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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22.07.2026

„iPhone-Moment für das Familien-Whiteboard“: Wie das Grazer Startup Dæly den Alltag organisieren will

Das Grazer Startup Dæly will die Familienorganisation digitalisieren und ist in seinem Segment bereits Marktführer im DACH-Raum. Mit einem abonnementfreien Touch-Wandkalender für 300 Euro bringt das gebootstrappte Unternehmen verstreute Kalender und To-Do-Listen an einen Ort und konnte schon mehrere Tausend Familien damit überzeugen. Die Gründer setzen dabei auf Datenschutz und Cashflow-Finanzierung.
/artikel/iphone-moment-fuer-das-familien-whiteboard-wie-das-grazer-startup-daely-den-alltag-organisieren-will
22.07.2026

„iPhone-Moment für das Familien-Whiteboard“: Wie das Grazer Startup Dæly den Alltag organisieren will

Das Grazer Startup Dæly will die Familienorganisation digitalisieren und ist in seinem Segment bereits Marktführer im DACH-Raum. Mit einem abonnementfreien Touch-Wandkalender für 300 Euro bringt das gebootstrappte Unternehmen verstreute Kalender und To-Do-Listen an einen Ort und konnte schon mehrere Tausend Familien damit überzeugen. Die Gründer setzen dabei auf Datenschutz und Cashflow-Finanzierung.
/artikel/iphone-moment-fuer-das-familien-whiteboard-wie-das-grazer-startup-daely-den-alltag-organisieren-will
Die Dæly-Gründer v.l.: Paul Truffner, Florian Ritter und Alexander Walliser | (c) Dæly
Die Dæly-Gründer v.l.: Paul Truffner, Florian Ritter und Alexander Walliser | (c) Dæly

„Es ist so ein bisschen der iPhone-Moment für das Familien-Whiteboard“, sagt Florian Ritter, Co-Founder des Grazer Startups Dæly, im Gespräch mit brutkasten. Gemeinsam mit Paul Truffner und Alexander Walliser hat der 26-Jährige ein digitales Wandgerät entwickelt, das die traditionelle Zettelwirtschaft in Haushalten ersetzen soll. Der 15,6 Zoll große Touchscreen fungiert als zentrales Organisationsmedium und soll verstreute Kalender, Einkaufslisten und To-Do-Anwendungen vereinen.

Die Organisation in vielen Familien sei nämlich auf bis zu acht verschiedene, teils digitale und teils analoge Werkzeuge verteilt, erklärt Ritter. Kinder besäßen oft noch kein Smartphone, weshalb Eltern Termine zwar digital im Google- oder Apple-Kalender pflegten, parallel aber einen analogen Wandkalender führten, damit der Nachwuchs sehe, wann etwa das Fußballtraining stattfinde. Diese fragmentierte Organisation führe zu mangelndem Überblick, ständigen Missverständnissen und Koordinationsstress im Alltag.

„Wir bringen diese ganzen Tools in ein einziges System“

Um Abhilfe zu schaffen, führt das System verschiedene Datenquellen zusammen. „Wir bringen diese ganzen Tools in ein einziges System, das für alle funktioniert“, erläutert Ritter. Die Gründer haben das Gerät systemagnostisch aufgebaut, sodass Nutzer Google-, Outlook- oder Apple-Kalender synchronisieren können. Dies löse ein häufiges Problem in Partnerschaften, in denen die Beteiligten oft unterschiedliche Kalendersysteme nutzten. Über eine kostenlose Begleit-App für iOS und Android lassen sich Einträge auch von unterwegs verwalten.

Neben der Terminplanung bietet das Gerät eine Aufgabenverwaltung, ein Punktesystem für Kinder sowie eine Einkaufs- und Essensplanung. Für Ritter ist klar: „Unser Ziel ist es, das beste Familienhaushalts-Managementsystem zu bauen, das es gibt.“ Die kontinuierliche Weiterentwicklung erfolge auf Basis des Feedbacks einer engagierten Nutzerschaft, wobei das System Software-Updates automatisch und kostenfrei aufspiele.

Hardware-Entwicklung ohne Investorengelder

Im konkreten Segment, in dem es mehrere internationale Player gibt, ist man bereits Marktführer im DACH-Raum. „Wir sind bereits bei mehreren Tausend Familien im Einsatz“, sagt Ritter. Dabei setzt so ein Hardware-Startup bekanntlich eine stabile finanzielle Basis voraus. Hier unterscheidet sich der Weg des Unternehmens von einigen im Technologiesektor üblichen Mustern: Dæly ist vollständig eigenfinanziert und bereits profitabel. Für die Entwicklung und den Start investierten die Gründer einen sechsstelligen Betrag aus eigenen Mitteln, die sie unter anderem durch ihr bestehendes E-Commerce-Unternehmen „Tassenliebling“ erwirtschaftet hatten. Der weitere Aufbau erfolge primär über eine Vorbestellungskampagne sowie den laufenden operativen Cashflow. „Wir können das richtig gut aus dem eigenen Cashflow heraus stemmen“, sagt Ritter.

Er sieht in diesem „Finance Based Scaling“ eine zentrale Stärke des jungen Unternehmens. Auf Risikokapital wolle das Team vorerst verzichten, um die Unabhängigkeit zu wahren. Die Bildschirme lässt das Startup wie branchenüblich in China fertigen. Ritter räumt dabei ein, dass eine Produktion in Europa wünschenswert wäre, dass jedoch die Kosten für das Endgerät, das aktuell 300 Euro kostet, massiv steigern würde.

Fokus auf den DACH-Raum und europäische Sicherheitsstandards

Trotz der Hardware-Fertigung in Asien legt das Unternehmen großen Wert auf europäische Sicherheitsstandards. Ein zentraler Aspekt des Produkts ist die bewusste Abgrenzung von US-amerikanischen Cloud-Diensten. Um die Privatsphäre der Familien zu schützen, betreibt das Unternehmen seine Server ausschließlich in Deutschland. Synchronisierte Kalenderdaten speichert das System laut Herstellerangaben nicht zwischen. Die technische Infrastruktur verzichtet bei der Datenspeicherung auf US-Anbieter wie Google Firebase, um die Einhaltung der DSGVO und europäischer Sicherheitsstandards zu gewährleisten.

Beim physischen Produkt setzt das Startup auf Nachhaltigkeit: Das Gerät verfügt über keinen Akku. Ritter begründet dies mit der höheren Energieeffizienz eines direkten Stromanschlusses und dem Umweltschutz durch die Vermeidung seltener Rohstoffe. Der feste Standort an der Steckdose sichere, dass das Gerät stets für alle Familienmitglieder auffindbar bleibe. In den kommenden Monaten liege der strategische Fokus des Teams ganz auf dem DACH-Markt. Eine weitere geografische Expansion behalte man sich für einen späteren Zeitpunkt vor.

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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.