# Concepts and definitions
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This section describes key concepts and defines terminology specific to this solution:

 **AV/ADAS** 

Autonomous Vehicle/Advanced Driver-Assistance System. Developing and deploying AV/ADASs requires scalable compute, storage, networking, analytics, and deep learning frameworks.

 **DAG** 

Directed Acyclic Graph. Workflows in Amazon MWAA are authored as DAGs using Python.

 **drive** 

Logical grouping of data, such as `Test Car 1 stores its rosbag file on Drive 1`.

 **lane detection (LaneDet)** 

A specific object detection model to identify automotive roads within images.

 **object detection** 

Refers to identifying objects within an image pulled from the rosbag file. Based on the [COCO dataset](https://cocodataset.org/) for object detection, this term implies the detection of recognized objects such as street lights, stop signs, and people.

 **ROS** 

Robot Operating System. The ROS is a set of software libraries and tools that help you build robot applications.

 **rosbag** 

A ROS archive file containing sensor data, meant for playback and logging.

 **scene detection** 

See **object detection**.

 **YOLO** 

You Only Look Once, a PyTorch object detection model.

**Note**  
For a general reference of AWS terms, see the [AWS Glossary](https://docs.aws.amazon.com/general/latest/gr/glos-chap.html).