Autonomous First-Response Routing for Data-Driven Risk Deployment
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Solution Overview
Problem
Current police patrol assignment methods rely on intuition rather than data, leading to inefficient resource allocation, incorrect risk assessment, and increased crime in non-designated areas, as well as inadequate training and resource consumption.
Innovation Solution
An autonomous first response system that processes emergency, traffic, network, and crime data using machine learning models to identify risk levels and deploy autonomous vehicles to high-risk areas, optimizing resource allocation and providing intelligent patrol routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If police officers are assigned to designated patrol areas based on intuition, then coverage of certain areas is ensured, but resource allocation becomes inefficient and crime rates increase in non-designated areas
Solution Approach 1:
The patent replaces the mechanical system of human intuition-based patrol assignment with an automated computer system that processes multiple data sources (crime data, emergency data, traffic data, weather data, social media data) through machine learning algorithms to objectively determine patrol area risk levels and optimize officer deployment
Solution Approach 2:
The system continuously collects real-time data from multiple sources including crime reports, emergency calls, traffic conditions, and social media, processes this feedback through machine learning models, and dynamically adjusts patrol area assignments to respond to changing risk conditions
2Reliability
If more officers are allocated to patrol areas, then crime prevention in those areas improves, but resources consumed for training and operations increase
Solution Approach 1:
The system changes the parameter of patrol area assignment from static, intuition-based designations to dynamic, data-driven risk level classifications that are continuously updated based on multiple data sources, allowing optimal allocation of fixed police resources to high-risk areas without increasing overall resource consumption
3Productivity
If autonomous vehicles are deployed to high-risk areas, then resource conservation is achieved, but system complexity increases
Solution Approach 1:
The patent segments the police force into human officers and autonomous vehicles, with each type performing different functions: autonomous vehicles handle routine patrol and monitoring in high-risk areas to conserve human resources, while human officers focus on complex investigations and high-priority responses
Solution Approach 2:
The system introduces an autonomous vehicle as an intermediary between human officers and the environment, allowing officers to remain stationary or relocate to different areas while autonomous vehicles perform patrol functions, thereby conserving human resources without requiring direct human involvement in routine patrol tasks
Data Source
AI summary
A device may receive emergency data, traffic data, network performance data, crime data, and gunshot data associated with a geographical area and may identify a location within the geographical area based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data. The device may determine, based on the emergency data, the traffic data, the network performance data, the crime data, and the gunshot data for the location, a risk level for the location and may identify an autonomous vehicle based on the risk level, the traffic data, and the network performance data for the location. The device may determine a route for the autonomous vehicle to the location based on the traffic data and the network performance data for the location, and may perform actions based on the autonomous vehicle and the route.