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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07.01.2025

Spin-offs: Uni Innsbruck aktuell mit 23 Beteiligungen an Ausgründungen

Die Universität Innsbruck hat sich mit ihrer Gründungs- und Beteiligungsstrategie seit der Gründung der Beteiligungsgesellschaft im Jahr 2008 über die Uni-Holding an 39 Spin-offs beteiligt. Aktuell gibt es noch 23 Beteiligungen im Portfolio.
/artikel/spin-offs-uni-innsbruck-aktuell-mit-23-beteiligungen-an-ausgruendungen
07.01.2025

Spin-offs: Uni Innsbruck aktuell mit 23 Beteiligungen an Ausgründungen

Die Universität Innsbruck hat sich mit ihrer Gründungs- und Beteiligungsstrategie seit der Gründung der Beteiligungsgesellschaft im Jahr 2008 über die Uni-Holding an 39 Spin-offs beteiligt. Aktuell gibt es noch 23 Beteiligungen im Portfolio.
/artikel/spin-offs-uni-innsbruck-aktuell-mit-23-beteiligungen-an-ausgruendungen
Universität Innsbruck, Spin-offs
(c) Universität Innsbruck

Vergleicht man die österreichische Spin-off-Landschaft mit jener anderer Länder, erweist diese sich als mager – wären da nicht diverse heimische Universitäten, die proaktiv Spin-offs fördern, wie brutkasten berichtete. Die Universität Innsbruck gilt als einer dieser Innovationstreiber.

Spin-offs in Deutschland

Eine Studie aus dem Oktober 2023 zur Entrepreneurship Performance deutscher Hochschulen ermittelte die Anzahl an Gründungen aus Hochschulen von 2014 bis 2022 und weist diese Werte für die 20 am höchsten gerankten Universitäten in Deutschland aus. Zusammen waren diese 20 Universitäten Ursprung von knapp 4.800 Startups. Dabei gibt es eine ausgeprägte Spitzengruppe mit der TU München (810 Startups) ganz vorne, gefolgt mit weitem Abstand von der TU Berlin (466) und dem Karlsruher Institut für Technologie (KIT, 321).

Hierzulande hat sich die Universität Innsbruck seit der Gründung ihrer Beteiligungsgesellschaft im Jahr 2008 über die Uni-Holding an 39 Spin-offs beteiligt. Durch die neu gegründeten Unternehmen wurden seither mehr als 200 neue Arbeitsplätze geschaffen.

“Der Ansatz der Universität Innsbruck, akademisch getriebene Spin-offs wirksam zu unterstützen, zeigt Früchte”, sagt Rektorin Veronika Sexl. “Durch die Unternehmen wird spezialisiertes Grundlagenwissen zum Wohle der Gesellschaft transformiert und diesen strategischen Ansatz werden wir auch in Zukunft weiter forcieren.” Neben Studienangeboten im Bereich Entrepreneurship und dem gemeinsam mit der Wirtschaftskammer Tirol betriebenen Gründungs- und Innovationszentrum InnCubator stellt die 2008 gegründete Beteiligungsgesellschaft Uni-Holding ein Kernelement der Strategie dar.

AQT und ParityQC als Aushängeschilder

Aktuell hält die Uni-Holding 23 Beteiligungen an Ausgründungen aus der Universität Innsbruck. Diese Unternehmen sind in den Bereichen Digitalisierung, Finanzen, Gesundheit, Ökologie und Technologie tätig. Neben den renommierten Ausgründungen im Bereich der Quantentechnologien – AQT und Parity QC – beschäftigt sich etwa das junge Spin-off QND – Quantum Network Design mit der Simulation von Quantennetzwerken, um die wesentlichen Grundsteine für eine industrielle Implementierung zu legen.

Beispiele der Innsbrucker Spin-offs

Innfoliolytix wäre ein weiteres Beispiel der Spin-off-Strategie: Das Startup macht Kapitalmarktanleger:innen aktuelle Forschungsergebnisse in Form von quantitativen Anlagestrategien zugänglich. Die Universitätsprofessoren Matthias Bank und Jochen Lawrenz vom Institut für Banken und Finanzen sind an der gemeinsamen Gründung und Entwicklung des Unternehmens mit der BTV AG und der Universität Innsbruck beteiligt; seit 2024 gilt Innfoliolytix als eine FMA-lizenzierte Wertpapierfirma. Im November 2024 wurde der vom Startup beratene und von der 3 Banken-Generali Investment-Gesellschaft verwaltete Fonds “Quant Global Plus” mit dem Österreichischen Dachfonds Award 2024 des GELD-Magazins in den Kategorien “Aktiendachfonds 1 Jahr” und “Aktiendachfonds 3 Jahre” ausgezeichnet.

KinCon biolabs wiederrum baut seine patentierte Plattformtechnologie weiter aus, um Pharmaunternehmen bei der Lösung medizinischer Herausforderungen, insbesondere bei Krebs und Morbus Parkinson, zu unterstützen. Das von Philipp Tschaikner und Eduard Stefan gegründete Unternehmen entwickelt eine zellbasierte Reportertechnologie, die strukturelle Veränderungen von schwer zu analysierenden Zielproteinen sichtbar macht. Wenn ein Wirkstoffkandidat an einen, spezifisch für das Zielprotein entwickelten Reporter bindet, beginnt der genetisch kodierte Reporter in den Zellen zu leuchten. Damit lasse sich die Wirksamkeit von Medikamentenkandidaten systematisch vorhersagen, sodass die Pharmaunternehmen neuartige Therapien schneller in die klinische Anwendung, d.h. zu den Patient:innen, bringen könnten.

Kartenspiel in USA lizenziert

Das von Physiker:innen an der Universität Innsbruck entwickelte Kartenspiel Seeker Chronicles konnte mittlerweile an den renommierten US-amerikanischen Spieleverlag Wise Wizard Games lizenziert werden. Es verbindet Wissenschaftsvermittlung mit Spielelementen. Dessen Erfinder:innen Hendrik Poulsen Nautrup, Lea Trenkwalder und Fulvio Flamini haben das Spin-off-Unternehmen OneStone Studios gegründet und arbeiten aktuell an Erweiterungen, einer digitalen Version des Spiels und mehreren neuen Spielen, alle mit dem Ziel, Wissenschaft der Gesellschaft näherzubringen.

Arbeitsbedingungen, Arbeitsorganisation und daraus resultierende Beanspruchungen mit dem Ziel zu betrachten, Arbeit “menschenzentriert” zu gestalten und hinsichtlich verschiedener Humankriterien in Unternehmen und Organisationen zum Wohle aller Beteiligten zu verbessern – das ist das Vorhaben von Humane Arbeit. Gegründet von Cornelia Strecker, Christian Seubert und Jürgen Glaser bietet das Spin-off arbeitspsychologische Beratung auf dem aktuellsten Stand wissenschaftlicher Forschung.

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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.