# autonomous vehicles Published articles for autonomous vehicles. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies DevFeed: [Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies]() Original publisher: [Read original article]() Author: Ali Kani Published: 2026-09-10T16:00:04Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Training AI Models](), [SDKs]() Tags: [ai-models](), [ai-training](), [artificial-intelligence](), [autonomous-vehicles](), [cosmos](), [customer-stories](), [datasets](), [driving](), [mobility](), [nvidia](), [nvidia-blackwell](), [nvidia-dgx](), [nvidia-drive](), [nvidia-halos](), [omniverse](), [physical-ai](), [simulation-and-design]() ### AI overview NVIDIA describes an open robotaxi platform for training AI driving models, simulation and safety validation, and real-time in-vehicle computing. ### Source excerpt The global robotaxi market -- physical AI's first commercial breakthrough -- is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world's busiest and most complex streets. Deploying a driverless vehicle is one challenge. Scaling a fleet is [...] ## System helps humans predict when self-driving cars will make mistakes DevFeed: [System helps humans predict when self-driving cars will make mistakes]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-02T15:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [autonomous vehicles](), [Deep learning](), [Artificial Intelligence](), [Robotics](), [Simulation](), [Computer science]() Tags: [aeronautical-and-astronautical-engineering](), [ai](), [artificial-intelligence](), [autonomous-vehicles](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [concept-wrapper-network](), [cw-net](), [deep](), [electrical-engineering-and-computer-science-eecs](), [eoin-kenny](), [human-computer-interaction](), [julie-shah](), [laura-major](), [machine-learning](), [mit-schwarzman-college-of-computing](), [momchil-tomov](), [motional](), [research](), [robotics](), [safety](), [school-of-engineering](), [self-driving](), [self-driving-cars](), [simulation](), [transparency]() ### AI overview MIT and Motional researchers developed CW-Net, a method that translates an autonomous vehicle's deep-learning decisions into understandable concepts. Tests found that the explanations helped safety drivers and nonexpert users better predict vehicle behavior. ### Source excerpt A new method, called CW-Net, translates the reasoning process of an autonomous vehicle's AI system into understandable concepts that explain its behavior. ## Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec DevFeed: [Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Nvidia](), [synthetic-data](), [Simulation and Design](), [data](), [configuration]() Tags: [3d](), [autonomous](), [autonomous-vehicles](), [data](), [developer-tools-techniques](), [developers](), [development](), [model](), [models](), [nvidia](), [omniverse](), [performance](), [physical-ai](), [post](), [robotics](), [scale](), [simulation-modeling-design](), [synthetic-data](), [training](), [tutorial]() ### AI overview This tutorial explains how to adapt an autonomous-vehicle perception stack across carline and sensor-rig variants using existing real-world drives. It presents a four-step workflow with NVIDIA Omniverse NuRec: pair a reconstructed drive with a target rig, render target camera views, refine the frames with NVIDIA Harmonizer, and train a perception model on the output. ### Source excerpt A perception stack is shaped by the vehicle that carries it. Move the same software to a new carline--for example, from an SUV to a sedan or another vehicle... ## Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps DevFeed: [Integrate NVIDIA Omniverse RTX Sensor Simulation Into Existing Apps]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-07-20T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [SDKs]() Tags: [3d](), [agent-skill](), [apis](), [apps](), [autonomous-vehicles](), [c](), [featured](), [lidar](), [nvidia](), [omniverse](), [openusd](), [physical-ai](), [python](), [robotics](), [simulation-modeling-design]() ### AI overview The article explains how to integrate NVIDIA Omniverse's ovrtx RTX sensor-simulation library into existing applications. It covers using its C and Python SDK to generate camera, lidar, radar, and related sensor outputs from OpenUSD scenes. ### Source excerpt Developers building 3D, design, simulation, robotics, and industrial digital twin applications need ways to bring physical AI capabilities into the tools and... ## Tiny robot boats build floating structures DevFeed: [Tiny robot boats build floating structures]() Original publisher: [Read original article]() Author: Rachel Gordon | MIT CSAIL Published: 2026-07-09T15:50:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Robotics](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)](), [Computer science]() Tags: [alejandro-gonzalez-garcia](), [aquatic-robots](), [architecture](), [artificial-intelligence](), [autonomous-vehicles](), [bioinspiration](), [cities](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [daniela-rus](), [electrical-engineering-and-computer-science-eecs](), [fire-ants](), [floatform](), [floating-infrastructure](), [hybrid-coordination](), [infrastructure](), [machine-learning](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [mit-sea-grant](), [mit-sea-grant-program](), [mit-senseable-city-lab](), [modular-robotic-boats](), [modular-self-reconfigurable-robots](), [niklas-hagemann](), [nonlinear-hydrodynamics](), [raft](), [research](), [robot-boats](), [robot-self-assembly](), [robotics](), [robots](), [school-of-architecture-and-planning](), [school-of-engineering](), [science](), [self-reconfiguration](), [senseable-city-lab](), [snap](), [swarm](), [swarm-robots](), [tiny](), [transportation](), [ultrasonic-beacon-system](), [urban-studies-and-planning](), [water](), [wei-wang]() ### AI overview MIT researchers developed FloatForm, a swarm of small robotic boats that can assemble into reconfigurable structures on the water and later break apart and reassemble into new configurations with minimal human direction. ### Source excerpt MIT researchers developed FloatForm, a swarm of small aquatic robots that snap together like ants forming a raft, assembling into reconfigurable structures on the water. ## Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools DevFeed: [Optimizing a Neural Reconstruction Pipeline Using NVIDIA Nsight Developer Tools]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-06-30T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Omniverse](), [Optimization](), [Tooling](), [GPU](), [Nvidia](), [CUDA](), [PyTorch](), [Physical AI](), [Robotics](), [Synthetic Data Generation](), [real-time](), [Reinforcement learning]() Tags: [3d](), [ai](), [autonomous](), [autonomous-vehicles](), [cuda](), [developer-tools](), [developer-tools-techniques](), [driving](), [engineering](), [featured](), [gpu](), [lidar](), [machine-learning](), [nvidia](), [omniverse](), [optimization](), [performance](), [physical-ai](), [real-time](), [reinforcement-learning](), [simulation-modeling-design](), [synthetic-data-generation]() ### AI overview This article explains how NVIDIA Nsight Developer Tools can optimize the NVIDIA Omniverse NuRec neural reconstruction pipeline. It focuses on reducing GPU-intensive reconstruction and rendering costs to improve engineering iteration and move toward real-time performance. ### Source excerpt NVIDIA Omniverse NuRec is a neural reconstruction pipeline for building high-fidelity 3D representations of real-world environments from multisensor data such... ## New chip could help tiny robots traverse complex environments DevFeed: [New chip could help tiny robots traverse complex environments]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-06-23T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Hardware](), [Algorithms](), [3D](), [navigation](), [real-time](), [Green Software](), [Computer science]() Tags: [3d](), [aeronautical-and-astronautical-engineering](), [algorithms](), [artificial-intelligence](), [assembly](), [augmented-and-virtual-reality](), [autonomous-vehicles](), [computer-chips](), [computer-science-and-technology](), [efficiency](), [electrical-engineering-and-computer-science-eecs](), [energy](), [energy-efficiency](), [gleanmer](), [hardware](), [inside](), [laboratory-for-information-and-decision-systems-lids](), [low-power](), [machine-learning](), [memory](), [mit-schwarzman-college-of-computing](), [national-science-foundation-nsf](), [navigation](), [peter-zhi-xuan-li](), [real-time](), [research](), [research-laboratory-of-electronics](), [robotics](), [school-of-engineering](), [sertac-karaman](), [system-on-a-chip](), [trajectory-planning](), [vivienne-sze](), [zih-sing-fu]() ### AI overview MIT researchers developed a low-power chip that combines an efficient mapping algorithm with dedicated hardware to generate detailed 3D maps for robot navigation in real time. The system-on-a-chip uses about 6 milliwatts and is intended for tiny autonomous robots and other battery-limited devices. ### Source excerpt Researchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power. ## MIT researchers develop a real-time spatiotemporal memory framework for robots DevFeed: [MIT researchers develop a real-time spatiotemporal memory framework for robots]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-06-17T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Robotics](), [Artificial Intelligence](), [Computer vision]() Tags: [3d-scene-graphs](), [aeronautical-and-astronautical-engineering](), [ai](), [algorithms](), [artificial-intelligence](), [autonomous-vehicles](), [computer-science-and-technology](), [computer-vision](), [daaam](), [describe-anything-anywhere-at-any-moment](), [electrical-engineering-and-computer-science-eecs](), [laboratory-for-information-and-decision-systems-lids](), [luca-carlone](), [machine-learning](), [map](), [memory](), [mit-schwarzman-college-of-computing](), [nicolas-gorlo](), [paper](), [real-time](), [research](), [robot-memory](), [robotic-perception](), [robotics](), [robots](), [school-of-engineering](), [spatial](), [spatiotemporal-mapping]() ### AI