Vehicle Anomaly Recognition and Routing for Driver-Free Reporting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Drivers face challenges in recognizing and notifying appropriate authorities about various surrounding anomaly events while driving, as they are difficult to communicate during operation and may lead to delays and confusion if the notification is made to inappropriate destinations.
Innovation Solution
An anomaly management system that utilizes a camera-mounted vehicle to automatically recognize anomaly events through machine learning, determine appropriate notification destinations, and transmit relevant information without driver intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the driver manually notifies anomaly events, then the driver can communicate the event details, but the driver cannot do so while driving and notification delays occur
Solution Approach 1:
The system enables self-service by having the vehicle's camera and processing unit automatically detect, recognize, and notify anomaly events without driver intervention. The system serves itself by capturing images, analyzing them for anomalies, and transmitting notifications to appropriate destinations autonomously.
Solution Approach 2:
The system performs preliminary action by continuously capturing images with the camera and pre-processing them for anomaly detection before the driver needs to notify anything. The system is ready to detect and notify anomalies as they occur, eliminating the need for the driver to manually report.
2Loss of information
If the driver notifies anomaly events manually, then the driver can provide event details, but the driver does not know where to notify and confusion occurs
Solution Approach 1:
The processing unit acts as an intermediary that receives the raw image data, analyzes it to identify the type of anomaly event, determines the appropriate notification destination based on the event type, and formats the notification. This intermediary function eliminates the need for the driver to know where to notify and resolves confusion about notification destinations.
Solution Approach 2:
The system provides feedback by analyzing the captured image, identifying the anomaly event type, and using this information to automatically determine the correct notification destination. This feedback loop ensures that the notification is sent to the appropriate authority based on the specific event detected.
3Reliability
If the driver notifies each anomaly event manually, then the driver can explain the event, but the driver is distracted from driving and safety is compromised
Solution Approach 1:
The system performs self-service by automatically capturing images, detecting anomalies, and sending notifications without requiring the driver to divert attention from the road. The vehicle's automated system handles the notification task, ensuring driving safety while maintaining notification efficiency.
Solution Approach 2:
The system replaces the mechanical action of manual notification with an automated electronic system. Instead of the driver physically communicating the event, the camera and processing unit electronically detect and transmit the anomaly information, freeing the driver to focus on driving while maintaining notification productivity.
Data Source
AI summary
An anomaly management system includes one or more processors. The one or more processors are configured to execute: video acquisition processing of acquiring a video captured by a camera mounted on a moving body; anomaly event recognition processing of automatically recognizing an anomaly event shown in the video and content of the anomaly event by using a machine learning model; notification destination determination processing of automatically determining a notification destination according to the content of the anomaly event; and notification processing of automatically transmitting to the notification destination, anomaly event information including at least information indicating the content of the anomaly event and position information indicating a position of the anomaly event.