von der Ohe

Co-Founder, CEO


You come across an opportunity to positively impact societies around the globe maybe once in your lifetime. An opportunity to truly make a difference. Our unique approach to autonomous driving gives us this chance: redefining how people move in a better way. Building and shipping a product with this great team is what drives me day and night.


Launched Zoox’ first self-driving vehicle on public streets as leading Technical Program Manager in Silicon Valley, launched Amazon’s first Echo as leading Technical Program Manager on Device Software, Founded two (funded) mobility companies. M. Sc. Management Science and Engineering, Stanford University.


Co-Founder, VP Engineering


Building products together with an amazing team based on cutting-edge technology to serve a greater purpose and solve problems – for the people, for our planet.


Manager of an engineering team in Silicon Valley to build autonomous shuttles, built teams and various mobility products: electric race cars, e-motorcycles, light electric vehicles, electric passenger vehicles, including one of the most successful electric delivery vehicles in Germany. RWTH Aachen, Imperial College London.


Co-founder, Director Teledrive Experience


Vay is aiming to launch the first vehicle without a safety driver on public roads in Europe. This involves exciting engineering challenges, many of which have never been worked on before, ranging from autonomous vehicle technology, cybersecurity, backend, machine learning and safety-critical SW. Coming up with engineering solutions to these topics is something that I’m super excited to work on at Vay.


Team Lead at Microsoft, Senior Software Engineer at Skype. M. Sc. in Computer Science from Belgrade University.


Director of Engineering Operations


Working closely with people from different cultures and professional backgrounds (hardware, software, operations, etc.) gives me the chance to learn new ways of approaching projects, structuring teams, and setting up processes every day. The results of this incredible teamwork are hugely rewarding, and visible in each step of our product.


Part of the management circle at AUDI AG, responsible for the implementation of prototypes at early development stages of new products (innovation vehicles, concept and pre-series vehicles, show cars, design models, testing single parts, PoCs, 3D-printing)


Director of Safety


All my professional life I have been working for the protection and safety of people and the environment. I want to further provide safe and user-friendly automation systems for our future mobility needs.


25+ years experience in system safety at Conti., Mando, Bosch. Led the VDA working group in Germany to create ISO 26262 and influenced worldwide safety standardization. Author of several text books on functional safety.


VP - Business & Corporate Development


What drives me is to work on goals that have a big impact on society. Additionally, I wanted to work with the smartest and most innovative people in the tech world. That’s better at Vay than any other company I’ve spoken to recently.


Was CEO and chief growth officer at Quirk. Began his professional life at Morgan Stanley as a fixed income trader after studying economics and finance. Built the first startup incubator in Africa in 2002 and has been mentoring founders of technology startups for over ten years. He is an angel investor in software technology and holds board positions in some of these companies.


VP - Product Design and Brand


Building the future of mobility, which is sustainable and truly serving the needs of people, by creating a holistic experience, a relatable brand and shaping services that are going to make a difference to how we move within and between cities.


Head of Global Design at N26, Senior Lead Designer at IDEO, in-car interactions for Volkswagen.


Senior Principal Software Engineer


After having worked in autonomous robotics research for a long time in the Silicon Valley, I am thrilled to be working at a company that is finally bringing this technology into people’s everyday lives.


Tech Lead at Google Tango in Mountain View, Research Engineer at Willow Garage, yoga instructor since 2018.


Director of Software Engineering


Helping engineers to do their best and most important work. Elegance in software. Bringing ideas from books to real life and from one domain to another. Going from A to B fast.


Software Generalist. Maps and Mobility Geek (Lon, Lat not Lat, Lon). High Load at Yandex, Geo Analytics and Last Mile at HERE Maps, Mobility Platform at Daimler. Conway’s Law Enthusiast.




A car enthusiast, driven by cars, driving and technology.


Nursery school teacher. Driver at Skoda’s start-up Caredriver.

How to measure glass-to-glass video latency?

by Bogdan Djukic (Vay Co-Founder)


At Vay, we deeply believe that there is a better way to optimize utilization rate of car sharing and ride hailing services. We are building a unique mobility service which will challenge private car ownership and introduce a new way of moving in big urban environments. The underlying technology empowering this new approach is teleoperation (remote vehicle control). Vay will be the first company launching a mobility service which is capable of delivering empty vehicles to our customers at the desired pick up location and taking them over once the trip is over. Our vehicle fleet is remotely controlled by remote drivers (teledrivers) who can be at the same city, different city or even in a different country compared to our customers.

As you might imagine, one of our core modalities is ultra-low latency video streaming. In order to be able to safely control what essentially is a 1.5 ton robot at higher speeds on public streets without a safety driver, we need to be able to achieve glass-to-glass video latency below 200ms.

What is glass-to-glass video latency?

Glass-to-glass latency refers to the time duration of the entire chain of the video pipeline. From the moment a light source hits the CMOS sensor on the camera mounted on the vehicle to the final rendering of the image on the computer screen of the teledriver. All components in this chain (camera frame capture, ISP post-processing, encoding, network transmission, decoding, screen rendering) add a certain amount of delay to the feedback for the teledriver.

Depending how vertically integrated you are or how good instrumentation of the video pipeline you have, you might be forced to look at your entire system as a blackbox in order to measure with high accuracy glass-to-glass latency and include all contributors which affect the final latency number.

How not to measure glass-to-glass video latency

Like with any other measurement methodologies, there are more and less precise ways to do this. The usual method that many use is putting a high precision digital stopwatch in front of the camera and capturing a photo while the stopwatch is being rendered on the screen. This is quite a manual effort with low precision (due to the refresh rate of the stopwatch screen) and does not provide you with a good understanding of the glass-to-glass latency value distribution over time. Some other less scientific methods involve snapping your fingers in front of the camera and looking at the rendered image on the screen in order to have a subjective feeling on the latency of the system.

How to do it properly?

At Vay, we recognized quite early in the project the importance of having the high precision and reliable tool for measuring glass-to-glass latency. The glass-to-glass latency measuring tool (which is based on this paper) became important in evaluating and comparing different camera models and hardware compute platforms. Given that we found it quite useful in our context, we decided to open source it. We hope you will find it useful as well for understanding your end-to-end system performance and consider it over more expensive alternatives.

Basic principle behind the tool is to centralize the emitting of the light source (LED) on the camera lens and its detection (phototransistor) on the computer screen. The centralization approach eliminates the need for time synchronization which increases the precision of this method. Once that is in place, we are able to calculate the time delta between when the light source is activated (LED triggering is usually measured in microseconds) and when it is detected. Another benefit of this approach is that you are able to execute multiple measurements over time in order to get the latency distribution over time.

In our repo, you will find an affordable setup which is based on Arduino Uno and related sensors (LED and phototransistor). By running a simple Python script, a measurement test will be kicked off for a predefined number of cycles. The end result is a chart that shows the glass-to-glass video latency distribution.

Btw, we are hiring!

In case you are excited about working on a deep tech project which includes several engineering disciplines (robotics, perception, video, control, data science embedded, cloud, mobile app development, etc.) and would like to contribute to Vay’s new mobility service concept, do check out Vay’s Career page. We are Berlin based but we are offering remote work options across different roles.

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