overview MIT researchers developed a long-term spatiotemporal memory framework that helps robots form and recall detailed models of large environments. The system combines map representations with language-based descriptions, answers environmental questions in plain language, and runs fast enough for real-time mobile-robot use. ### Source excerpt A new spatial memory system for robots efficiently captures details about the objects they see while exploring their environment. ## Real-time AI: what is it and why it needs streaming data DevFeed: [Real-time AI: what is it and why it needs streaming data]() Original publisher: [Read original article]() Author: Jenny Medeiros Published: 2025-09-04T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Artificial Intelligence](), [Streaming](), [real-time](), [Latency](), [autonomous vehicles](), [Transactions]() Tags: [agentic](), [ai](), [autonomous-vehicles](), [batch](), [latency](), [rag](), [reactive](), [real-time](), [streaming](), [thought-leadership](), [transactions]() ### AI overview The article explains real-time AI as systems that perceive, interpret, and act on streaming events with minimal delay. It contrasts this approach with batch-oriented AI and describes applications such as autonomous-vehicle navigation, fraud detection, and emergency-room prioritization. ### Source excerpt Learn how streaming data takes your AI from reactive to proactive and the real-world applications driving instant intelligence. ## Top AI agent use cases across industries | Redpanda DevFeed: [Top AI agent use cases across industries | Redpanda]() Original publisher: [Read original article]() Author: Artem Oppermann Published: 2025-06-10T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [AI Agent](), [Artificial Intelligence](), [autonomous vehicles](), [real-time](), [Reinforcement learning](), [Simulation](), [data](), [Security]() Tags: [ai](), [ai-agent](), [ai-agent-applications](), [ai-agent-capabilities](), [ai-agent-technology-advancements](), [ai-agent-use-cases](), [ai-agents-in-autonomous-vehicles](), [ai-decision-making-models](), [ai-for-efficiency-in-industries](), [ai-in-cybersecurity](), [autonomous-vehicles](), [camera](), [fundamentals](), [intelligent-virtual-assistant-ai](), [natural-language-processing-ai](), [radar](), [real-time](), [real-world-ai-agent-examples](), [reinforcement-learning](), [safety](), [sensor](), [sensor-fusion-in-ai-agents](), [sensors](), [simulation]() ### AI overview This article explains what distinguishes an AI agent from ordinary software and surveys AI agent use cases across industries. It focuses on autonomous vehicles, describing how perception, decision-making, control, sensor fusion, simulation, reinforcement learning, and safety protocols support real-time operation. ### Source excerpt Learn how AI agents are being used in the real world to solve problems, boost efficiency, and make work easier across different industries. ## NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets DevFeed: [NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets]() Original publisher: [Read original article]() Author: Ming-Yu Liu; Hanzi Mao; Jinwei Gu; Pranjali Joshi; Asawaree Published: 2025-03-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Cosmos](), [Physical AI](), [Artificial Intelligence](), [Robotics](), [Synthetic Data Generation](), [datasets](), [autonomous vehicles](), [foundation-models](), [Isaac](), [Omniverse](), [Open Source]() Tags: [ai](), [artificial-intelligence](), [autonomous-vehicles](), [community](), [cosmos](), [datasets](), [foundation-models](), [isaac](), [nvidia](), [omniverse](), [open-source](), [physical-ai](), [research](), [robotics](), [synthetic-data-generation]() ### AI overview NVIDIA announces Cosmos Transfer, an open world foundation model that generates controllable, photorealistic video scenes from structured inputs such as depth maps, trajectories, LiDAR scans, and 3D bounding boxes. Together with NVIDIA Omniverse, it supports synthetic data generation for robotics and autonomous vehicle development. NVIDIA also introduces an open Physical AI Dataset on Hugging Face and the Isaac GR00T N1 foundation model for humanoid robot reasoning. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Safety and Security in Automated Driving DevFeed: [Safety and Security in Automated Driving]() Original publisher: [Read original article]() Author: Adam Published: 2019-07-08T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [autonomous vehicles](), [Security](), [risk-management](), [Cybersecurity](), [Architecture & Design]() Tags: [automotive](), [autonomous-vehicles](), [driving](), [risk](), [risk-management](), [safety](), [security](), [systems]() ### AI overview This commentary examines how cybersecurity can be integrated into the established safety discipline for automated driving. It discusses threat modeling, minimal risk conditions, emergency stops, risk-treatment strategies, and risks associated with generic vehicle architectures. ### Source excerpt Let's explore the risks associated with Automated Driving.