20.03.2020

Autonomous vehicles: AVL optimizes object recognition in AI

The future of driving is autonomous… But until vehicles reach human-level driving capabilities, AI still has to learn a few things. The Graz-based company AVL is tackling some of these challenges together with the Silicon Valley based technology provider Deepen.AI.
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AVL trainiert die AI mit Deepen AI
(c) Adobe Stock / Monopoly919
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Simply leaning back instead of having to pay attention to traffic: That is the vision of autonomous driving. This is intended not only to make traveling more pleasant for the passengers, but also to make it safer than having one person at the steering wheel, when distractions and human errors are the leading cause of fatalities. Clearly, in order to successfully tackle the driving task, Autonomous Driving  Systems (ADS) must be able to recognize objects, assess situations correctly, and master driving skills.

+++How software helps to reduce human driving errors+++

This process takes place in several stages. In the first phase, the object detection must determine where an object is located at all. In the second step, a detected object is then classified: It is determined whether it is, for example, a vehicle, an adult, a child or an animal – because a child behaves differently from an adult, for example. Finally, the system must carry out the so-called „tracking“. This involves analyzing where the object was in the past and where it is now – in order to draw conclusions about where the object will probably be next.

Separating the data wheat from the data chaff

Self-driving cars use data from various sensors installed in the vehicle – such as cameras or LiDAR sensors, which measure the distance between the objects and the car. These sensors produce countless amounts of data – and it is precisely this data that must be correctly classified so that the AI can identify which part of it is relevant to safety and which is not.

This is where the Silicon-Valley company Deepen.AI comes into play. Deepen has developed technology for better detection and segmentation of object data in road traffic in cooperation with AVL, based in Graz. First results of this cooperation were presented at the CES 2020 in Las Vegas.

PoC with AVL for the future of autonomous driving

Deepen.AI, founded by three former Google employees, is about the aforementioned challenge of providing ADSs a correct understanding of the world surrounding them. To achieve this, AI needs some human help in order to be effectively trained to make correct inferences. That’s why, in addition to its 17 full-time employees, Deepen.AI works with around 250 people in India who clean up the data collected by the sensors and teach AI to recognize things: For example, they mark when the AI has overlooked a side mirror on a car or misclassified objects. „These data analysts clear up doubts that the AI has about some objects,“ explains Mohammad Musa, Co-Founder and CEO of Deepen.AI: „They help with classification and calibration.”

This very deep focus on data integrity is also the context of the PoC developed jointly with AVL. „It is important for AVL to have correctly annotated data at pixel and point level,“ explains Thomas Schlömicher, Research Engineer ADAS at AVL. Ideally, the cooperation should result in a complete „Data Intelligence Pipeline“, which will be used by AVL’s numerous B2B customers to annotate their data and thus jointly shape the future capabilities of autonomous driving.

 „Safety Pool“ as next step after PoC

„Together“ is also the keyword behind the joint goal that the partners want to pursue after the successful PoC. One big challenge affecting the industry is that the various car manufacturers are currently pursuing different paths, each one using its own proprietary approach. „But the industry needs standards,“ says Musa: “That is the basis for everyone to trust the safety of the systems.”

Therefore, „Safety Pool™, (www.safetypool.ai) a project led by Deepen and the World Economic Forum, has the goal to define quantified benchmarks and uniform descriptions of driving situations, which will then serve not only as standards for the industry, but also as a solid backbone to derive consensus-driven safety assessments and frame regulations. This will bring society one significant step closer to benefitting from the revolutionary capabilities of automated driving technologies.

