For serious devs, future founders, and system designers
Design a distributed protocol where multiple eVTOL operators 'negotiate' vertiport slot timings based on priority, constraints, and time-to-landing — like blockchain gas bidding meets aviation.
Build a microservice that can accept a live stream of incoming eVTOL coordinates and dynamically reroute based on safety zones, congestion, or predicted conflicts.
Build a routing engine that determines when cargo should switch from drone → van → foot delivery for last-mile optimization, including battery, time, and urban zone limits.
Train a small model (XGBoost, KNN, or transformer-based) to predict future flight conflicts based on current coordinates, speed, altitude, and past airspace data.
Design a tamper-proof event log of all UAM operations (landings, slots, handovers, etc.) to enable compliance/auditing across competitors. No heavy chain — use Merkle-style hashes.
Build a model to dynamically price landing/takeoff slots based on congestion, demand, operator tier, and real-time usage.
Build a routing system that includes real-time battery drain (based on load, headwind, elevation) and recommends path adjustments or emergency diversions.
Design an admin dashboard supporting at least 3 real-time views: Pilot Console, Ground Control, Emergency Override.
Build a serverless cloud function that detects and resolves mid-air conflict events using rule-based separation logic.
Design an API standard that two vertiports can use to sync flight events, handovers, emergencies, and schedule adjustments — think 'airport-to-airport socket.'
Build a system that records every flight's movement, decisions, and comms — and lets operators 'replay' events to review safety and errors.
Build a simplified digital twin of a drone/eVTOL and simulate real-time data — telemetry, pitch, yaw, battery — with remote control inputs.
Design a system that handles multi-party disagreements: e.g., 2 operators claim the same landing pad. Build dispute resolution workflows with timeouts, logs, and arbitration.
Given a city model + air corridor specs + weather feeds + real estate zones, build a dynamic 3D map showing safe vs. blocked vs. high-risk fly paths.
Design a system that receives hundreds of sensor-level inputs (weather, drone health, traffic) and ranks them by criticality using AI — so that only the most urgent ones reach the pilot/controller.
Create a DID (Decentralized Identity)-inspired login/access mechanism for pilots, drones, and command terminals. Enforce role-based policies with revocation, without a central server.
Design a system where multiple EVTOLs can share telemetry data with each other (peer-to-peer style), useful for flight safety when ATC is offline.
Build a routing logic layer that activates when GPS fails or network loss occurs — fallback instructions + nearest safe landing zones.
Design a time-efficient schedule for charging, unloading, cooling, and preparing drones on the ground — similar to airport tarmac ops for UAVs.
Build a rules engine that checks proposed flight paths or schedules against government rulesets (e.g. FAA, DGCA, DGIS) and flags violations.
Build an AI system to schedule and route eVTOL flights dynamically based on weather, air traffic, and charging infrastructure availability.
Design a digital twin prototype of a vertiport terminal — mapping infrastructure, drone pads, passenger flow, and battery swaps — in real-time 3D.
Design a blockchain-inspired or Web3-based identity/pass & payment system for passengers using eVTOLs.
Build a 3D interface to visualize designated air corridors for urban drone traffic in a metro city — with zoning, noise heatmaps, and congestion overlays.
Design a lightweight system to handle air cargo pickup, drone routing, delivery, and package tracking — including weight, energy usage, and time to destination.
Create a system for drone-based medevac or emergency supply drops — auto-deploy based on API triggers (earthquake, crash, etc.).