![]() ![]() Huge amount of rich and diverse labelled data is the most precious asset required for training and validation of autonomous vehicles. In order to get autonomous vehicles quickly on public roads, huge training data is required, and the current shortage of it, is the biggest challenge. RECTLABEL PRICE VERIFICATIONThis ingested data is labeled and processed to build a testing suite for simulation, validation and verification of ADAS models. In the ADAS development process, a high volume of data is acquired from the test fleet through the cameras, ultrasonic sensors, radar, LIDAR, and GPS, which is then ingested from the vehicle to the data lake. Data annotation or labeling of objects play a vital role in this process by automating and fast tracking the process.Īnnotation is the process of labeling the object of interest in the image or video by using bounding boxes to help AI or Machine Learning models understand and recognize the objects detected by sensors. This could only be attained by rigorous testing and validation under enormous datasets including multiple scenarios. Moreover, envisaging the path of moving entities is determined as the next most important ability to be acquired by highly automated vehicles. The vehicle with high automation should be trained enough to track, classify, and differentiate the objects in the vicinity with an aim to decide its course of action. Perception is the basis for a vehicle to be able to drive itself (without a driver). The launch of Audi’s A8 (featured with level 3 functions) that reached deployment stage in early 2019, had increased the confidence among the majority of OEMs and tier-1s however, the level 4 and 5 vehicles still need enough time and testing to get on public roads. However, these vehicles have reached the deployment stage only in restricted operation design domains (ODDs). With the massive advancement towards the development of autonomous driving systems, no one today denies or questions the practicality of the driverless vehicles. ![]()
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