Vehicle-Launched UAV Threat Response for Real-Time Intruder Detection
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Solution Overview
Problem
Current security systems fail to effectively deter and respond to threats in real-time, particularly in interactions between law enforcement and citizens, as traditional alarms often become background noise, allowing potential criminals to commit crimes without being identified or apprehended.
Innovation Solution
A method utilizing unmanned aerial vehicles (UAVs) equipped with flight controllers, machine learning systems, and sensors to detect and classify events, launch response operations such as identifying intruders, alerting authorities, and guiding law enforcement, through a system that integrates event databases, response plans, and AI for predictive scoring and action planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional alarm systems are used to deter threats, then attention can be drawn to potential threats, but the alarm becomes background noise and is neglected, allowing crimes to go undetected
Solution Approach 1:
The patent transitions from ground-based alarm systems to aerial surveillance using drones, adding a vertical dimension to threat detection. This dimensional change allows the system to observe crimes from above, preventing the alarm from becoming background noise by providing a novel perspective that captures criminal activities that ground-based systems miss.
Solution Approach 2:
The patent introduces an intermediary AI system that processes surveillance data and automatically detects criminal patterns. This intermediary layer between the raw surveillance data and law enforcement enables reliable threat identification even when alarms become background noise, as the AI continuously analyzes data to distinguish genuine threats from normal activities.
2Productivity
If law enforcement officers directly respond to threats, then immediate action can be taken, but officers are exposed to danger and may be assaulted or killed
Solution Approach 1:
The patent positions drones as intermediary agents between law enforcement officers and criminal threats. The drones can approach suspects and gather intelligence without exposing officers to direct danger, serving as a buffer that protects officer safety while maintaining response capability. The AI system further acts as an intermediary by pre-processing threat assessment, allowing officers to respond more safely with better information.
Solution Approach 2:
The patent implements preliminary surveillance and threat assessment using drones and AI before officers engage with suspects. This preliminary action allows the system to identify threats, plan responses, and gather intelligence in advance, enabling officers to take safer, more informed actions when they do respond to threats.
3Measurement precision
If comprehensive surveillance is implemented to identify criminals, then criminal identification improves, but system complexity and resource requirements increase
Solution Approach 1:
The patent divides the surveillance system into modular components: drone hardware, onboard sensors, AI processing units, and communication systems. This segmentation allows the complex surveillance function to be broken into manageable parts that can be developed, deployed, and maintained independently, reducing overall system complexity while maintaining identification accuracy.
Solution Approach 2:
The patent implements self-service capabilities through autonomous drone operation and automated AI analysis of surveillance data. The system performs threat detection, criminal identification, and response coordination without requiring constant human intervention, reducing operational complexity while maintaining high identification accuracy through continuous automated processing.
Data Source
AI summary
A threat response system including one or more UAVs for protecting a vehicle of an owner. The one or more UAVs may be located in a docking station within a trunk of the vehicle. The threat response system may launch the one or more UAVs in response to detecting an intruder in a vicinity of the vehicle. The threat response system may further classify an occurring event by analyzing data received from the one or more UAVs with a machine learning system trained on data in an event database, with the event database storing one or more predicted events each corresponding to an event where the vehicle is vandalized, broken into, and/or stolen by the intruder. The threat response system may further select one or more UAV response operations from a response plan database to address the occurring event based on the classification of the occurring event.