Multi-Vehicle Event Detection and Video Data Collection
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Solution Overview
Problem
Unoccupied vehicles, such as parked cars, often lack effective video data capture during events like break-ins or accidents due to camera configurations that focus on the front or road, leaving owners and authorities without crucial information for timely response and damage prevention.
Innovation Solution
A system where a primary vehicle's control device detects events, obtains video data, and communicates with additional vehicles to gather additional data, processing this information with a server to determine event types and trigger relevant actions, such as alerting authorities or insurance providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If camera devices are configured to focus on the front or road, then the vehicle can capture road conditions and front views, but the vehicle cannot capture events occurring at the back or sides during break-ins or accidents
Solution Approach 1:
The patent combines video data from multiple sources including the primary vehicle's camera and additional vehicles in the proximity zone to create comprehensive event coverage. This merging of multiple camera perspectives resolves the limitation of single-vehicle camera configurations.
Solution Approach 2:
The system enables camera devices to serve multiple functions: capturing front views for normal driving, detecting events at the back or sides during incidents, and providing comprehensive coverage when multiple vehicles collaborate. This multi-functionality addresses the coverage gap without requiring dedicated cameras for every position.
2Loss of information
If the system requests additional video data from multiple additional vehicles, then comprehensive event information can be gathered, but computing resources and communication bandwidth are consumed
Solution Approach 1:
The system performs preliminary event classification using detecting devices (sensors, microphones) before requesting video data. This preliminary action filters out non-critical events, preventing unnecessary video data requests and conserving computing resources while maintaining information completeness for genuine incidents.
Solution Approach 2:
The system requests video data from additional vehicles selectively based on event type and severity. Rather than requesting data from all nearby vehicles for every detected event, the system applies partial action by targeting only those cases where additional video evidence is likely to be valuable, optimizing the balance between information completeness and resource consumption.
3Measurement precision
If the system processes video data from multiple vehicles to determine event types, then accurate event classification can be achieved, but processing time and computational load increase
Solution Approach 1:
The system performs preliminary event classification using detecting devices before detailed video analysis. This preliminary classification provides an initial event type assessment that guides subsequent video processing, reducing the computational load and time required for full video analysis while maintaining classification accuracy.
Solution Approach 2:
The processing system segments video data analysis into priority levels based on event type and severity. Critical events receive immediate full analysis, while less urgent events undergo prioritized processing. This segmentation reduces overall processing time while maintaining accurate classification for all events.
4Loss of information
If the primary vehicle sends messages to multiple additional devices requesting data, then comprehensive event data can be collected, but communication overhead and network traffic increase
Solution Approach 1:
The primary vehicle performs preliminary event assessment using local detecting devices before initiating communication with additional vehicles. This preliminary action determines whether external video data is actually needed, filtering out cases where the primary vehicle's own sensors provide sufficient information and avoiding unnecessary communication overhead.
Solution Approach 2:
The system uses an event type classification intermediary to determine which additional vehicles need to be contacted. Rather than broadcasting requests to all nearby vehicles, the intermediary selectively identifies and contacts only those additional vehicles whose video data would be relevant to the specific event type, reducing communication overhead and network traffic.
Data Source
AI summary
A server device obtains, from a primary device associated with a first vehicle, event data concerning an event, video data concerning the event, and/or location data concerning a location of the first vehicle. The server device processes the event data to determine a type of the event, and determines, based on the type of the event and the location data, a proximity zone around the location of the first vehicle. The server device determines additional devices within the proximity zone, where each additional device is associated with a different vehicle. The server device sends, to the additional devices, a message requesting additional video data concerning the event, and obtains the additional video data from at least one additional device. The server device performs, based on relevant event information based on the event data, the video data, and the additional video data, an action concerning the event.