ADAS Prototype

Building a Custom ADAS Prototype Using Computer Vision for a Research Project

At Haloxion, we frequently collaborate on research-driven ideas that push technology beyond standard applications. For this project, our client needed a custom Advanced Driver Assistance System (ADAS) prototype to support their academic and experimentation needs. The goal was not to build a commercial system, but to create a flexible, research-grade computer vision setup that could detect objects, read traffic signs, and analyze road lanes in real time.

Understanding the Requirement

The client was exploring ADAS capabilities for a larger study. Their requirements included:

• Detecting vehicles and pedestrians
• Recognizing traffic signs
• Performing lane detection on roads
• Running everything through a simple, reproducible Google Colab workflow
• Using custom-trained YOLO models, not generic pretrained datasets
• Allowing easy inference on both images and videos

Our role was to design a solution that was accurate enough for research, yet easy for the client to experiment with and modify.

Our Solution-Finding Approach

We focused on modularity and clarity:

1. Custom Model Training

   We helped prepare datasets and trained YOLOv8 models — one for vehicles & pedestrians, and another for traffic signs — to ensure the system worked on the client’s specific needs rather than generic benchmarks.

2. Lane Detection Pipeline

   We implemented a lightweight lane-detection algorithm that complemented the object detectors, giving the client a complete vision pipeline.

3. Colab-First Workflow

   Since the project was for research, we built everything around Google Colab so the client could:

   • Run inference on images/videos
   • Retrain models if needed
   • Download results easily
   • Work without worrying about local hardware limits

4. Simplified Setup

   API keys and configuration steps were streamlined so the system could run with minimal effort — an important detail for a research team iterating quickly.

The Final Outcome

The final ADAS prototype delivered:

• Custom-trained YOLO models for better domain-specific accuracy
• Object detection for vehicles, pedestrians, and traffic signs
• Lane detection for both images and video
• A ready-to-use Google Colab notebook for inference, testing, and experimentation
• Exportable results and performance metrics for analysis

This gave the client a solid, practical research tool that supported experimentation, data collection, and model evaluation without the complexity of a full production ADAS system.

Why This Project Matters

This collaboration reflects Haloxion’s ability to turn advanced machine-learning concepts into working prototypes tailored for research, testing, and innovation.

Whether it’s ADAS, computer vision, or custom ML workflows, we help clients build systems that meet their specific goals.

If you’re exploring a similar idea or require a custom model for research or prototyping, we’d love to help.