Why Do Autonomous Vehicles Need Data Annotation ? 

The entire system of Artificial Intelligence has managed to catch hold of the common person’s attention because of the ways that it has and it continues to revolutionize the Driving experience of humans. Today, completely automatic driving or driverless cars are not far from reality. But it has not been a sudden leap forward, rather a gradual experience of automated transportation. These 5 levels of autonomous driving play a pivotal role:- 







Let us now visit these levels on at a time:- 

Level 0: No Automation 

Level 0 is accredited as the level where the driver effectively is in full control of the car without any support from a driver assistance system. 

Level 1: Driver Assistance 

Level 1 is the level of an autonomous vehicle wherein an automated system is installed on the vehicle that can at times, help the human driver to conduct some parts of the driving task. In fact, many top-of-the-range car models offer level one automation. A human being is still the driver of the car with full-control on driving but, the driver can utilize technology like adaptive cruise control for active safety. At this level, a computer can control either steering or acceleration or braking, but it is not programmed to do everything simultaneously. 

Level 2: Partly Automated Driving 

At this level, mechanisms that make partial automation possible are already in practical usage. Semi-autonomous driving assistance systems, such as the Steering and Lane Control Assistant including Traffic Jam Assistant, ensure that daily driving experience becomes much more comfortable. They can brake, accelerate automatically, and, unlike level 1, take over steering. With the remote-controlled parking function, it's possible to pull into tight spots without a driver for the first time. The salient features of level 2 automation are: 1. Adaptive Cruise Control i.e. adapting speed automatically 2. Autonomous Emergency Braking Lane Detection 

3. Lane Keeping Assist 

4. Parking Assistance Parking Line Detection 

Level 3: Highly Automated Driving 

In the third development stage, drivers gain more freedom to completely turn their attention away from the road under certain conditions. In other words, the technology will be powerful enough that the driver can hand over complete control to the car. The driver, however, must be able to take over control within a few seconds. This implies that automation is only momentarily possible at this level. 

Level 4: Fully Automated Driving

This level is attributed to a stage where full autonomous driving is enabled, although a human driver can still request control, and the vehicle still has a cockpit. In level 4, the vehicle can handle the majority of driving aspects on its own. The technology in level 4 has evolved to a stage where the vehicle can handle highly complex driving situations without any human intervention. The human being, however, is expected to remain fit for driving and capable of taking over control of the vehicle if required. If human beings ignore a warning alarm, the car has the authority to move into safe conditions, for example by pulling over. 

Level 5: Full Automation (Driverless) 

Full Automation or level 5 is where true autonomous driving is achieved. In this stage of automation, everyone in the car is a passenger. Here drivers don’t really even need to have a license and the system of automation is completely on its own, i.e. without the understanding or intervention of a human being. Just like level 4, this level also is only possible theoretically for now. 


● The number of autonomous vehicles is predicted to significantly increase in the near future. By 2023 it is predicted that more than 740,000 cars with autonomous driving capabilities will be added to the market and a vast majority of this growth will largely be driven by countries such as the United States of America, China, and others situated in Western Europe. Despite this growth, these vehicles are still

predicted to have limited autonomous capabilities and still operate under human supervision. 

● In order to develop their autonomous capabilities, algorithms that drive these vehicles are expected to be trained to detect, track, and classify objects and make informed decisions for planning of a specific path and safe navigation. Billions of incident free miles are supposed to be clocked in the Autonomous Vehicle system so that a certain acceptable level of safety norms can be achieved. The overall transition of a vehicle from level 2 to level 3 of automation, is expected to be exponential. This is due to the increasing complexity of such vehicles with time, the increased use of multiple sensors, and most importantly, the petabytes of raw data captured continuously by a fleet of Autonomous Vehicles. 

● Generally, annotation is referred to as the process of labeling the object of interest in the image or video by using bounding boxes to assist AI or ML models understand and recognize the objects detected by sensors. In a developmental sense, a high volume of data is acquired from the test mechanisms through cameras, ultrasonic sensors, radar, LIDAR, and GPS, which is then ingested from the vehicle to the data lake. This ingested data is then thoroughly labeled and processed in order to create a testing mechanism for simulation, validation and verification of automation models. 

● Huge amount of training data is required for autonomous vehicles to be operational on roads, and the current shortage of this training data is the biggest challenge which is why data annotation in Autonomous Vehicles becomes even more important. A large amount of rich and diverse labelled data is the most precious asset required for training and validation of autonomous vehicles. Ground annotation includes a collection

of information based on location which enables the image data to relate to the real situation on ground. This annotated data is crucial for training and validating the perception and prediction models with high precision. 

● In order to obtain the necessary volume of data to feed into the system and train the ML algorithms, researchers need to have thousands and thousands of images annotated. This could potentially vary from simple 2D bounding boxes to relatively complex annotation methods, such as semantic segmentation. Even though automation companies such as Waymo, Argo.ai and Tesla have already amassed a ton of such annotated data, it is still enough for AI companies to overcome these challenges. 

● For autonomous vehicles, ground-truth labeling helps in annotating urban situations, highway environments, road markings and signboards, and different weather conditions that enable an efficient training of the vehicle and smooth detection of moving objects. Labelling this huge dataset requires significant resources, time and money and is largely done manually. 

● A number of automation software tools, and labeling apps have come up recently to provide frameworks that possess the ability to create algorithms for automating the entire labeling process and ensuring precision and safety simultaneously. Some of the more prominent automatic annotation tools are Amazon’s SageMaker Ground Truth, MathWorks Ground Truth Labeler app, Computer Vision Annotation Tool (CVAT) developed by Intel, Visual Object Tagging Tools (VoTT) by Microsoft, DataTurks, LabelMe, Fast Image Data Annotation Tool (FIAT), COCO Annotator and Cloud-LSVA.

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