Traffic Surveillance Using Passenger Brain Wave Error Monitoring
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Solution Overview
Problem
Current traffic surveillance systems lack an effective method to monitor and enforce adherence to traffic rules, particularly in real-time, using innovative technologies that can detect violations based on cognitive functions.
Innovation Solution
A traffic surveillance system utilizing an error monitoring apparatus that collects and analyzes Event-Related Potentials (ERPs) from passengers to determine if traffic rules are being followed, transmitting error information to a traffic control server, which can then impose penalties for rule violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional traffic surveillance systems are used, then basic traffic monitoring is possible, but real-time detection of traffic rule violations based on cognitive functions cannot be achieved
Solution Approach 1:
The patent introduces an error monitoring apparatus as an intermediary device that collects Event-Related Potential (ERP) signals from passengers' brains and transmits them to a traffic control server. This intermediary system bridges the gap between traditional traffic surveillance and cognitive function detection, enabling violation detection through brain wave analysis without requiring complete system redesign
Solution Approach 2:
The patent replaces traditional mechanical/optical traffic violation detection methods with a biological signal-based system. Instead of using cameras and sensors to detect physical violations, the system uses ERP signal processing to detect cognitive states and determine whether traffic rules are being followed, substituting mechanical detection with neurophysiological measurement
2Productivity
If ERP signal processing is implemented, then real-time traffic rule compliance detection is enabled, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing of ERP signals by collecting and pre-processing brain wave data continuously while the passenger is in the mobility. The system prepares the neural signals in advance and transmits them to the server for final analysis, allowing real-time detection without intensive processing during critical decision moments
Solution Approach 2:
The patent divides the ERP signal processing into separate functional modules: signal collection by the error monitoring apparatus, signal transmission, and signal analysis by the traffic control server. This segmentation distributes computational load and allows parallel processing, reducing overall processing time and enabling real-time violation detection
3Reliability
If brain wave signals are collected and analyzed, then accurate error detection is possible, but privacy and ethical concerns arise
Solution Approach 1:
The patent extracts only the necessary ERP signal data required for error detection and transmits only this specific information to the traffic control server. By taking out only the relevant neural signals needed for violation detection rather than collecting comprehensive brain data, the system maintains detection reliability while minimizing privacy intrusion
Solution Approach 2:
The patent implements a feedback mechanism where the system monitors ERP signals to determine whether traffic rules are being followed and provides appropriate feedback through the mobility system. This closed-loop feedback ensures reliable error detection while the feedback is limited to traffic rule compliance information rather than personal cognitive data, addressing privacy concerns
Data Source
AI summary
An error monitoring apparatus and method are provided. The error monitoring method includes collecting an Event-Related Potential (ERP) for at least one passenger in a first mobility for a predetermined amount of time, analyzing the ERP collected for the predetermined amount of time, and transmitting error information of the first mobility to a traffic control server based on a result of analysis. The error information of the first mobility includes at least one of information on a time when the ERP is generated, a waveform of the ERP, position information of the first mobility or operation information of a second mobility, and the second mobility is different from the first mobility and has caused the ERP.