AI-Driven Autonomous Convoy:

Leader–Follower Technology

A conceptual AI-driven autonomous convoy system designed to improve safety and efficiency in high-risk mobility operations. It explores the problem space, key users, requirements, and potential opportunities through a product management lens.

Use Case
Military Convoy Operations

Author
Goni Kim

Industry
Defense

Written
12/01/2025

Overview

Problem Space

Users & Stakeholders

Short Summary

This project explores a conceptual AI-driven autonomous convoy system aimed at improving safety and efficiency in high-risk mobility operations.

Project Objective

To identify user needs, examine operational challenges, and evaluate how autonomous driving technologies could reduce risk, optimize resources, and enhance convoy performance.

Research Scope

This research covers problem analysis, user and stakeholder mapping, scenario exploration, technology evaluation, and early product opportunity discovery from a product management perspective.

Current Problem

Convoy operations in high-risk environments expose drivers and support personnel to significant threats, such as IEDs, ambushes, and hazardous terrain, while also requiring substantial manpower to execute safely.

Limitations of the Current System

Today’s convoys rely heavily on human drivers, lack real-time autonomous support, and depend on manual coordination, resulting in higher risk, limited scalability, and operational inefficiencies.

Why This Matters Now

Increasing mission tempo, manpower constraints, and rising operational threats highlight the need for safer, more efficient mobility solutions. Emerging autonomous technologies present a timely opportunity to reduce exposure and improve convoy resilience.

Primary Users

  • Vehicle Drivers – responsible for operating vehicles in hazardous environments.

  • Convoy Commanders – oversee movement, safety, and coordination during missions.

Pain Points:
High exposure to threats, heavy cognitive load, limited situational awareness, and high stress during complex or hostile operations.

Secondary Users

  • Maintenance Teams – ensure vehicle systems and sensors remain mission-ready.

  • Logistics Officers – plan routes, manage resources, and coordinate convoy schedules.

Pain Points:
Frequent vehicle downtime, complex maintenance for aging fleets, limited data for decision-making, and high planning overhead.

Goals and Success Metrics

SWOT Analysis

Goals

  • Enhance Safety: Reduce risk to human drivers in high-threat environments. Even if a driver is KIA or incapacitated, the convoy can continue its mission safely.

  • Reduce Manpower Requirements: Minimize the number of personnel needed to operate and monitor convoys, optimizing human resource allocation.

  • Increase Efficiency & Uptime: Improve convoy operational tempo, decrease downtime, and enable faster mission completion.

Success Metrics

  • Mission Continuity Rate: Percentage of missions completed without interruption despite driver unavailability.

  • Risk Reduction: Decrease in exposure to threats for personnel (e.g., KIA/near-miss incidents).

  • Operational Efficiency: Time saved per convoy mission, convoy speed improvements, and reduced human resource hours per operation.

  • Vehicle Uptime: Percentage increase in vehicles ready for deployment due to autonomous operation support.

Strengths

  • Enhanced Safety: Autonomous convoys reduce human exposure in high-risk operations, increasing personnel survivability.

  • Operational Efficiency: AI-guided vehicles can optimize speed, spacing, and route selection, improving mission tempo.

  • Scalable Technology: Leader–follower systems allow multiple vehicles to operate with minimal human oversight.

  • Data-Driven Insights: Real-time vehicle and environmental data can inform logistics, maintenance, and mission planning.

Weaknesses

  • Technology Reliability: Autonomous systems must operate flawlessly in unpredictable terrain and threat environments.

  • Integration Complexity: Existing vehicles, communication networks, and convoy protocols may be difficult to retrofit.

  • User Trust & Training: Operators and commanders may be hesitant to rely fully on AI for critical missions.

  • High Development Costs: R&D, maintenance, and sensor infrastructure require substantial investment.

Opportunities

  • Manpower Optimization: Reduce personnel requirements for convoy operations, freeing resources for other missions.

  • Force Multiplication: Autonomous convoys can operate in higher-risk zones that would be unsafe for humans.

  • Emerging AI & Sensor Technologies: Advances in computer vision, vehicle-to-vehicle communication, and LIDAR can improve performance and expand capabilities.

  • Cross-Domain Applications: Technology could extend to civilian logistics, emergency response, or disaster relief scenarios.

Threats

  • Cybersecurity Risks: AI systems are vulnerable to hacking, spoofing, or communication jamming.

  • Environmental & Terrain Challenges: Extreme weather, debris, or uncharted roads may disrupt autonomous operations.

  • Regulatory & Policy Constraints: Military protocols or safety regulations could limit deployment or require manual override.

  • Public / Military Trust: Failures, accidents, or misjudgments could undermine confidence in autonomous convoy technology.

Lean Canvas

Unique Value Proposition

Enable safer, more efficient convoy operations in high-risk environments by combining AI-driven leader–follower autonomy with full vehicle self-driving capabilities, reducing human exposure and optimizing resource use.

High-Level Concept

“Autonomous convoys that can operate in hazardous zones with minimal human oversight, ensuring mission continuity and improved operational efficiency.”

