Towards Automation of Reference Data Generation for ADAS/AD Functions Development ALiVA Framework FEV.io

Format:

Book

Conference/Event

Author/Creator:

Mardhekar, Amogh, author.

Contributor:

Hivarkar, Umesh N.

Mohod, Rucha

Pawar, Rushikesh

Shirudkar, Rohit

Conference Name:

International Automotive CAE Conference Road to Virtual World (2024-10-23 : Delhi, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

The advancements towards autonomous driving have propelled the need for reference/ground truth data for development and validation of various functionalities. Traditional data labelling methods are time consuming, skills intensive and have many drawbacks. These challenges are addressed through ALiVA (automatic lidar, image and video annotator), a semi-automated framework assisting for event detection and generation of reference data through annotation/labelling of video and point-cloud data. ALiVA is capable of processing large volumes of camera and lidar sensor data.Main pillars of framework are object detection-classification models, object tracking algorithms, cognitive algorithms and annotation results review functionality. Automatic object detection functionality creates a precise bounding box around the area of interest and assigns class labels to annotated objects. Object tracking algorithms tracks detected objects in video frames, provides a unique object id for each object and performs distance ranging.A unique feature of cognitive algorithms is the elimination of non-realistic objects of interests which appear in billboards or advertisements on buses/trucks. The framework also has a feature of event detection like overtaking scenarios or pedestrians/animals crossing the roads.Annotation review functionality is provided where assessment and correction of auto annotated data can be done manually. The results can be saved in standard file formats such as txt, csv, Json and open ASAM, ensuring compatibility across different systems.ALiVA replaces traditional annotation methods, thereby reducing the effort, the need for skilled resources and the time required to annotate large datasets. This eliminates human biases, manual errors and inconsistencies.ALiVA is validated for numerous customer requirements and offers a large amount and variety of data to quantify the benefits offered. Some of the distinguishing features are models and functionalities that are optimized for Asian road scenarios, which are typically characterized by very high road density. It is platform independent, adaptable to newer requirements, complements newer event definitions for data segmentation and works both in cloud environments for Data as a service and as a standalone desktop application

Notes:

Vendor supplied data

Publisher Number:

2024-28-0022

Access Restriction:

Restricted for use by site license