Roadside Drone Dispatch System Using Traffic Prediction
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Solution Overview
Problem
Arranging roadside drone service is difficult due to lack of awareness among potential beneficiaries, difficulty in finding suitable drones, and delayed arrival of drones, which reduces their effectiveness.
Innovation Solution
A system that receives traffic data to predict future road conditions, identifies locations needing drone service, and automatically routes suitable drones to provide services by establishing contracts with drone owners/operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If drones are dispatched to provide roadside service, then service effectiveness is improved, but arrangement difficulty and time consumption increase
Solution Approach 1:
The system enables self-service by having drones automatically dispatch themselves to needed locations based on predicted traffic conditions. The drone service system receives traffic data, predicts future road conditions, and automatically routes suitable drones without requiring manual arrangement by beneficiaries, thereby reducing arrangement difficulty while maintaining service effectiveness
Solution Approach 2:
The system implements feedback mechanisms by continuously receiving traffic data, predicting future conditions, and adjusting drone dispatch decisions accordingly. This closed-loop approach ensures drones are sent to locations where they will be most effective based on real-time and predicted traffic states, resolving the contradiction between service effectiveness and arrangement complexity
2Reliability
If drones are dispatched manually, then service can be provided, but arrival time is delayed reducing effectiveness
Solution Approach 1:
The system performs preliminary action by predicting future road conditions before they occur and proactively dispatching drones to anticipated problem locations. By using traffic data to forecast future states and pre-positioning drones at predicted issue locations, the system eliminates delays associated with reactive manual dispatch, thereby reducing arrival time while maintaining service effectiveness
Solution Approach 2:
The system applies dynamics by continuously adapting drone dispatch decisions based on real-time traffic data and predicted conditions. The dynamic routing and dispatch mechanism adjusts to changing traffic states, ensuring optimal arrival times and maximizing service effectiveness in response to evolving road conditions
3Productivity
If traffic data is collected and processed to predict needs, then drone dispatch efficiency is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by using a single integrated platform that performs multiple functions: collecting traffic data from various sources, processing and analyzing the data, predicting future road conditions, identifying suitable drones, and routing them to appropriate locations. This multi-functional approach improves dispatch efficiency while managing system complexity through consolidation rather than separation of functions
Data Source
AI summary
A method comprises receiving, at a vehicle system of a vehicle, sensor data from one or more sensors; detecting an object based on the sensor data; determining whether a dynamic map maintained by a remote computing device includes the detected object; upon determination that the dynamic map includes the detected object, determining a remaining time-to-live associated with the detected object based on data associated with the dynamic map; and transmitting data about the detected object to the remote computing device if the determined remaining time-to-live associated with the detected object is less than a predetermined threshold time.