Screen and camera – that's all you need for visualisation. Markus Peier, Julian Lerch and Korbinian Kunst, doctoral students and research assistants at the Lab of Adaptive Lighting Systems and Visual Processing, have set up an experimental setup. If a person enters the camera's field of vision, they appear on the computer display – framed by a red frame and supplemented with information on the percentage probability that the object is a human. An AI-supported system developed at the lab makes this recognition and visualisation possible for automated vehicles in road traffic.

“Not only people are detected, but objects in general,” emphasises Peier. The project on light-based object detection, headed by Professor Tran Quoc Khanh, deals with several aspects of research at once: What must the specifications of a camera be in order to be able to recognise objects or people at all, even in the street at night? What pixel size and resolution must it have, and what gain, exposure time and aperture must be set in order to deliver reliable results? And how can drivers or automated driving systems be supported in detecting in the dark?

What is good visibility?

The researchers have set up a spotlight consisting of 25,600 microLEDs. “The headlights of the future are digital, computer-controlled projectors rather than lamps,” explains Kunst. This projector is linked to the high-resolution camera in the centre of the vehicle windscreen. If an object is detected with only a low probability, it can be specifically illuminated by the large number of pixels in order to increase visibility for the AI system, but also for people, as he explains. However, it needs to be clarified how good visibility is defined for people and cameras.

Kunst emphasises the interdisciplinary approach: among other things, psychology plays a role in determining what contrasts people need for reliable detection, as does the question of what influence driving speed, object dynamics, position or size of the object have.

Communication without gestures

However, adaptive light-based systems can be used not only for detection, but also for communication in the world of automated and autonomous driving. Markus Peier describes a possible situation: if there is no longer a driver in the car, communication with gestures is no longer possible. “But how do I then communicate who is allowed to drive first at a blind spot, for example? What symbols can be used instead, and where should they be visible?

In addition, the signs must also be internationally understandable,” he emphasises. It would be conceivable to use symbols to indicate the driving status or warnings with stop symbols on a display in the front of the vehicle's radiator.

The automotive industry already has ideas on all of these issues. “But development is still in its infancy,” says Peier. The TU research team is using artificial intelligence specifically for its solution approaches.