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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25.06.2026

Eustella: Wiener Claude- und ChatGPT-Herausforderer geht in Vollbetrieb

Eustella setzt mit seinem Agentic-AI-Angebot auf Open Source, Mobile First und 100 Prozent Europa - und hat damit große Pläne. Nach der Beta-Phase folgte nun der Launch.
/artikel/eustella-wiener-claude-und-chatgpt-herausforderer-geht-in-vollbetrieb
25.06.2026

Eustella: Wiener Claude- und ChatGPT-Herausforderer geht in Vollbetrieb

Eustella setzt mit seinem Agentic-AI-Angebot auf Open Source, Mobile First und 100 Prozent Europa - und hat damit große Pläne. Nach der Beta-Phase folgte nun der Launch.
/artikel/eustella-wiener-claude-und-chatgpt-herausforderer-geht-in-vollbetrieb
Eustella-CEO Matteo Rosoli | (c) AI Factory Austria AI:AT/Arman Rastegar
Eustella-CEO Matteo Rosoli | (c) AI Factory Austria AI:AT/Arman Rastegar

Prominente Investoren wie Hansi Hansmann, Hermann Futter und die 3VC-Gründer Peter Lasinger und Roman Scharf im Publikum, Bitpanda Co-Founder Christian Trummer und Emmi-Co-Founder Johannes Brandstetter – mittlerweile Vice President AI for Science bei Mistral AI – auf der Bühne: Das Launch-Event des Wiener Startups Eustella in der AI Factory Austria AI:AT zog diese und noch weitere Größen der heimischen Innovationsszene an. Der Plan des Unternehmens geht aber weit über die Landesgrenzen hinaus: Man will KI-Nutzer:innen in ganz Europa überzeugen.

„Es gibt mehr als 130 Millionen aktive AI-User in Europa, aber kaum europäische Alternativen, vor allem nicht für Konsumentinnen und Konsumenten und vor allem nicht im Agent-Bereich“, sagt CEO Matteo Rosoli beim Launch-Event. Der KI-Experte – übrigens ein Absolvent des High-Potential-Programms der Wiener HTL Spengergasse – hat das Unternehmen gemeinsam mit Alexander Maitz, Jakob Steinschaden und Bastian Kellhofer gegründet. Das Konzept: Ein Agentic-AI-Angebot, das mit den US-Riesen mithalten kann, aber zu 100 Prozent in Europa gehosted ist – etwa bei Ionos in Berlin oder Scaleway in Paris.

Fable-5-Sperre „Spitze des Eisbergs“

„Die Sperre von Anthropics Fable 5 war nur die Spitze des Eisbergs der Souveränitäts-Thematik in Europa“, meint Rosoli. Souveränität sei zu einer Frage der Business Continuity und damit zu einer Frage des Überlebens für europäische Unternehmen geworden. Unter anderem wegen der Regulatorik, die, wie der CEO betont, „auch einen guten Zweck“ habe, würden europäische Lösungen vielfach hinter amerikanischen und asiatischen hinterherhinken. Das wolle man ändern: „Wir wollen nicht die europäische Software sein, die nicht so gut funktioniert und nicht so viel Mehrwert bringt. Wir wollen wirklich eine europäische Alternative sein.“

Orchestrierung entscheidend

Dabei baut Eustella kein eigenes KI-Modell, wie es etwa Mistral in Frankreich macht. Das Wiener Startup setzt auf verschiedene Open-Source-Modelle – nicht nur von Mistral, sondern auch von US-Anbietern wie Google und OpenAI. „Entscheidend ist die Orchestrierungsebene“, erklärt Rosoli. Diese „Agent Orchestration“ treffe eine smarte Auswahl, welche Modelle für welchen Zweck optimal passen und halte damit auch den Token-Verbrauch gering. Kombiniert wird das mit einem Mobile-First-Ansatz auf Nutzer:innen-Seite – Stichwort: „Agent in a pocket“.

Kompetitive Preise

Preislich will Eustella nicht nur mithalten, sondern gibt sich kompetitiv: Neben einer stark eingeschränkten Free-Version gibt es Angebote um sechs, 18 bzw. 90 Euro monatlich. „Jeder Anbieter kann kompetitive Preise anbieten. Wir machen das ganz bewusst aus Europa heraus. Der Preis rechnet sich aber auch für uns“, sagt der Gründer. Mit dem 18-Euro-Modell könne man etwa schon „richtig schön ein, zwei große Agents laufen lassen.“

„Beschäftigen uns 50 Prozent unserer Zeit mit AI-Safeguards und Themen wie Prompt Injections“

Das zweite große Verkaufsargument ist Sicherheit: „Die Daten sind bei Eustella sicher. Anders als bei den amerikanischen und asiatischen Anbietern braucht man sich keine Sorgen machen, dass etwas an Werbeanbieter verkauft wird“, sagt der Gründer. Für Sicherheit wolle man aber natürlich auch auf technischer Ebene sorgen. „Wir beschäftigen uns 50 Prozent unserer Zeit mit AI-Safeguards und Themen wie Prompt Injections. Deswegen releasen wir unsere Funktionen auch Schritt für Schritt, um gar keine Angriffsflächen zu ermöglichen“, so Rosoli.

Partnerschaften mit Bitpanda und Geizhals

Abgerundet werden soll das Angebot durch sogenannte Daten-Partnerschaften. Solche gibt es bislang mit dem Wiener Krypto-Unicorn Bitpanda, das über eine Schnittstelle aktuelle und zuverlässige Daten zum Krypto-Markt liefern soll, und mit dem Wiener Portal Geizhals, das valide Preisvergleichsdaten einspeist. Bitpanda-Co-Founder und Chief Scientist Christian Trummer sieht in der Partnerschaft aber noch mehr Potenzial: „Wir erwarten uns sehr viel von Eustella. Agentic Finance ist bei uns ein Riesen-Thema und in der Finanzbranche sind immer Vertrauen und Zuverlässigkeit wichtig.“ In Zukunft wolle man „die Finanzplattform für Agents“ sein. „Eustella ist hier der perfekte Partner für uns“, so der Bitpanda-Gründer.

Bitpanda-Co-Founder Christian Trummer (l.) neben Phillip Maasberg von Ionos | (c) AI Factory Austria AI:AT/Arman Rastegar

„Schauen einmal, dass die Server den Zuwachs an Usern überleben“

Bis dahin liegen aber noch einige Schritte vor dem Wiener Startup. „In den nächsten paar Tagen schauen wir einmal, dass die Server den Zuwachs an Usern überleben“, sagt Rosoli. „Dann werden wir Stück für Stück die Agent-Funktionalitäten aufbauen – dabei gibt es verschiedene Sicherheitsstufen.“ Schon bald soll Eustella etwa voll autonom kleine Essensbestellungen abwickeln können. Für davor gibt aber Co-Founder Jakob Steinschaden das erste konkrete Ziel aus: „Es wäre super, wenn wir im App-Store auf Platz eins landen.“

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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.