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In diesem Video vom Automagazin tuningblog geht es um NVIDIA, Cosmos und die Zukunft des autonomen Fahrens. NVIDIA setzt mit Cosmos und der Omniverse-Plattform neue Maßstäbe in der Entwicklung künstlicher Intelligenz für autonome Fahrzeugsysteme. Mithilfe von Modell-Destillation, Closed-Loop-Training und synthetischer Datenerzeugung werden intelligente Fahrsysteme aufgebaut, die sich ständig verbessern und an komplexe Umgebungen anpassen können.

Besonders spannend: Cosmos nutzt reale Fahrdaten, um daraus 3D-Simulationen zu erstellen. Diese werden wiederum in Trainingsszenarien umgewandelt, bei denen die KI durch wiederholte Lernprozesse nahezu auf dem Niveau eines menschlichen Fahrers agieren kann. Die simulierten Umgebungen schließen die Lücke zur echten Welt und fördern eine sichere, flexible Fahrzeugsteuerung.

Wer sich für autonome Systeme, moderne Simulationstechniken oder einfach die Zukunft der Mobilität interessiert, findet hier spannende Einblicke in eine Technologie, die gerade erst am Anfang steht.

#nvidia, #cosmos, #autonomesfahren, #omniverse, #ki, #simulation, #zukunft #tuningblog - das Magazin für Auto-Tuning und Mobilität!

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Transkript
00:00Welcome to the video from the Auto Magazine Tuning Blog.
00:05This time, we're talking about Autonomous Driving Systems and NVIDIA's role in developing such technologies.
00:12NVIDIA is driving the development of Artificial Intelligence for autonomous vehicles with a combination of the Omniverse platform and the capabilities of the Cosmos system.
00:20Cosmos supports AI-centric autonomous vehicle systems with prediction and inference capabilities that enable a fully trainable end system.
00:33This involves new methods such as model augmentation, closed-loop training, and the generation of synthetic data.
00:42Model augmentation begins with transferring the driving knowledge of a slower, more intelligent teacher model to a smaller, faster student model.
00:51In the car, the teacher model shows the optimal driving path, while the student model learns iteratively until it performs at almost the same level.
01:01This process provides a basis for the control model.
01:05However, fine-tuning is required for more complex scenarios.
01:09This is where closed-loop training comes into play.
01:12It uses driving data logs to reconstruct physics-based 3D models with the help of Omniverse, in which driving behavior can be trained.
01:20These scenes are specifically varied to test the model's ability to generate driving trajectories.
01:29Cosmos evaluates the AI's behavior in these scenes to measure the model's performance.
01:33The resulting new scenarios and evaluations form a comprehensive data set that improves training in closed simulation loops and makes autonomous systems more robust in the face of difficult traffic situations.
01:47In addition, the generation of synthetic 3D data strengthens the adaptability of autonomous systems to different environments.
01:58Omniverse creates detailed 4D driving environments from driving data by combining maps and images to create a digital twin of the real world.
02:06Each pixel is segmented to support Cosmos in scene recognition.
02:13On this basis, Cosmos generates realistic and diverse training scenarios, further closing the gap between simulation and the real world.
02:23Omniverse and Cosmos thus enable the autonomous vehicle system to learn more intelligently, adapt better, and drive more safely.
02:30A clear step forward towards safe and intelligent mobility.
02:37As always, you can find the link to read the article in our magazine in the video description.
02:43Did you find this information about NVIDIA's autonomous driving system interesting?
02:47Then please leave a subscription.
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