Video-Talk with AVL and Deepen AI

==> Deepen AI

==> AVL Creators Expedition

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10.08.2026

TEDAI-Kuratorin Alina Nikolaou: „Dieses Jahr kuratieren wir für Kollision“

Von 28. bis 30. Oktober geht die TEDAI in der Wiener Hofburg in die dritte Runde. Alina Nikolaou, Co-Founder, Director & Curator der TEDAI, spricht über die Entscheidung für Wien und über ein Programm, das bewusst Widerspruch inszeniert.
/artikel/tedai-2026-alina-nikolaou-interview
10.08.2026

TEDAI-Kuratorin Alina Nikolaou: „Dieses Jahr kuratieren wir für Kollision“

Von 28. bis 30. Oktober geht die TEDAI in der Wiener Hofburg in die dritte Runde. Alina Nikolaou, Co-Founder, Director & Curator der TEDAI, spricht über die Entscheidung für Wien und über ein Programm, das bewusst Widerspruch inszeniert.
/artikel/tedai-2026-alina-nikolaou-interview
Alina Nikolaou, Co-Founder, Director und Kuratorin von TEDAI Vienna. | (c) Florentina Olareanu

Geoffrey Hinton, Peter Steinberger, Yukiyasu Kamitani, Kauna Ibrahim Malgwi und Nenad Tomašev von Google DeepMind stehen bei der dritten TEDAI in der Wiener Hofburg auf der Bühne. Ein Honorar bekommt keiner von ihnen. Stattdessen arbeitet Alina Nikolaou drei bis sechs Monate mit jedem Speaker am Skript, in zwei bis vier Calls, danach folgen Rhetorik-Coaching und ein Fact-Checking der Inhalte.

Im Interview erklärt die Co-Founder, Director & Curator der TEDAI, wie dieser Prozess abläuft, warum das Programm heuer gegensätzliche Positionen direkt nacheinander setzt und wie die Konferenz überhaupt nach Wien kam.


brutkasten: Wie kam die TEDAI nach Wien und nicht nach San Francisco?

Alina Nikolaou: Seit circa 2010 gibt es TEDxVienna, damals ein Verein, der eine eintägige Veranstaltung im Volkstheater organisiert hat. Aufgrund der kuratoriellen Qualität und der Reichweite der Talks hat sich Ende 2023 die Mutterkonferenz TED HQ bei uns gemeldet, mit der Idee, das TEDAI-Format nach Europa zu bringen. In San Francisco gab es das damals schon ein Jahr lang als Pilot. Interesse hatten viele Städte, die Klassiker wie London, Paris und Berlin. Wien ist ja keine klassische KI-Stadt, wie es Paris oder Zürich wären. Trotzdem haben wir uns nach mehreren Runden für Wien entschieden: wegen der inhaltlichen Ausrichtung, die sich wie ein roter Faden durch die letzten drei Jahre zieht, wegen Wien als Weltstadt und UN-Stadt, in die unterschiedlichste Disziplinen und Kulturen reisen, und wegen des Fokus auf Lebensqualität und auf Innovation, nicht um der Innovation willen, sondern um der Menschheit ein besseres Leben zu ermöglichen. Heuer sind wir die einzige TEDAI weltweit, weil die Organisatoren in San Francisco es strategisch nicht weiter vorantreiben wollten. Dort häufen sich so viele KI-Events, dass sich das in der DNA des Ökosystems absorbiert. Ein KI-Event in Europa sticht stärker hervor.

Wie hat sich das Programm inhaltlich entwickelt?

Im ersten Jahr lag der Fokus stark auf Grundlagenbildung. Große Sprachmodelle waren plötzlich da und wurden fleckenhaft, experimentell verwendet. Wir haben eine Einführung gegeben, für die Personen vor Ort und für jene, die die Talks später online sehen. Die zweite Ausgabe hatte das Augenmerk darauf, dass KI in der Arbeitswelt angelangt ist. Was bedeutet das strategisch für Unternehmen und für die Infrastruktur, auf der KI beruht, und was bedeutet es auf der Mikroebene für unser kritisches Denken? Dieses Jahr sehen wir verstärkt, wie KI in der Gesellschaft agiert, von Aktivismus für oder gegen KI bis zu Sicherheits- und geopolitischen Fragen und paradigmenwechselnden Fragen in der Medizin. Es ist das breiteste Programm, das wir je hatten. Wie ich immer sage: Dieses Jahr kuratieren wir für Kollision. Es wird viele Speaker geben, die gegensätzliche Meinungen vertreten, teilweise direkt nacheinander, sodass sich das Publikum am Abend selbst eine Meinung bilden kann. Genau dort entsteht Innovation.

