Improving driver monitoring systems: The case for synthetic data

6 min read


Driver monitoring systems (DMS) that assess alertness behind the wheel are rapidly becoming the leading automotive safety feature across the globe. In the EU for example, vehicle safety regulator EuroNCAP is requiring all new cars to incorporate a DMS in order to comply with its safety rating.

Amidst this push, startups are benefiting from business opportunities in the DMS space, offering solutions that range from heartmetrics to onset sleep detection. Among them, Swedish Devant is tapping the potential of synthetic data.

Launched in 2021, the startup generates synthetic data of lifelike digital humans to support the training, validation, and testing of machine learning networks — such as the ones behind driver monitoring systems. Specifically, it develops 3D simulated humans that are diverse in both appearance and behaviour across different situations

But how exactly can synthetic data improve DMS? TNW spoke with Richard Bremer, Devant’s co-founder and CEO, to find out more.

The gap synthetic data can fill

Interest in synthetic data started in the early 1990s, and it didn’t take long for the tech industry to realise the technology’s value in accelerating machine learning.

The automotive sector was one of the first proponents of synthetic data, adopting it in the mid-2010s for the development of autonomous vehicles, advanced driver assistance systems (ADAS), and most recently, DMS.

table with driver opinions about distracted driving in the EU in 2019