AI Road Incident Detection with Interactive Human Verification
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Solution Overview
Problem
Current road inspection technologies are costly, require complex installations, and are limited in capability, often relying on manual methods due to high costs and specialized equipment, which are inefficient for detecting a wide range of road-related incidents.
Innovation Solution
A system that combines artificial intelligence with human interaction, using a mobile device with a camera and sensors to autonomously detect and report road incidents, allowing for interactive tagging and adjustment of AI-generated data, and transmitting this information to a remote server for processing and incident management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized vehicles with multiple sensors are used for road assessment, then measurement precision and detection capability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The system segments the road incident detection task into two parts: (1) AI model performs automated detection of road incidents from video data, and (2) human operator reviews and confirms detections through a user interface. This segmentation allows the system to achieve high detection accuracy without requiring complex specialized sensor equipment, as the AI model handles the complex analysis while standard cameras capture the data.
Solution Approach 2:
The system enables self-service road assessment by allowing human operators to independently review and confirm AI-detected incidents through a mobile device interface. This eliminates the need for specialized trained personnel and complex calibration procedures, as the system provides pre-processed incident data that operators can verify with minimal training.
2Device complexity
If manual road surveying methods are used, then device complexity is reduced, but productivity and time consumption worsen
Solution Approach 1:
The system performs preliminary action by having the AI model automatically detect and tag potential road incidents in video data before human review. This pre-processing step filters and organizes data, so that when operators review the footage, they only need to verify pre-identified incidents rather than manually searching through entire video datasets, dramatically increasing productivity while keeping equipment simple.
Solution Approach 2:
The system introduces an intermediary AI model between the video data and human operators. The AI model processes video data to identify and tag road incidents, then presents filtered results to operators. This intermediary layer automates the time-consuming detection work while maintaining simple equipment, resolving the contradiction between productivity and device complexity.
3Measurement precision
If complete video datasets are uploaded to servers for processing, then measurement precision is improved, but loss of time and network bandwidth increase due to large data sizes
Solution Approach 1:
The system extracts only the essential information needed for incident detection by using the AI model to analyze video data locally and extract tagged incident data. Instead of uploading complete video datasets, only the extracted incident information (coordinates, timestamps, detection confidence) is transmitted to the server. This extraction approach maintains detection precision while minimizing data transmission time and bandwidth usage.
Solution Approach 2:
The system performs preliminary data processing and filtering on the mobile device before transmission. The AI model pre-processes video data to identify and tag incidents, then only the tagged incident data is uploaded to the server. This preliminary action reduces the data volume that needs to be transmitted while preserving all necessary information for accurate incident detection and review.
Data Source
AI summary
System and methods for including a system mounted to a vehicle for identifying incidents of a roadway and transmitting the incidents to a server, the server located remotely from the system, the system comprising: a device having: a camera for obtaining digital images; at least one sensor including a location based sensor; a memory and processor for executing image processing instructions for processing the digital images for automated detection of the incidents, generating object data based on the processing, generating incident data including the object data and the images; and a network interface for sending the incident data over a communications network to the server during operation of the vehicle on the roadway. Also included are both interactive and autonomous possesses for implementing the device.