Update README.md

README.md

@@ -8,4 +8,19 @@ tags:
 - adas
 - semantic-segmentation
 - obstacle-detection
----
+---
+## SceneSeg
+Self-driving cars are usually trained to detect specific object types, such as cars, pedestrians, buses, etc. 
+Such approaches are prone to failure cases when a self-driving car encounters an unusual object that it hasn't seen before, 
+e.g. a rickshaw, or if a self-driving car encounters a strange presentation of a known object, e.g. a cyclist that has fallen over. 
+In these scenarios, self-driving cars are unable to detect these critical objects leading to dangerous driving outcomes. 
+To address this, we have developed SceneSeg, a neural network that is able to segment all important foreground objects, 
+irrespective of what that object is. SceneSeg is able to implicitly learn the visual features of foreground objects such as cars, 
+buses, vans, pedestrians, cyclists, animals, rickshaws, trucks and other similar objects, even though it has not been explicitly 
+trained to detect these object types. SceneSeg is also able to detect objects that are outside of its training data, such as tyres 
+rolling down a highway, or a loose trailer. SceneSeg can also detect objects in unusual presentations that it hasn't seen during training. 
+SceneSeg performs robustly across challenging weather and lighting conditions, including during heavy rain, snow and low light driving. 
+SceneSeg performs out of the box on roads across the world without any parameter tuning. SceneSeg provides self-driving cars with a core 
+safety layer, helping to address 'long-tail' edge cases which plauge object-level detectors. 
+
+<img src="https://github.com/autowarefoundation/autoware_vision_pilot/blob/main/Media/SceneSeg_GIF_Rain.gif">