Wer kommt zur TEDAI?

Die Zielgruppe ist divers, von der Wirtschafts- und Bildungswelt über Startups und KMU bis zu Konzernen, die anfangs zwei Personen geschickt haben und mittlerweile zwanzigköpfige Delegationen. Der gemeinsame Nenner: Es sind Entscheidungsträger:innen, die oft über Budget verfügen und über den Tellerrand hinausblicken möchten. Im ersten Jahr kamen fast 70 Prozent des Publikums von außerhalb Österreichs, die größten Delegationen aus Deutschland, UK und Frankreich. Im zweiten Jahr waren rund 40 Prozent aus Österreich, was für uns ein Erfolg war. Letztes Jahr waren 65 Länder vor Ort.

Was macht das Format aus?

Wir haben eine klare Regel: keine Demo-Booths, höchstens künstlerische Installationen oder spielerische Exhibits. Dafür gibt es am zweiten Tag parallel Workshops, Reflexionsrunden und sogenannte Ideas Adventures, bei denen wir Kleingruppen aus der Hofburg hinausschicken. Es gibt auch keine Speaking Fee. Stattdessen arbeite ich mit allen Speakern drei bis sechs Monate lang an ihren Talks, konkret in zwei bis vier Calls, in denen wir das große Thema aufmachen und immer weiter auf dieses eine Skript runterbrechen. Danach werden sie bis zur Sekunde vor der Bühne von Rhetorik-Coaches begleitet, dazu kommt ein rigoroses Fact-Checking. Einen TED Talk von Geoffrey Hinton oder Peter Steinberger wird man so auf keiner anderen Konferenz hören.

Wohin soll die Reise gehen?

Was ich immer stärker beobachte: Problemlösungsorientiertes Forschen bringt im KI-Bereich die spannendsten Innovationen hervor und bedeutet gleichzeitig das nachhaltigste wirtschaftliche Wachstum. Wir arbeiten an einer Plattform, die die grundlegenden Probleme dokumentiert, die Forschende und Gründer:innen derzeit am meisten beschäftigen, und diese im Rahmen von Veranstaltungen nach außen trägt. Also nicht nur ideenzentriertes Denken, wie bei TED Talks, sondern problemzentriertes Denken.


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AI Summaries

Autonomous vehicles: AVL optimizes object recognition in AI

  • Clearly, in order to successfully tackle the driving task, Autonomous Driving  Systems (ADS) must be able to recognize objects, assess situations correctly, and master driving skills.
  • Self-driving cars use data from various sensors installed in the vehicle – such as cameras or LiDAR sensors, which measure the distance between the objects and the car.
  • Deepen has developed technology for better detection and segmentation of object data in road traffic in cooperation with AVL, based in Graz.
  • „It is important for AVL to have correctly annotated data at pixel and point level,“ explains Thomas Schlömicher, Research Engineer ADAS at AVL.
  • Ideally, the cooperation should result in a complete „Data Intelligence Pipeline“, which will be used by AVL’s numerous B2B customers to annotate their data and thus jointly shape the future capabilities of autonomous driving.

AI Kontextualisierung

Welche gesellschaftspolitischen Auswirkungen hat der Inhalt dieses Artikels?