Unfair Advantage

  • Proprietary AI convoy algorithms for multi-vehicle coordination

  • Integration of real-time vehicle and environmental data

  • Scenario-tested autonomous navigation for military logistics

Channels

  • Military R&D and testing programs

  • Partnerships with defense contractors (e.g., Oshkosh, Lockheed)

  • Pilot deployments in controlled training and logistics exercises

Customer Segments

  • Military logistics departments

  • Convoy commanders and operational units

  • Defense technology integrators

Early Adopters

  • Forward-operating units in high-risk theaters

  • Military bases testing autonomous resupply solutions

  • Defense contractors developing next-gen mobility platforms

Cost Structure

  • AI R&D and algorithm development

  • Vehicle sensor and communication hardware

  • Maintenance, updates, and software retraining

  • Pilot deployment and testing costs

Revenue Stream

  • Government defense contracts

  • Integration services with existing vehicle fleets

  • Licensing of AI convoy technology to defense contractors

Proposed Solution

AI-Driven Convoy Autonomy

FSD‑Like Individual Vehicle Autonomy

How It Works (High-Level)

Core Features

Develop a multi-vehicle autonomy system that enables convoys to operate with minimal human intervention in high‑risk environments. The system processes battlefield variables—enemy weapon signatures, friendly force status at the company/squad level, vehicle health, and terrain constraints—to generate real‑time tactical recommendations.

Convoy commanders and squad leaders receive prioritized alerts and suggested actions based on each vehicle’s location and condition, while headquarters receives continuous status updates for faster, more informed support.
The platform is designed to scale across the broader soldier technology ecosystem, integrating with wearable devices (e.g., smart goggles, AR headsets) to display troop vitals, vehicle diagnostics, and convoy status directly in a commander’s view.

Introduce full self‑driving capabilities for individual vehicles within the convoy to support operations that demand rapid mobility under threat. Unmanned transport vehicles can extract squads from ambushes, reinforce small engagements, or deliver supplies without exposing a driver to risk.

Because the cabin no longer requires a dedicated operator, interior space can be repurposed for personnel or equipment, improving mission flexibility. If autonomous systems fail or the tactical situation requires manual control, the vehicle automatically transitions to a human-operated mode to ensure mission continuity.

Perception & Analysis: Sensors and onboard AI interpret battlefield conditions, vehicle status, and environmental data.

  1. Decision Layer: Autonomy models determine optimal convoy formation, route adjustments, and threat responses.

  2. Commander Interface: Recommendations and alerts are delivered through integrated displays or wearable AR systems.

  3. Operational Flexibility: Vehicles switch dynamically between autonomous, leader–follower, and manual modes depending on mission demands.

Epic 1: Convoy Coordination & Navigation

Features:

  • AI-Led Mission Navigation: Vehicles autonomously follow preloaded mission plans and dynamic route protocols.

  • Real-Time Vehicle-to-Vehicle & HQ Communication: Continuous status and threat updates between vehicles and headquarters, enabling coordinated adjustments and virtual driver support.

Epic 2: Autonomous Driving & Safety

Features:

  • Autonomous Obstacle & Threat Response: Vehicles detect and react to hazards automatically.

  • Modular AI Driving Modes: Supports fully autonomous, remote-assisted, or manual (via HQ intervention) operation.

  • Safety Protocols & Fallback Behaviors: Ensures mission continuity and occupant safety even if AI encounters an unexpected scenario.

Assumption & Constraints

Risks & Mitigations

Future Opportunities

Assumptions

  • Vehicles operate in environments with predictable terrain data and map availability.

  • AI systems can reliably process sensor inputs and maintain convoy coordination under typical mission conditions.

  • Headquarters maintains continuous communication and can provide remote assistance if needed.

Constraints

  • Environmental: Extreme terrain, weather conditions, and electromagnetic interference may impact sensor performance and vehicle autonomy.

  • Hardware: Vehicles are limited by onboard processing power, sensor range, battery/fuel capacity, and payload restrictions.

  • Regulatory / Policy: Military protocols, safety regulations, and operational rules may restrict autonomous operations or require human override.

AI Misclassification: Incorrect identification of obstacles or threats could compromise safety.
Mitigation: Use multi-sensor fusion, continuous model retraining, and scenario-based validation to improve accuracy.

  1. GPS / Communication Loss: Loss of positioning or data links may disrupt convoy coordination.
    Mitigation: Implement redundant communication channels, local vehicle autonomy, and predictive navigation algorithms.

  2. Cybersecurity Threats: AI systems could be vulnerable to hacking or spoofing.
    Mitigation: Apply end-to-end encryption, intrusion detection systems, and secure vehicle-to-vehicle communication protocols.

  3. Human Override Transition Failure: In case of AI failure, remote or manual takeover might be delayed or unsuccessful.
    Mitigation: Design seamless handover protocols, virtual driver support, and continuous monitoring from headquarters.

  • Full Autonomous Fleet: Expand the system to operate entire convoys fully autonomously, reducing human involvement and increasing operational flexibility.

  • Integration with Logistics AI: Connect convoy autonomy with broader logistics management systems to optimize supply chains, route planning, and mission scheduling.

  • Modular Autonomy Packages: Develop scalable AI modules that can be adapted for other vehicle types, enabling wider adoption across different military and civilian mobility platforms.