Nvidia drive av brings artificial intelligence defined driving to new mercedes benz cla

Nvidia is integrating its drive av software and artificial intelligence infrastructure into the new mercedes benz cla, delivering enhanced level 2 point to point driver assistance and over the air upgrade potential.

Nvidia is bringing its drive av software with enhanced level 2 point to point driver assistance capabilities to u.s. roads by the end of this year, starting with mercedes benz and its all new cla model. The cla is the first mercedes benz vehicle to feature the mb.os platform, and it uses nvidia’s full stack drive av software, artificial intelligence infrastructure and accelerated compute to power advanced driver assistance. The design is intended to support over the air updates for future upgrades and new features, including planned enhancements to mb.drive assist pro that may be available ex factory and through the mercedes benz store.

The mercedes benz cla recently received a five star european new car assessment program euroncap safety rating, with mb.drive active safety features contributing to accident mitigation, avoidance and the overall top score. Nvidia describes drive av as a dual stack architecture that combines an artificial intelligence end to end core driving stack with a parallel classical safety stack built on the nvidia halos safety system, adding redundancy and safety guardrails. This unified architecture enables advanced level 2 automated driving functions such as point to point urban navigation in complex city environments, proactive collision avoidance, automated parking in tight spaces and cooperative steering between the system and the driver.

Nvidia deep learning models power artificial intelligence assisted urban driving that can interpret traffic holistically to handle lane selection, turns and route following in congested or unfamiliar areas, as well as proactively respond to vulnerable road users like pedestrians, cyclists and scooter riders. Beyond in vehicle capabilities, nvidia and mercedes benz are using nvidia omniverse libraries and digital twins of factories and assembly lines to design, plan and optimize manufacturing operations virtually, while omniverse and the nvidia cosmos platform are used to test and validate intelligent driving software in simulation before real world deployment. A cloud to car development pipeline ties together nvidia dgx training infrastructure, omniverse and cosmos simulation for billions of virtual miles, and in vehicle drive agx compute with the drive hyperion architecture, creating a closed loop system intended to accelerate algorithm iteration, improve accuracy, validate safety in rare edge cases and support scalable deployment across multiple vehicle platforms, including those of other global automaker partners.

58

Impact Score

Sarvam artificial intelligence signs ₹10,000 crore deal with tamil nadu for sovereign artificial intelligence park

Sarvam artificial intelligence has signed a ₹10,000 crore memorandum of understanding with the tamil nadu government to build india’s first full stack sovereign artificial intelligence park, positioning the startup at the center of the country’s data sovereignty push. The project aims to combine government exclusive infrastructure with deep tech jobs and advanced model development for indian use cases.

Nvidia expands Drive Hyperion ecosystem for level 4 autonomy

Nvidia is broadening its Drive Hyperion ecosystem with new sensor, electronics and software partners, aiming to accelerate level 4-ready autonomous vehicles across passenger and commercial fleets. The company is pairing this hardware platform with new Artificial Intelligence models and a safety framework designed to support large-scale deployment.

Nvidia DGX SuperPOD becomes blueprint for Rubin artificial intelligence factories

Nvidia is positioning its Rubin platform and DGX SuperPOD as the core blueprint for the next generation of large scale artificial intelligence factories, unifying new chips, high performance networking, and orchestration software. The company is targeting massive agentic artificial intelligence, mixture of experts models, and long context workloads while cutting inference token costs.

Contact Us

Got questions? Use the form to contact us.

Contact Form

Clicking next sends a verification code to your email. After verifying, you can enter your message.