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Now-a-days, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology. ADAS uses the technology like radar, vision and combinations of various sensors including LIDAR to automatize dynamic driving tasks like steering, braking, and acceleration of vehicle for controlled and safe driving. And to integrate these advance technologies, the ADAS needs labeled data to train the algorithm to detect the various objects and moments of driver. Image annotation is one the well-known service to create such training data for computer vision. There are number of open source annotated datasets available viz. COCO, KITTI etc.

ADAS and AV technologies are going to disrupt the entire transportation industry, as we know it, with a profound impact on human life. They promise to enhance human lives by providing a safer and much more accessible transportation ecosystem to all of society. However, to deliver on all of its promises, they need to be at least as good as a ‘good’ human driver. Therefore, they need to be very safe and robust, with the ability to perform in a variety of driving scenarios, and be very secure, being immune from any external cyberattacks. Hence, such technologies need to be tested very extensively. However, from various studies, it has been found that, to declare a full AV as good as a human driver, the AV will be required to drive more than a billion miles on real roads, taking tens and sometimes hundreds of years to drive those miles, considering even the most aggressive testing assumptions.