Autonomous Emergency Evacuation System with Dynamic Drop-off Selection
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Solution Overview
Problem
Current emergency evacuation systems lack efficiency and safety in rapidly and effectively relocating at-risk individuals during emergencies, as they do not adequately account for the type of emergency and the safety and capacity of potential drop-off locations.
Innovation Solution
An emergency evacuation system that utilizes autonomous vehicles to identify and classify safe locations based on emergency type, determines the number of at-risk individuals, and deploys vehicles to safely transport them to suitable drop-off locations, ensuring safety thresholds and capacity are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If autonomous vehicles are deployed to evacuate at-risk individuals during emergencies, then the speed of evacuation is improved, but the complexity of coordinating multiple vehicles and locations increases
Solution Approach 1:
The system segments the evacuation process into distinct phases: emergency detection, safe location identification, at-risk individual counting, vehicle deployment, and drop-off coordination. Each phase is handled by specific system components working independently but coordinated through a central platform, reducing overall system complexity while maintaining high evacuation speed.
Solution Approach 2:
The system performs preliminary identification of safe locations and determination of at-risk individual counts before deploying autonomous vehicles. This advance preparation allows vehicles to be dispatched immediately with pre-calculated routes and destination assignments, eliminating coordination delays during the actual evacuation process.
2Reliability
If multiple prospective safe locations are identified and evaluated, then the safety of drop-off locations is improved, but the time required to select appropriate locations increases
Solution Approach 1:
The system evaluates prospective safe locations using multiple parameters including safety ratings based on emergency type, capacity to accommodate at-risk individuals, and proximity to the emergency location. By establishing predetermined thresholds for these parameters, the system can rapidly filter and rank locations without exhaustive analysis, ensuring both safety and speed in location selection.
3Productivity
If the system determines the number of at-risk individuals and matches them with appropriate vehicle capacity, then the efficiency of evacuation is improved, but the complexity of logistics coordination increases
Solution Approach 1:
The system continuously monitors the number of at-risk individuals identified and compares this data with the capacity of available autonomous vehicles. This feedback loop enables dynamic adjustment of vehicle deployment plans, ensuring that the number of vehicles dispatched matches the actual evacuation needs. The system optimizes logistics by assigning specific vehicles to specific locations based on real-time capacity requirements, improving efficiency while maintaining manageable coordination complexity through automated decision-making.
Data Source
AI summary
Aspects of the present disclosure relate to emergency evacuation. An emergency can be detected at an emergency location. A type of the emergency can be determined. Prospective safe locations proximate to the emergency location can be identified. A safety rating of each prospective safe location can be determined based on the type of emergency. A number of at-risk individuals at the emergency location can be determined. A subset of drop-off locations of the prospective safe locations that have a safety rating that satisfies a safety threshold can be selected, the subset of drop-off locations satisfying a size limit required for the number of at-risk individuals. A set of autonomous vehicles required for the number of at-risk individuals can then be determined. The set of autonomous vehicles can be deployed to the emergency location.