Free Space Detection Using Lane Segmentation
Free space detection is a key perception task in autonomous driving. It determines the drivable area by identifying where the vehicle can safely move. One common approach uses lane segmentation — detecting lane markings and then extracting the region between them as free space.
Here’s a video that walks through this concept:
🚗 What Is Lane Segmentation?
Lane segmentation refers to classifying each pixel in an image as:
- Lane marking, or
- Not lane marking
This creates a segmentation map where the lanes are clearly identified. Once the lanes are located, the region between left and right lanes — and sometimes extending forward — can be treated as free space or drivable area.
Unlike simple edge detection, lane segmentation uses deep learning models such as:
- U‑Net
- DeepLab
- ENet
- Fast‑SCNN
These models provide pixel‑level labels, allowing more robust and accurate lane detection even under shadows, wear, and complex road scenes.
🔍 How Free Space is Extracted
The process generally follows these steps:
1. Input Camera Image
A forward‑facing camera captures the road.
2. Preprocessing
The image is resized and normalized to match the segmentation model’s input.
3. Lane Segmentation
A neural network produces a binary mask of lane regions:
Lane Mask:
1 → lane marker
0 → background
4. Lane Boundary Detection
Using the mask, extract left and right lane boundaries by:
- detecting connected components, or
- using geometric methods (e.g., sliding window, polynomial fitting)
5. Region of Interest (ROI)
Focus only on road regions — removes sky and non‑road areas.
6. Free Space Polygon
Construct a polygon that spans between the left and right lane boundaries and extends to the bottom of the image. This region corresponds to the drivable free space.
🧠 Why Lane Segmentation Works
Unlike classical lane detection (edge + Hough), segmentation:
- captures curved lanes
- is robust to shadows/lighting changes
- can handle multiple lane markings
- supports dense semantic understanding
This makes it ideal for free space detection in urban and highway scenarios.
🛠️ Typical Neural Networks for Lane Segmentation
| Model | Characteristics |
|---|---|
| U‑Net | Encoder–decoder structure; good for dense per‑pixel masks |
| DeepLab | Atrous convolutions capture multi‑scale context |
| ENet | Efficient, real‑time segmentation model |
| Fast‑SCNN | Lightweight, optimized for fast inference |
🧠 Extensions: Combining with BEV
For a more robust perception pipeline, lane segmentation can be fused with Bird’s Eye View (BEV) representations or LIDAR data to detect free space in both image and 3D space. This improves:
- obstacle avoidance
- path planning
- driving policy control
🧠 Summary
Free space detection using lane segmentation is a powerful and practical method in the autonomous driving stack. By segmenting lane markings and extracting the region between them, vehicles can identify safe drivable space even in challenging environments.