The role of generative AI on fleet formation and optimization:

When fleet operators leverage superimposed layers on geographical maps, they can leverage AI and predictive analytics to enhance their decision-making on routes and schedules, increase efficiency, improve reliability, and enhance sustainability. The automations involve data collection, analysis, visualizations, predictions, and optimizations. Data-driven recommendations become available to these operators.

Specifically, these include:

Data collection: from a variety of sources such as sensors, cameras, GPS or external databases.

Data analysis: to extract patterns for vehicle performance, driver behavior, customer preferences, or traffic conditions.

Data visualization: such as dashboards, charts, maps, or reports

Data prediction: with models for regression and classifications

And data optimization: such as for route planning, maintenance scheduling, vehicle allocation, or formation adjustment.

The 3D-formation and flight path between source and destination across rocky terrain, on the other hand, has very little data other than contour maps, weather conditions and other environmental factors but they can still be viewed in segments and sequences and generative AI encodes and decodes state using transformers.

Specific use cases include: 

• Designing optimal flight paths and schedules for multiple drones, taking into account factors like weather, traffic, terrain, and regulations. Generative AI can use techniques like reinforcement learning or evolutionary algorithms to find the best solutions for complex optimization problems¹².
• Generating realistic simulations and scenarios for testing and training drone operators and systems. Generative AI can use techniques like generative adversarial networks (GANs) or variational autoencoders (VAEs) to create synthetic data and environments that mimic real-world conditions³⁴.
• Enhancing the capabilities and performance of drones, such as improving their vision, navigation, communication, and autonomy. Generative AI can use techniques like convolutional neural networks (CNNs) or transformers to process and generate visual, audio, or textual data from drones’ sensors and cameras⁵⁶.

More information about generative AI and its applications is available via the following resources:

• The Generative AI Dossier: A collection of high-impact use cases of generative AI across six major industries, including transportation and mobility, by Deloitte.
• AI Drones: How Artificial Intelligence Works in Drones and Examples: An article that showcases 13 companies that are using AI to improve a new generation of intelligent drones, by Built In.
• AI-Driven Fleet Management: Predict, Optimize, Automate: An article that explains how AI-based recommendations can help skilled fleet workers excel, by Hitachi.
• The Essential Guide to Applying AI to the Drone Delivery Ecosystem: A guide that covers the key applications of AI to drone delivery, such as obstacle detection and avoidance, GPS-free navigation, contingency management and emergency landings, delivery drop, and safe landings, by CloudFactory.