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![Autonomous convoy routing via drone
swarms and multi‑modal threat detection
Krish Kapadia1
, Yilin Liu2
, Zhenjiang Mao1
, Ishaan Sen1
, Zhongzheng Zhang1
, Zhouyang Zhou1
10/06-10/10, 2025
Sponsored by AWS and Maximus
By the AutoGators
[1] University of Florida
[2] Vanderbilt University](https://image.slidesharecdn.com/eam5-autogator1-251023052545-eca3e282/85/Autonomous-Convoy-Routing-via-Drone-Swarms-and-Multi-Modal-Threat-Detection-1-320.jpg)
![Autonomous convoy routing via drone
swarms and multi‑modal threat detection
Krish Kapadia1
, Yilin Liu2
, Zhenjiang Mao1
, Ishaan Sen1
, Zhongzheng Zhang1
, Zhouyang Zhou1
10/06-10/10, 2025
Sponsored by AWS and Maximus
By the AutoGators
[1] University of Florida
[2] Vanderbilt University](https://image.slidesharecdn.com/eam5-autogator1-251023052545-eca3e282/75/Autonomous-Convoy-Routing-via-Drone-Swarms-and-Multi-Modal-Threat-Detection-1-2048.jpg)
Uploaded byIvan Ruchkin
Autonomous Convoy Routing via Drone Swarms and Multi-Modal Threat Detection
The final presentation of the AutoGators team in the AWS & Vanderbilt Mission Autonomy Hackathon on October 10, 2025. This project won the "Most Innovative" Award (10k USD). Code: https://github.com/ishaan-sen/vb-autonomy-hacks
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Autonomous Convoy Routing via Drone Swarms and Multi-Modal Threat Detection
- 1. Autonomous convoy routingvia drone swarms and multi‑modal threat detection Krish Kapadia1 , Yilin Liu2 , Zhenjiang Mao1 , Ishaan Sen1 , Zhongzheng Zhang1 , Zhouyang Zhou1 10/06-10/10, 2025 Sponsored by AWS and Maximus By the AutoGators [1] University of Florida [2] Vanderbilt University
- 2. 2 Team Members Krish KapadiaYilin Liu Zhenjiang Mao Ishaan Sen Zhongzheng Zhang Zhouyang Zhou Sponsored by AWS and Maximus
- 3. Why Autonomous RoutePlanning Matters 3 Motivation Sponsored by AWS and Maximus • Supply-chain convoys operate in dynamic and uncertain environments • Human operators have limited situational awareness and can’t react quickly to fast- changing threats. • Traditional centralized navigation systems fail under communication loss or incomplete data.
- 4. Project Goals 4 Sponsored byAWS and Maximus • Enable real-time adaptive navigation through a swarm of drones that detect, analyze, and communicate threats. • Augment human decision-making rather than replace it.
- 5. Problem Definition Sponsored byAWS and Maximus 5 Given a road network and a reconnaissance drone swarm flying over it, plan safe and efficient convoy routes that avoid detected hazards while keeping humans in the loop.
- 6. Technical Challenges Sponsored byAWS and Maximus 6 • Dynamically detect threats along the convoy's path • Autonomously reroute the ground convoy to avoid danger • Find optimal safe path by minimizing travel time and distance • Leave final decision-making authority with human operators • Integrate efficiently with existing infrastructure
- 7. Novelty of theSolution Contributions 1. Three-Modality Threat Detection: Vision, Thermal, and Sound 3. Human-Centered Emergency Decision Making: Humans mediate the decision-making instead of end-to-end AI 2. Two-Stage Swarm-Guided Navigation Framework: Static Environment Scanning, Dynamic Route Planning Sponsored by AWS and Maximus 7
- 8. Demo Case Study:Nashville Area 1. Real-world map of Nashville, Tennessee 3. A demo based in Central Nashville Path planning and real-time decision making 2. Real/Synthetic Dataset & Environment Sponsored by AWS and Maximus 8 34363 Nodes, 113462 Edges For Vision: i5500 Images, 4 threat categories
- 9. System Overview Solution Threats DetectorPath Planning Algorithms “Optimal” Path Nashville City Map Static Environment Scanning Threats Detector Human Decision Making Final Path Dynamic Route Planning Sponsored by AWS and Maximus 9 Path Planning Algorithms Nashville City Map
- 10. AWS integration Solution AWS integration: •IoT Core • S3 • SageMaker • Bedrock • DynamoDB • EC2 Sponsored by AWS and Maximus 10
- 11. Threat Detection Solution Sound Anomaly Detection RCNN AWSRekognition AWS S3 CNN Vision Anomaly Detection Thermal Anomaly Detection Sponsored by AWS and Maximus 11
- 12. Vision Anomaly Detection Evaluation AIDERImage Dataset: 5138 images, 4 Anomaly Classes F1 Precision Recall Tested Image Fire 0.990 0.990 0.990 105 Flood 0.990 1.000 0.981 106 Traffic 0.942 0.957 0.928 97 Collapsed Building 0.970 1.000 0.942 103 Sponsored by AWS and Maximus 12
- 13. Sound Anomaly Detection Evaluation SoundWave Dataset: 2208 segments, 2 Anomaly Classes F1 Precision Recall Tested Segment s Fire 1.000 1.000 1.000 288 Normal 1.000 1.000 1.000 288 Sponsored by AWS and Maximus 13
- 14. Thermal Anomaly Detection Evaluation ImageDataset: 1000 images, 2 Detection Classes F1 Precision Recall Tested Image Fire 0.9899 1.000 0.9800 50 Normal 0.9901 0.9804 1.000 50 Sponsored by AWS and Maximus 14
- 15. Path Planning The outputof our solution is in the form of a path selection, which accounts for: • Road blockages • Hazard zones • Single points of failure Solutions Sponsored by AWS and Maximus 15
- 16. Path Planning The pathplanning algorithm uses a modified extension of the A* graph pathfinding algorithm for routing decisions. Solutions Sponsored by AWS and Maximus 16
- 17. Cost Analysis System Specification •Swarm Size: 20–45 drones • Sensors: RGB camera, thermal sensor, and sound module • Onboard Compute: Optional GPU/CPU for edge AI processing Hardware Cost • Market Price Range: $5K–$12K per drone, including sensors DJI Mavic 3T Enterprise DJI Matrice 30T Sponsored by AWS and Maximus 17
- 18. Limitations and FutureDirections Limitations • Sound sensing accuracy drops in noisy or windy conditions • Thermal and audio modules increase payload → shorter flight time • Cloud-based model calls may introduce latency Future Directions • Strengthen human–AI collaboration for faster, safer responses • Design lightweight multi-modal fusion (vision + thermal + audio) models • Integrate onboard AI chips to enable full edge autonomy Sponsored by AWS and Maximus 18
- 19. Key Takeaways Primary Objective: •Safe and Optimal Route Planning in an Emergency Situation AutoGators' Solution: • Three-Modality Threat Detection • Two-Stage Swarm-Guided Navigation Framework • Human-guided navigation decisions Our Example Demonstration: • Based on a real-world road map of Nashville • Presenting a demo in the area around Vanderbilt's campus Sponsored by AWS and Maximus 19
- 20. Contact Information Yilin Liu:[email protected] Ishaan Sen: [email protected] Krish Kapadia: [email protected] Zhenjiang Mao: [email protected] Zhongzheng Zhang: [email protected] Zhouyang Zhou: [email protected] Sponsored by AWS and Maximus 20
- 21. Thank You! AWS ProvidesCloud Services Maximus provides citizen services Sponsored by AWS and Maximus 21