/Raphael Reimann (3)

/List /Grid /Slideshow /Metadata
CAN 2017 – Highlights and Favourites

As 2017 comes to a close, we take a moment to look back at the outstanding work done this year. From spectacular peformances, large scale installations, devices and tools to the new virtual spaces for artistic exploration – so many great projects are being added to the CAN archive! Here are a just few, 25 in total, that we and you enjoyed the most this year.

22/12/2017
Features / Members
Who Wants to be a Self-Driving Car? – Empathising with self-driving vehicle systems

Created by Joey Lee (US), Benedikt Groß (DE), and Raphael Reimann (DE) from the moovel Lab, in collaboration with MESO Digital Interiors (DE), Who Wants to be a Self-Driving Car? is a data driven trust exercise that uses augmented reality to help people empathise with self-driving vehicle systems. The team built an unconventional driving machine that lets people use real-time, three-dimensional mapping and object recognition displayed in a virtual reality headset to navigate through space.

11/10/2017
Members / Unity / vvvv
❮ OpenDataCam 2.0 – An open source tool to quantify the world ❯

OpenDataCam is a open source tool to quantify the world. It consists of a camera attached to a mini computer that is running an object detection algorithm that counts and tracks moving objects.

❮ CAN 2017 – Highlights and Favourites ❯

As 2017 comes to a close, we take a moment to look back at the outstanding work done this year. From spectacular peformances, large scale installations, devices and tools to the new virtual spaces for artistic exploration – so many great projects are being added to the CAN archive! Here are a just few, 25 in total, that we and you enjoyed the most this year.

❮ Who Wants to be a Self-Driving Car? – Empathising with self-driving vehicle systems ❯

Created by Joey Lee (US), Benedikt Groß (DE), and Raphael Reimann (DE) from the moovel Lab, in collaboration with MESO Digital Interiors (DE), Who Wants to be a Self-Driving Car? is a data driven trust exercise that uses augmented reality to help people empathise with self-driving vehicle systems. The team built an unconventional driving machine that lets people use real-time, three-dimensional mapping and object recognition displayed in a virtual reality headset to navigate through space.