Leider hat die AI für diese Frage in diesem Artikel keine Antwort …

Autonomous vehicles: AVL optimizes object recognition in AI

  • Clearly, in order to successfully tackle the driving task, Autonomous Driving  Systems (ADS) must be able to recognize objects, assess situations correctly, and master driving skills.
  • Self-driving cars use data from various sensors installed in the vehicle – such as cameras or LiDAR sensors, which measure the distance between the objects and the car.
  • Deepen has developed technology for better detection and segmentation of object data in road traffic in cooperation with AVL, based in Graz.
  • „It is important for AVL to have correctly annotated data at pixel and point level,“ explains Thomas Schlömicher, Research Engineer ADAS at AVL.
  • Ideally, the cooperation should result in a complete „Data Intelligence Pipeline“, which will be used by AVL’s numerous B2B customers to annotate their data and thus jointly shape the future capabilities of autonomous driving.

AI Kontextualisierung

Welche wirtschaftlichen Auswirkungen hat der Inhalt dieses Artikels?

Leider hat die AI für diese Frage in diesem Artikel keine Antwort …

Autonomous vehicles: AVL optimizes object recognition in AI

  • Clearly, in order to successfully tackle the driving task, Autonomous Driving  Systems (ADS) must be able to recognize objects, assess situations correctly, and master driving skills.
  • Self-driving cars use data from various sensors installed in the vehicle – such as cameras or LiDAR sensors, which measure the distance between the objects and the car.
  • Deepen has developed technology for better detection and segmentation of object data in road traffic in cooperation with AVL, based in Graz.
  • „It is important for AVL to have correctly annotated data at pixel and point level,“ explains Thomas Schlömicher, Research Engineer ADAS at AVL.
  • Ideally, the cooperation should result in a complete „Data Intelligence Pipeline“, which will be used by AVL’s numerous B2B customers to annotate their data and thus jointly shape the future capabilities of autonomous driving.

AI Kontextualisierung

Welche Relevanz hat der Inhalt dieses Artikels für mich als Innovationsmanager:in?

Leider hat die AI für diese Frage in diesem Artikel keine Antwort …

Autonomous vehicles: AVL optimizes object recognition in AI

  • Clearly, in order to successfully tackle the driving task, Autonomous Driving  Systems (ADS) must be able to recognize objects, assess situations correctly, and master driving skills.
  • Self-driving cars use data from various sensors installed in the vehicle – such as cameras or LiDAR sensors, which measure the distance between the objects and the car.
  • Deepen has developed technology for better detection and segmentation of object data in road traffic in cooperation with AVL, based in Graz.
  • „It is important for AVL to have correctly annotated data at pixel and point level,“ explains Thomas Schlömicher, Research Engineer ADAS at AVL.
  • Ideally, the cooperation should result in a complete „Data Intelligence Pipeline“, which will be used by AVL’s numerous B2B customers to annotate their data and thus jointly shape the future capabilities of autonomous driving.

AI Kontextualisierung

Welche Relevanz hat der Inhalt dieses Artikels für mich als Investor:in?

Leider hat die AI für diese Frage in diesem Artikel keine Antwort …

Autonomous vehicles: AVL optimizes object recognition in AI

  • Clearly, in order to successfully tackle the driving task, Autonomous Driving  Systems (ADS) must be able to recognize objects, assess situations correctly, and master driving skills.
  • Self-driving cars use data from various sensors installed in the vehicle – such as cameras or LiDAR sensors, which measure the distance between the objects and the car.
  • Deepen has developed technology for better detection and segmentation of object data in road traffic in cooperation with AVL, based in Graz.
  • „It is important for AVL to have correctly annotated data at pixel and point level,“ explains Thomas Schlömicher, Research Engineer ADAS at AVL.
  • Ideally, the cooperation should result in a complete „Data Intelligence Pipeline“, which will be used by AVL’s numerous B2B customers to annotate their data and thus jointly shape the future capabilities of autonomous driving.

AI Kontextualisierung

Welche Relevanz hat der Inhalt dieses Artikels für mich als Politiker:in?

