Driverless Cars The concept of movie-inspired automated vehicles sparked off the automobile industry with a great deal of enthusiasm to bring that not-so-easy-to-conquer idea into the realm. Notwithstanding what has just been said, we – the folks of the ongoing generation have a true testimony of self-driving cars before us. We are seeing automated cars drifting through driveways around us with no human soul occupying the driving seat.

Self-driving cars are indeed a marvel of technology and it’s beyond words witnessing a technology evolving before us which, not to mention, is transforming our lives, redefining our ways of living and paving the path for a sustainable future. And, blessed is experiencing the technology transformation in the time of our life.

What Brainpowers Driverless Cars

Seeing a car driving through a lane in the dark midnight with no one holding the steering might provoke horror into the nerves of many golden oldies, particularly those who have been alienated to the transforming automobile industry. However, nerds of the up-and-coming generation will get their minds filled with a sense of curiosity when seeing a car with a missing gear or shift lever switching on its own.

Is your inquisitive mind playing a chord of curiosity wondering about the technology that works behind to enable cars to drive on their own? Are you wondering about the mechanism that brain powers self-driving cars to cruise command free with no need of manhandling? If this is the case, we have this knowledge-induced note to give you some insight into the technology behind car automation. The note will give you an acquaintance of the data labelling/annotation process being executed at the back end that leads to AI training and machine automation.

How Data Labelling Aids Self Driving System

Data labelling is the core of the artificial intelligence (AI) integration and machine learning process. Both fully automated and semi-automated cars are hooked up with a suite of hardware assembly, e.g., cameras, sensors and various other motion-sensing devices to track down the objects on and around the driving lane.

ADAS system, for instance, is a computer vision mechanism that powers up a self-driving car with cognizance to intelligently recognize the environment on and around a driveway.

The annotation which is also said to be as the labelled data fed into a car through AI training makes it all possible.

How does Annotation Aid to AI Training and Machine Learning?

Like we all intelligent homo sapiens feed our brain with knowledge through textual, audio-visual learning since childhood to develop intelligence and consciousness of mind, machines, on the same token, are fed with data through AI training to develop machine learning. The plain truth of the course is that annotated data comes first and foremost in the view that adds up all to the functionality of an automated vehicle.

Annotation, in the simplest text and in context to self-driving cars, can be defined as the process of labelling possibly every minor to major element laying across driving lanes. This annotated data is further fed into the car's computer vision to identify the objects and to enable fail-safe cruising on the roads.

Annotation: Process & Types

Annotation, without exaggerating anything about it, is a great deal of time and resources. It is about assembling, aggregating, structuring and then labelling an enormous size of data to train AI which is to be integrated into a self-driving car.

Different types of annotations are used to label data for developing an AI-equipped automated mechanical model. Audio, video, image, and text annotations hold the front line of the primary types of annotation which further lead to more precise and industry-specific subcategories of annotations.

As far as the automobile industry is concerned, image segmentation, video annotation, 2D & 3D point cloud annotation are the ones on which self-driving cars and vehicles are largely hooked on. The labelled data fed into an automated car helps its computer vision with image classification, segmentation and target detection. This is how annotation powers up the AI of automated cars to induce intelligence in the mechanical system through machine learning.

Here are the three primary data annotation types which automobile industry take leverage of to power up their line of self-driving vehicles:

1. Semantic Segmentation

Semantic segmentation is undeniably the most utilised data annotation technique for developing datasets for AI training and machine learning. The segmentation technique is said to be holding the highest potential among its counterparts when it comes to procuring precision in the annotation.

Fair to say, segmentation enables data scientists and AI trainers to get a complete insight into the imagery on or around the driving lane — with every pixel detail reckoned up through point-to-point labelling. The data labelled through semantic segmentation enables self-driving cars to understand the real-world environment. Cars’ artificial intelligence is thus trained with accurately segmented data which further contributes to their course of automated cruising ensuring no muddles and mistakes while measuring the miles.

2. 3D Cuboids

Most vehicles are designed cuboid in shape and this annotation technique goes well on the purpose when it comes to defining vehicles steering across the driveways. The vehicles images enclosed in 3-dimensional boxes give self-driving cars a clear view of the vehicles moving to and fro along the route.

The data labelled in 3D boxes gives drivers less the clear interpretation of the surrounding environment allowing them to keep their course of the route right on track. All in all, the 3D annotation technique also plays a pivotal role in developing intelligence in driverless cars for fail-safe ferrying on the roads.

3. Polygons

Failing to identify objects of indefinite size and shape may leave a car’s automation in complete disarray. Seeing the intricacy of the road and transport system, such objects might be in abundance at places on and around the paths, pavements and parkways. Polygon, the most time-consuming annotation approach, comes in place to patch up the proposition of self-driving cars.

The technique, which combines 2D and 3D annotation, works well to infuse intelligence into cars’ computer vision with the accurate datasets of indefinite objects — which, in all sincerity, forearms the driverless vehicles with the right algorithm for safe and secure automation on wheels.

The Bottom Line

Self-driving cars are no more the talk of the future — we are seeing them on roads measuring the miles. However, the automobile industry aiming for expansion around automated cars will take time, almost a decade if fairly speaking, to get the ground. Come what may, the one thing that this age of  AI and machine learning has substantiated for sure is the significance of data — which, in all realms, is going to be even more significant with the evolution of self-driving cars.