device complexity increases due to neural network implementation
Solution Approach 1:
The patent introduces a neural network as an intermediary computational model that mediates between raw sensor data and individual role determination. The neural network processes acceleration data, location data, and other sensor inputs to infer whether an individual is an operator or passenger, thereby improving detection precision while encapsulating complexity within the trained model rather than requiring complex real-time processing logic
Solution Approach 2:
The patent transforms physical sensor parameters (acceleration, location, device movement patterns) into probabilistic outputs indicating individual roles. By changing the parameter representation from raw sensor values to probability distributions through neural network processing, the system achieves higher measurement precision in determining operator versus passenger status
2Reliability
If neural networks are used to analyze sensor data for role detection, then reliability of policy processing is improved, but loss of time increases due to data processing requirements
Solution Approach 1:
The neural network is pre-trained offline using labeled training data before deployment. This preliminary action allows the model to learn complex patterns and relationships in sensor data that indicate individual roles, so that during actual policy processing, the network can quickly infer roles without requiring complex real-time computations, thereby maintaining high reliability while reducing processing time
Solution Approach 2:
The patent replaces traditional rule-based or threshold-based detection mechanisms with a neural network-based probabilistic inference system. This substitution enables more reliable and nuanced determination of individual roles by learning from training data, while the trained network's efficient forward propagation provides faster processing compared to complex rule engines
Data Source
AI summary
Systems and methods for using collecting and analyzing device sensor data to determine whether an individual is an operator or a passenger of a vehicle are disclosed. According to certain aspects, an electronic device associated with the individual may collect or access sensor data that is indicative of or associated with an operation of the vehicle. The electronic device may transmit pertinent portion(s) of the sensor data to a backend server, which may input the portion(s) into a neural network for analysis. The neural network may output a probability metric(s) indicative of whether the individual is a passenger or an operator of the vehicle.