Uses And Future Of Polyline Annotation

Artificial intelligence promises to transform the way people travel in the coming years. AI is already present in numerous products and is being increasingly adopted by large corporations. There are significant challenges to the use of automated vehicles for consumers across the globe, but serious progress is being made.

AI algorithms that drive automated vehicles are able to interpret and navigate their environment because they are trained with annotated video and image data. Human operators label roads and traffic in real-time, allowing car AI systems to operate better and drive policies. This blog will focus on one practical and very important type of annotation that’s used in automated vehicles: polyline annotation.

What is polyline annotation?

A technique that is used to define linear structures in images and video. Our roads trace the outline of roads, and our train tracks trace the outline of train tracks, using perpendicular lines connected to vertices. Annotators use these lines or even place them within images using online tools. For video training data, you must pick specific areas out of each frame.

Different ways through which polyline annotation supports automated vehicles

Using polyline annotation, you can teach a machine learning model the exact scale and space relationship of the different objects the vehicle travels over. The technique enables the following functionalities:

Lane detection is a core capability for all autonomous vehicles due to its importance in keeping autonomous vehicles centered and in the correct position in the lane. AI is also a super-powerful technology, one of the most advanced, which then helps computer vision models know what to do next when navigating across multi-lane highways. To build models that accurately predict driving behavior, those polylines must be properly marked.
It helps in navigating traffic laws as different road markings indicate different traffic laws. If the driverless car is to stay out of emergency situations, like getting stuck in a parking lot. It’s possible to train a system that highlights the appropriate road markings and important information on them.
Lane lines are crucial for vehicle safety. It can help autonomous cars avoid collisions and not be a road hazard by making them safe in designated lanes. Drivers must never stray from their assigned lanes or lanes that are clearly marked for passing.