Exigency of Standardization for Annotation Format in Advanced Driving Assist System (ADAS) Feature Development

Automotive industry is going through a massive digital transformation to enable advance ADAS functions like cruise control, safety and parking assist. To develop and test advance and complex deep neural network-based AI/ML ADAS models, the need of huge amount of rich and diverse annotated data is utmost important. Over the past decade it has been observed that annotation complexity has increased tremendously and evolved from a simple bounding box to complex annotations like segmentation, 3D bounding box, key points etc. that too with multiple sensor integration. Hence such stupendous annotation task cannot be executed inhouse unlike in the past, companies choose to outsource time consuming and labor-intensive task to third party vendors. Hence annotation becomes an additional and unexpected challenge in ADAS function development, which urge the need for standard annotation format. The overall approach, in this paper is to propose comprehensive simple and robust annotation structure and file format pertinent to all annotation types to overcome the current disparity industries are facing. In addition, in this paper we have presented different type of labelling methodology for camera, radar and Lidar sensor data which are being used for automotive drive data.