I built a real-time Drivable Area Segmentation model for Indian roads (Here is how it runs at 55 FPS)

#deeplearning #machinelearning #performance #showdev

Hey everyone! I recently hit a major milestone in my journey of building autonomous vehicle tech, and I wanted to share the raw behind the scenes process.

If you have ever driven in India, you know the reality. Lane markings are more of a suggestion, and often they do not even exist. Standard ADAS (Advanced Driver Assistance Systems) that rely on crisp white lines completely break down in our unstructured traffic.

To solve this at PerceptionAV, we had to rethink the foundational question of self driving: How does the car know where it is actually safe to drive?

Instead of looking for lanes, I built a Drivable Area Segmentation pipeline.

The Core Problem

Most open source datasets are heavily biased toward structured Western roads. Training a model to just "find the lane" results in a fragile system. We needed a model that looks at the chaotic blend of tarmac, dirt patches, and broken curbs and simply highlights the "free space".

What I Built

I put together a real time computer vision pipeline that processes live video feeds and overlays a solid green mask strictly over the drivable surface. The goal was speed and accuracy, because a self driving system cannot afford latency.

Here are the current specs of the run:

Frame Rate: Holding steady between 50 and 55 FPS.
Compute: Running on an RTX 4050 (6GB VRAM) using CUDA acceleration.
Focus: Strictly identifying safe road surfaces while ignoring the barrage of two wheelers, buses, and pedestrians.

The Tech Reality

Getting this to run smoothly on desktop GPU hardware is step one. The real challenge we are tackling next is optimizing this architecture for edge devices. Moving from a dedicated GPU down to a Raspberry Pi 5 or an affordable edge tensor unit changes the entire game. We have to look at aggressive quantization and lightweight model architectures to keep that 30+ FPS threshold on device.

What is Next?

Finding the free space is just the beginning. The road map involves layering in dynamic object detection specifically for Indian traffic nuances like auto rickshaws and stray animals. I am also exploring monocular depth estimation to calculate the actual distance to the edge of the mask without relying on expensive LIDAR setups.

I need your thoughts!

Have any of you tackled severe model quantization for computer vision on edge devices? I would love to hear what frameworks or deployment tricks you recommend when moving off a dedicated GPU.

Drop a comment below and let us talk shop.