# autonomous vehicles Autonomous vehicles use automated driving systems with sensing, computing, and actuation modules to perform part or all of the dynamic driving task. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## 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.