Creating trust in mobility with novel collaborative driving solutions

We built a research platform together with Veoneer to learn quickly if internal ideas and/or external supplier tech is worthwhile pursuing further into product development.


Veoneer and Techno Creatives continuously explore selected areas for ideating new products, services, and features. With the LIV platform, the feasibility of the idea as well as the user response can be quickly tested and use as a basis for continued investment or not.


The main purpose is to learn quickly and bring forward a range of new services instead of making heavy investments into a few bets with long product development cycles.

Sensing behaviour and context in order to make sure the passengers understand future moves.

The LIV platform was leveraged to generate tests around a set of different algorithms for monitoring physiological and cognitive states (e.g. drowsiness, stress, cognitive load, distraction, etc.) that impact human performance, safety, well-being. After initial testing, enough data was provided to decide on which route to pursue with selected milestones.

Showcasing how driver profiles can be developed over time based on accumulated sensor data

In the future, driver profiles can be used to trigger system adjustments allowing for personalised collaborative driving features, adapted to each individual driver’s profile. In turn, improving both safety and convenience for the driver.

Techno Creatives designed and developed an interactive display showcasing data visualisations from 10 individual drivers, based on a range of sensor data collected and accumulated over time. The focus resided in how such data insights can be leveraged for future product and system development.

Leveraging in-car voice assistants to improve and facilitate driving experiences.

This could be used for driver’s intent to the relevant service, tasks like playing media, receive alerts or even closing the garage door. Techno Creatives leveraged the LIV platform to make a thorough analysis of eight available voice assistants through ten different languages. The focus resided on string distance and intent matching to select the strongest technology for further development.