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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15.11.2024

Kreativität unter Druck: Wie du und dein Team trotz Zeitnot kreative Ideen entwickelst

Gastbeitrag. Als Expertin für Creative Leadership unterstützt Kerstin Lobner Führungskräfte und Teams dabei, innovative Lösungen zu finden und ihr kreatives Potenzial zu entfalten. Für die brutkasten-Community liefert sie praktische Tipps für kreative Methoden zur Ideenentwicklung in der Wirtschaft.
/artikel/kreativitaet-unter-druck-wie-du-und-dein-team-trotz-zeitnot-kreative-ideen-entwickelst
15.11.2024

Kreativität unter Druck: Wie du und dein Team trotz Zeitnot kreative Ideen entwickelst

Gastbeitrag. Als Expertin für Creative Leadership unterstützt Kerstin Lobner Führungskräfte und Teams dabei, innovative Lösungen zu finden und ihr kreatives Potenzial zu entfalten. Für die brutkasten-Community liefert sie praktische Tipps für kreative Methoden zur Ideenentwicklung in der Wirtschaft.
/artikel/kreativitaet-unter-druck-wie-du-und-dein-team-trotz-zeitnot-kreative-ideen-entwickelst
Kerstin Lobner | (c) Ideenflow

Die Uhr tickt, die Deadline rückt näher – und jetzt sollen du und dein Team auch noch kreative Ideen entwickeln? Klingt unmöglich, oder? Doch genau unter solchen Bedingungen kann Kreativität zur Höchstform auflaufen. Aber warum fällt es uns oft schwer, unter Druck kreativ zu denken, und wie kannst du und dein Team diese Hürde überwinden? Hier sind einige Ansätze, um den kreativen Funken auch unter Zeitnot zu entzünden.

Der Druck als Kreativitätskiller

Zunächst einmal: Kreativität braucht oft Raum. Die besten Ideen kommen, wenn man Zeit hat, Gedanken schweifen zu lassen. Wenn aber die Deadline drängt, blockiert das Gefühl von Stress oft die kreativen Prozesse. Anstatt entspannt nach Lösungen zu suchen, fühlen wir uns gehetzt und neigen dazu, auf alte Muster zurückzugreifen – nicht gerade die ideale Ausgangssituation für frische Ideen.

Lösung #1: Timeboxing – Nutze die Zeit klug

Anstatt den gesamten Prozess unter Druck zu setzen, hilft es, die Zeit in kleinere, überschaubare Blöcke zu unterteilen. Diese Technik nennt sich „Timeboxing“. Gebt jeder Phase der Ideensammlung – von der ersten Brainstorming-Runde bis zur Auswahl der besten Ideen – eine feste Zeitvorgabe. So bleibt der Fokus erhalten, ohne dass die Hektik Überhand nimmt. Ironischerweise kann eine solche Strukturierung dazu führen, dass kreative Prozesse in kürzerer Zeit effizienter ablaufen. Setzt euch z.B. ein 10-Minuten-Zeitfenster für das Brainstorming und anschließend weitere 10 Minuten, um die vielversprechendsten Ideen zu priorisieren.

Lösung #2: Kreativitätstechniken wie die 6-3-5-Methode

Eine weitere Technik, die unter Zeitdruck Wunder wirken kann, ist die „6-3-5-Methode“. Hierbei schreiben sechs Personen in fünf Minuten jeweils drei Ideen auf. Diese Ideen werden dann an den nächsten Teilnehmer:in weitergegeben, der/die darauf aufbaut oder neue Vorschläge entwickelt. Durch den schnellen, iterativen Austausch kommen nicht nur viele Ideen zusammen, sondern die Zeitvorgabe sorgt auch dafür, dass niemand zu lange über einer Idee brütet. Diese Technik fördert den Fluss und verhindert, dass der Druck lähmend wirkt.

Lösung #3: Klare Fokussierung durch präzise Fragestellungen

Unter Zeitdruck geht es darum, möglichst schnell die relevanten Ideen zu identifizieren. Je klarer und fokussierter die Fragestellung ist, desto einfacher wird es, zielgerichtet zu arbeiten. Statt „Wie können wir unser Produkt verbessern?“ könnte die Frage lauten: „Wie können wir unsere App-Nutzer schneller zum Kaufabschluss führen?“ – konkrete Aufgabenstellungen fördern schnelle, kreative Lösungsansätze.

Lösung #4: Mikro-Pausen einlegen

Kreativität unter Druck bedeutet nicht, ununterbrochen Höchstleistungen zu erbringen. Mikro-Pausen sind Gold wert. Schon fünf Minuten Abstand können das Gehirn wieder erfrischen und die Kreativität ankurbeln. Diese kurzen Pausen verhindern, dass dein Team in hektisches Denken verfällt und helfen dabei, aus einem anderen Blickwinkel auf das Problem zu schauen. Ein kurzer Spaziergang um den Block oder einfach frische Luft schnappen kann Wunder wirken.

Lösung #5: Gamification – Der spielerische Ansatz

Wenn die Stimmung im Team angespannt ist, hilft es oft, den Druck mit einem spielerischen Element aufzulockern. Eine einfache Möglichkeit: Macht aus dem Ideensammeln ein kleines Spiel. Vergesst den Ernst der Lage für einen Moment und veranstaltet z.B. einen „Pitch-Wettbewerb“, bei dem die Teammitglieder ihre verrücktesten Ideen in nur 60 Sekunden präsentieren. Diese Methode nimmt dem Team den Stress und fördert gleichzeitig unkonventionelle Lösungsansätze.

Fazit: Kreativität unter Druck ist möglich – mit den richtigen Techniken

Der Schlüssel zu Kreativität unter Zeitnot ist es, Strukturen zu schaffen, die den Prozess erleichtern, statt zusätzlichen Druck aufzubauen. Durch Timeboxing, präzise Fragestellungen und spielerische Elemente können du und dein Team auch in stressigen Situationen kreative Höchstleistungen abrufen. Der Trick liegt darin, den Druck in geordnete Bahnen zu lenken und den kreativen Fluss zu fördern, anstatt ihn zu ersticken.


Über die Gastautorin Kerstin Lobner

Kreativität prägte sie von klein auf, als Enkelin des General Managers von Faber-Castell in Irland. Während andere im Alter an Neugierde verlieren, vertiefte sie ihr Interesse an Kreativität stetig.

Nach verschiedenen Positionen im Konzern-Marketing in Branchen wie IT, Telekommunikation und Gesundheitswesen unterstützt sie heute Führungskräfte und Teams dabei, innovative Lösungen zu finden und ihr kreatives Potenzial zu entfalten.


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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.