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Explore Vehicle Dynamics 🏎️ Simulate longitudinal and lateral dynamics for autonomous vehicles and race cars 🌧️ Analyze the impact of dry and wet surfaces on vehicle motion 📈 Build models with Vehicle Dynamics Blockset 🔗 Watch the video to learn more:

MATLAB

105,414 subscribers

11,830 Aufrufe • vor 1 Jahr •via X (Twitter)

Wissenschaft & Technologie Bildung Autos & Fahrzeuge

Anya Rossi• Live Now

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Profilbild von Redeemer

Redeemervor 1 Jahr

Can a space craft be simulated here?

Profilbild von Yang

Yangvor 1 Jahr

Want to learn how practical AI skills and automations for your business and work? Check out our 50+ step-by-step video tutorials 100% FREE 20+ hours of Ai and Automation goodness absolutely free 🥳

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We submitted the final proposal for the next phase of NASA’s Lunar Terrain Vehicle Services (LTVS) contract, to build, fly, and operate the Intuitive Machines-led Moon RACER vehicle on the Moon. NASA is expected to announce the LTV delivery and demonstration award by the end of 2025. Learn more:

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NVIDIA open models provide the foundation to innovate across agentic AI, robotics, autonomous vehicles, and research. 🤖 This release contributes 10 trillion language tokens, 500,000 robotics trajectories, and 100 terabytes of vehicle sensor data to the community. These frameworks are designed to significantly accelerate specialized AI development and discovery. Learn more:

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As AI moves into the physical world, safety must be the top priority. Leaders from Zoox, Aurora, Boston Dynamics, ANSI and NVIDIA discuss the innovations and safety practices shaping the next generation of autonomous vehicles and robotics. 🎥 Watch the full panel on-demand:

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NVIDIA DRIVE

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Autonomous driving through tight, dynamic, stochastic, and adversarial traffic-dynamics on sub-urban roads in India, as well as through partially unstructured environments. This demos showcases the robustness of our motion planning and decision making algorithmic frameworks in enabling #autonomousdriving through seamlessly through such traffic and environmental scenarios. The vehicle starts from a generic open environment at the temple, where there are no traffic-rules to abide by. It then exits the region and assumes a generic autonomous navigation behaviour, negotiating complex traffic scenes. At various points it can be seen that the other vehicles (bikes, autos, bicyclists, and cars) didn't abide by any traffic-rule and moved in crisscross fashion, presenting adversarial scenarios, challenging our autonomous vehicle at Swaayatt Robots to take care of the collision avoidance. This classical motion planning and decision making algorithmic framework is being further scaled up with deep #reinforcementlearning, which will practically solve the sub-urban traffic-dynamics and environment negotiation for #autonomousvehicles in India and throughout the world as well. This demo was done at the Kankali Kali Mata mandir in the city of Bhopal. This demo was a culmination of our prior works and demos: off-roads, on-roads, bidirectional traffic negotiation in single lane roads, and toll-plaza navigation. We have taken up the arduous task of solving the Level-4 autonomous driving by the end of 2024, globally. #machinelearning #deeplearning

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Autonomous vehicle learning to dodge traffic, performing stochastic adversarial negotiation. On 27th August we had representatives from the Suzuki Motor Corporation's autonomous department, Genki Maeda (Department Manager, AD Platform Development), Karachi Nobunari (Department General Manager, Advance Technology Development Department) and Ronit Kumar (Suzuki Innovation Center) visit us to test our #autonomousdriving technology. This was a high-stakes demo, where we asked our engineering team (including the founder, Sanjeev Sharma) to ride two wheeler and to cut the path of our autonomous vehicle at random / at will, in a live demo, creating an adversarial scenario, where it is the sole responsibility of our autonomous vehicle to dodge obstacles and prevent accidents. Over the years, we have been building autonomous driving technology to enable negotiation of adversarial-complex-stochastic traffic dynamics. This demo is only a short trailer of what is being developed and what is going to come next. We made the vehicle first negotiate randomly placed static vehicles, bikes and cones on the road. Then in the next section of the road, our engineering team started cutting the path of autonomous vehicle at random, and let it take care of obstacles avoidance and negotiation, balancing aggressiveness and passivity. These algorithmic frameworks are being scaled up to achieve Level-4 and Level-5 autonomous driving, in the most complex of traffic situations imaginable in the world for #autonomousvehicles, i.e., Indian traffic on Indian roads, to conquer this space globally. The speed of the vehicle was kept low in this demo, keeping in mind the safety of the guests. The core motion planning and decision making algorithm in the demo utilized one #reinforcementlearning agent. There is going to be another demo on similar lines next week, on an extended stretch of a road. #deeplearning #India

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Shawn Ryan

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MATLAB

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Hamid

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In locations ranging from the Alaska tundra and Arizona desert, to the mountains of New Zealand and sub-zero testing chambers, we have tested the Lucid Gravity in some of the most extreme conditions on earth. No matter the weather outside your door, your Lucid Gravity will be ready every time. Combine this with our seating for seven adults and all their cargo, incredible driving dynamics on any surface, and unrivaled efficiency and performance. No where else can you find all of this in a single vehicle. Learn more and order yours today:

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Tsla Chan

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WPP, the world's largest advertising company, is collaborating with NVIDIA, Boston Dynamics, and Canon to explore robotic-assisted cinematography and virtual ad production. Here are some cool shots of Atlas on the set.

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