Before we launch a product or service on the market at Zander Labs, we make a point of testing our technologies as realistically as possible.

At Zander Labs, we make it a point to test our technologies as realistically as possible.

Science in the lab often shows us what can be done, but science in the field shows us what really works. In this way, we can assess how our solutions react under the real conditions under which they must ultimately function.

Our quest to bridge the gap between science and industrial applications is driven by our commitment to maximizing the practical value and impact of our work. Our principle is to put our innovative ideas - which we rigorously test in the lab - into practice as quickly as possible to accelerate the path from idea to real-world application. To achieve this, we develop customized experiments and novel paradigms that go beyond traditional laboratory conditions.

Whenever possible and appropriate, we also use realistic simulations to test our technologies in scenarios that come very close to real-life conditions.

Take CARLA (Car Learning to Act), an open-source driving simulator developed for autonomous vehicle research. CARLA’s simulation platform supports flexible specification of sensor sets and environmental conditions, enabling full control of agents and vehicles, map creation, and more. By customizing CARLA to replicate critical real-world driving dynamics, we can refine our workload classifier in automotive scenarios.

CARLA's customization allows us to develop events that replicate real driving conditions with different workloads. By adapting pedestrian and vehicle behavior, we can simulate difficult scenarios such as sudden pedestrian crossings or unpredictable vehicle performance.

By adapting the driving conditions, we can actively modulate the driver's workload. Navigating in dynamic scenarios like these requires the driver to react quickly, which temporarily leads to a high workload. Evaluating the performance of our classifiers in these scenarios is crucial, and CARLA's simulated environments provide an ideal platform for such evaluations.

The inclusion of EEG data in these simulations allows us to validate the accuracy of our classifiers under ecologically valid conditions, while continuing to work under controlled lab conditions.

This method effectively bridges the gap between controlled laboratory conditions and unpredictable real-world scenarios, ensuring that our solutions remain robust and reliable even in dynamic environments.

In addition to automotive research, similar simulation software can also be used in other areas such as medical training. For example, surgical procedures can be simulated in VR environments to train surgeons in a risk-free scenario. Testing our technologies with such tools gives us a strategic advantage as we can refine our solutions under controlled but realistic conditions.

At Zander Labs, we are committed to rigorous testing in realistic simulations to ensure that our solutions bridge the gap between innovation and conditions outside the lab. By translating scientific insights into actionable, high-quality solutions that are thoroughly tested in real-world scenarios, we ensure that our technology is both reliable and transformative when it reaches users.

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