Leider hat die AI für diese Frage in diesem Artikel keine Antwort …

Autonomous vehicles: AVL optimizes object recognition in AI

  • Clearly, in order to successfully tackle the driving task, Autonomous Driving  Systems (ADS) must be able to recognize objects, assess situations correctly, and master driving skills.
  • Self-driving cars use data from various sensors installed in the vehicle – such as cameras or LiDAR sensors, which measure the distance between the objects and the car.
  • Deepen has developed technology for better detection and segmentation of object data in road traffic in cooperation with AVL, based in Graz.
  • „It is important for AVL to have correctly annotated data at pixel and point level,“ explains Thomas Schlömicher, Research Engineer ADAS at AVL.
  • Ideally, the cooperation should result in a complete „Data Intelligence Pipeline“, which will be used by AVL’s numerous B2B customers to annotate their data and thus jointly shape the future capabilities of autonomous driving.

AI Kontextualisierung

Was könnte das Bigger Picture von den Inhalten dieses Artikels sein?

Leider hat die AI für diese Frage in diesem Artikel keine Antwort …

Autonomous vehicles: AVL optimizes object recognition in AI

  • Clearly, in order to successfully tackle the driving task, Autonomous Driving  Systems (ADS) must be able to recognize objects, assess situations correctly, and master driving skills.
  • Self-driving cars use data from various sensors installed in the vehicle – such as cameras or LiDAR sensors, which measure the distance between the objects and the car.
  • Deepen has developed technology for better detection and segmentation of object data in road traffic in cooperation with AVL, based in Graz.
  • „It is important for AVL to have correctly annotated data at pixel and point level,“ explains Thomas Schlömicher, Research Engineer ADAS at AVL.
  • Ideally, the cooperation should result in a complete „Data Intelligence Pipeline“, which will be used by AVL’s numerous B2B customers to annotate their data and thus jointly shape the future capabilities of autonomous driving.

AI Kontextualisierung

Wer sind die relevantesten Personen in diesem Artikel?

Leider hat die AI für diese Frage in diesem Artikel keine Antwort …

Autonomous vehicles: AVL optimizes object recognition in AI

  • Clearly, in order to successfully tackle the driving task, Autonomous Driving  Systems (ADS) must be able to recognize objects, assess situations correctly, and master driving skills.
  • Self-driving cars use data from various sensors installed in the vehicle – such as cameras or LiDAR sensors, which measure the distance between the objects and the car.
  • Deepen has developed technology for better detection and segmentation of object data in road traffic in cooperation with AVL, based in Graz.
  • „It is important for AVL to have correctly annotated data at pixel and point level,“ explains Thomas Schlömicher, Research Engineer ADAS at AVL.
  • Ideally, the cooperation should result in a complete „Data Intelligence Pipeline“, which will be used by AVL’s numerous B2B customers to annotate their data and thus jointly shape the future capabilities of autonomous driving.

AI Kontextualisierung

Wer sind die relevantesten Organisationen in diesem Artikel?

Leider hat die AI für diese Frage in diesem Artikel keine Antwort …

Autonomous vehicles: AVL optimizes object recognition in AI

  • Clearly, in order to successfully tackle the driving task, Autonomous Driving  Systems (ADS) must be able to recognize objects, assess situations correctly, and master driving skills.
  • Self-driving cars use data from various sensors installed in the vehicle – such as cameras or LiDAR sensors, which measure the distance between the objects and the car.
  • Deepen has developed technology for better detection and segmentation of object data in road traffic in cooperation with AVL, based in Graz.
  • „It is important for AVL to have correctly annotated data at pixel and point level,“ explains Thomas Schlömicher, Research Engineer ADAS at AVL.
  • Ideally, the cooperation should result in a complete „Data Intelligence Pipeline“, which will be used by AVL’s numerous B2B customers to annotate their data and thus jointly shape the future capabilities of autonomous driving.