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1. Built a satellite connection experience, visualizing signal strength through real-time radar scans and dynamic feedback.
2. Designed an intelligent tire pressure monitoring experience that detects real-time changes, surfaces contextual alerts, and helps drivers take timely action.
3. Designed an AI-powered navigation and voice control experience that turns the car into a smart co-driver, understanding commands, optimizing routes in real time, and reducing driver distraction.
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Dragon Eye: 4K Neural-Link HMI (Rive)
Description: High-performance Vehicle HMI with adaptive AI telemetry. Built in Rive using complex Lua scripting, 3 custom Node Scripts, and ViewModel architecture. Features automated structural safety speed-limiters and oxygen-depletion altitude overrides. Engineering meets survival. 🐉🚀
https://vimeo.com/1178627971?share=copy&fl=sv&fe=ci
DOCUMENTATION: DRAGON EYE – NEURAL-LINKED HMI SYSTEM
Project Overview
Dragon Eye is a high-stakes Vehicle Human-Machine Interface (HMI) built entirely within the Rive engine. It simulates a biological-mechanical bridge between a pilot and a dragon-class flight engine. This project represents 80+ hours of architectural engineering, focusing on real-time data flow, safety-critical overrides, and immersive visual feedback.
TECHNICAL ARCHITECTURE
1. The Logic Layer (Lua Scripting)
The system is powered by a custom-coded backend using 3 Node Scripts and 2 Listener Action Scripts. This setup handles:
Asynchronous Telemetry: Real-time calculation of velocity, altitude, and energy consumption.
Contextual AI Feed: A "Synaptic Feed" that pushes dynamic messages to the HUD based on the current flight state.
2. ViewModel & Data Flow
Utilizing a ViewModel architecture, the project ensures a single source of truth for all UI elements.
Range Map Converters: Translate raw user drag-gestures into precise engine-velocity increments.
Synchronized State Machine: Ensures seamless transitions between Idle, Cruise, Turbo, and Combat modes.
SAFETY-CRITICAL PROTOCOLS (The "Guardian" Logic)
To elevate the HMI from a visual tool to a survival system, I implemented two major safety overrides:
Automatic Structural Protection (Speed Limiter):
The system continuously monitors mechanical stress. If the pilot exceeds the structural safety threshold (550+ units), the Neural Override triggers an immediate, automated deceleration to prevent catastrophic airframe failure.
Atmospheric Reclamation (Oxygen Protocol):
The AI monitors the rider’s biological vitals. If oxygen levels drop below 15% due to high-altitude flight, the system initiates a forced descent to a "breathable zone" (lower altitude) to prevent pilot hypoxia, overriding manual controls until vitals stabilize.
VISUAL & COMBAT SYSTEMS
Weaponry Interface: Custom-coded weapon systems featuring synchronized flicker effects and dynamic target-locking scaling.
Adaptive HUD: The interface scales and shifts based on G-force simulation, providing a 1:1 responsive experience.
Audio-Visual Synergy: Designed to sync with high-tempo, adrenaline-driven soundscapes for maximum pilot immersion.
Lead Engineer: Istvan Rac
Framework: Rive (Lua / State Machine / ViewModels)
Industry: Vehicle HMI / Aerospace Interface Design
80+ hours in Rive with Lua scripting and state machines — Dragon Eye is on another level entirely!
My submission for the Rive Vehicle HMI challenge. Inspired by Mini Cooper OS 9 Vehicle HMI which features a circular OLED screen. Distinct, playful and technically unique. In the video, I give a break down of how I built it in Rive.
A circular OLED HMI inspired by Mini Cooper OS 9 — this concept is absolutely brilliant!
As someone who appreciates HMI and vehicle interfaces, really blown away by some of the other work and what is possible with Rive.
I have been meaning to try building something out for a while so this was the perfect opportunity. This is a pretty simple interaction - based on Capsule Corp from the Dragon Ball Universe, just showing the transition from capsule state to a vehicle with dynamic controls for each view.
Definitely things I would love to experiment with more and already could improve on but just a quick bit of exploration!
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