Driva

Analytics Full-stack Platform Visualization Web Mobile IoT Cloud
Driva

An exploration of how to translate vehicle telemetry into driver-facing feedback. The project focuses on turning raw automotive signals into interpretable metrics that support safer and more efficient driving.

Modern vehicles produce large volumes of telemetry, but raw measurements are not the same as insight. The hard part is translation: turning a stream of signals into feedback a driver can understand and act on. This project explored that translation, with an emphasis on clarity and trust.

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. With careful engineering, even low-level signals can become practical feedback 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: