Why should you seriously consider synthetic data to train and validate in-cabin monitoring systems? What are the advantages of synthetic data versus real-world data to train these systems? And why are many DMS/OMS developers already implementing synthetic data in their data generation pipelines?
There are several in-cabin monitoring use cases, and likewise, they have different data needs, hence, different data challenges that you need to overcome if you want to successfully train the deep learning models behind the systems.
Save the date! This May 10-12, 2022, Anyverse, will be at the AutoSens in Detroit! AutoSens is the world’s foremost meeting of automotive engineers working to improve automotive imaging and vehicle perception for production vehicles.
Whether it’s DMS, OMS, or any other interior camera system, acquiring data to develop in-cabin monitoring systems is challenging. But… Why is that? Why is acquiring real-world data particularly hard for the in-cabin monitoring use case?
The University of Warwick and Anyverse have just started what we hope will be a long partnership in the field of autonomous driving perception systems. Our first joint research project objective is to compare the performance and results of an autonomous driving AI model when training and validating it with real-world data and highly accurate synthetic data.
How to generate accurate long-range detection data to train and validate autonomous vehicles has been challenging for developers since the very beginning of autonomous transportation.
It may be time to give synthetic data a try, but not just any synthetic data… pixel-accurate synthetic data capable of mimicking the behavior of your self-driving system in the real world.
If developing and validating autonomous driving systems wasn’t already hard enough… having inaccurate data could make your life even harder.
Talking about pixel-accurate, synthetic data is talking about safety enhancement and trustworthy data to develop accurate autonomous driving systems.
Many deep learning models struggle to see the relationships between objects in a scene, but the machine learning model MIT researchers have developed brings machines one step closer to understanding and interacting with the scene environment, just like humans would do…