Driva

Analytics Full-stack Platform Visualization Web Mobile IoT Cloud
Driva

An exploration into humanizing vehicle telemetry. The project addresses the challenge of translating vast streams of raw automotive data into meaningful, intuitive experiences for drivers, moving beyond mere numbers to provide actionable insights for safer and more efficient mobility.

Modern vehicles produce vast streams of data, but raw information is not the same as insight. The real challenge is not capturing this telemetry, but translating it into a language that is meaningful to a human driver. This project was an exploration of that translation, looking at how to turn complex signals into an intuitive story about a person’s driving habits and behaviors.

The core of the work was in designing a system as part of Magna’s SmartBridge™ suite, that could bridge the gap between machine-generated data and human understanding. This required a robust architecture capable of processing and orchestrating real-time data, but the focus was always on the end experience. The goal was to deliver clarity, helping people see not just how far they drove, but how they drove—safely, efficiently, and sustainably.

To maintain coherence across a complex, multi-platform system, a key principle was establishing a single source of truth for communication between services. By automatically generating typed SDKs for the Flutter mobile app directly from the backend’s API specifications, we could ensure that the different parts of the system spoke the same language. This approach wasn’t just a technical convenience; it was a way to manage the inherent complexity of building a reliable, device-agnostic experience.

Ultimately, this project served as a proof of concept for a more human-centric approach to automotive IoT. It’s a demonstration that with thoughtful engineering, even the most technical data can be made practical and useful for everyday drivers.

Stack

While the problem is more important than the tools, the tech stack tells a story about the project's architecture and trade-offs. Here's what this project is built on: