# Driving Published articles for Driving. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Thinking Like A Naturalist: An Interview with Claire Barrett DevFeed: [Thinking Like A Naturalist: An Interview with Claire Barrett]() Original publisher: [Read original article]() Author: J Simpson Published: 2026-09-15T07:00:00Z Content type: article Language: en Sources: [Nordic APIs]() Topics: [Artificial Intelligence](), [systems](), [Testing]() Tags: [2026](), [ai](), [ai-agents](), [ai-readiness](), [api-governance](), [api-integration](), [api-security](), [artificial-intelligence](), [awareness](), [blog](), [digital-transformation](), [driving](), [generative-ai](), [governance](), [integration](), [summit](), [systems](), [testing]() ### AI overview Claire Barrett discusses the mindset shift needed for AI-enabled strategies, emphasizing systems thinking, governance, testing, awareness, and attention to long-term risks. ### Source excerpt Ahead of Nordic APIs Summit 2026, we catch up with speaker Claire Barrett on the mindset shift needed for adopting AI-enabled strategies. "Imagine driving around an old town," responds Claire Barrett when asked to describe what it means to be AI-ready from an integration perspective. "Imagine a European city that's been inhabited for thousands of ... ## 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 [...] ## On ACLs and AI-Generated Device Configurations DevFeed: [On ACLs and AI-Generated Device Configurations]() Original publisher: [Read original article]() Published: 2026-08-11T05:11:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [Artificial Intelligence]() Tags: [ai](), [driving](), [netlab](), [qos](), [youtube]() ### AI overview The article introduces a NetworkAutoMagic episode discussing ACL complexity, QoS, and the pitfalls of AI-generated network device configurations, with related notes and video options mentioned. ### Source excerpt Last Friday, I had a lovely chat with Steinn Bjarnarson and Urs Baumann, resulting in the NetworkAutoMagic episode 11. We couldn't avoid mentioning netlab, the seven layers of ACL hell (which is still balmy compared to the QoS hell), and the gotchas of AI-generated device configurations. Fortunately, I don't have to go into more details; Steinn published extensive notes, and if you don't feel like listening to us while driving, you can waste time watching us on YouTube. ## Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super DevFeed: [Generate Trajectories, Reasoning Traces, and Auto-Labels with NVIDIA Alpamayo 2 Super]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-04T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [foundation-models](), [Cosmos](), [model-deployment](), [Reinforcement learning](), [Inference](), [GitHub](), [hugging face](), [Open Source Models & Datasets]() Tags: [accelerate](), [automotive-transportation](), [autonomous](), [benchmarks](), [cosmos](), [customization](), [data](), [developer-tools-techniques](), [developers](), [development](), [diffusion](), [drive](), [driving](), [evaluation](), [featured](), [fine-tuning](), [generate](), [generation](), [github](), [hugging-face](), [inference](), [learning](), [models](), [nvidia](), [reasoning](), [robot-navigation](), [robotics](), [simulation-modeling-design](), [top-stories]() ### AI overview NVIDIA Alpamayo 2 Super is an open 34-billion-parameter reasoning vision-language-action model for autonomous vehicle development. It combines NVIDIA Cosmos 3 Super Reasoner with a diffusion-based Action Expert to generate trajectories, reasoning traces, meta-actions, scene answers, and auto-labels across development workflows. ### Source excerpt Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding, and data... ## The power of collaboration: How we can reduce traffic congestion DevFeed: [The power of collaboration: How we can reduce traffic congestion]() Original publisher: [Read original article]() Published: 2026-07-07T16:42:08Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Algorithms, Complexity](), [Optimization](), [Routing (disambiguation)](), [App](), [Artificial Intelligence]() Tags: [algorithms](), [algorithms-theory](), [climate-sustainability](), [collaboration](), [data-mining-modeling](), [driving](), [google](), [optimization](), [reduce](), [routing](), [transportation](), [us]() ### AI overview Google Research describes a large-scale routing experiment in 10 major US cities. By guiding a small fraction of trips toward alternative routes, the study reports improved overall traffic conditions, faster driving speeds, and reduced emissions. ### Source excerpt Algorithms & Theory ## 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... ## Powering self-driving vehicle analytics at Avride with ClickHouse Cloud DevFeed: [Powering self-driving vehicle analytics at Avride with ClickHouse Cloud]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-05-11T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [data](), [Apache Iceberg](), [telemetry](), [Grafana]() Tags: [analytics](), [apache-iceberg](), [autonomous](), [aws](), [clickhouse](), [cloud](), [data-infrastructure](), [driving](), [grafana](), [latency](), [lidar](), [metrics](), [robots](), [storage](), [streams](), [telemetry](), [tooling](), [visualization]() ### AI overview Avride uses ClickHouse Cloud as the data backbone for its autonomous vehicles and delivery robots, supporting ride-data indexing, metrics, analytics, and internal tooling. Its migration from Apache Iceberg reduced index lookup and ingestion latency. ### Source excerpt Avride replaced Apache Iceberg with ClickHouse Cloud, cutting index lookup latency from 20 seconds to under 100ms and ingestion from hours to seconds. ## Beyond A/B Testing: Using Surrogacy and Region-Splits to Measure Long-Term Effects in Marketplaces DevFeed: [Beyond A/B Testing: Using Surrogacy and Region-Splits to Measure Long-Term Effects in Marketplaces]() Original publisher: [Read original article]() Author: Iraklikhorguani Published: 2026-03-25T13:56:39Z Content type: article Language: en Sources: [Lyft Engineering - Medium]() Topics: [A/B Testing](), [App](), [Optimization]() Tags: [a-b-testing](), [app](), [cost](), [drivers](), [driving](), [growth](), [optimization](), [payments](), [pricing](), [resources](), [revenue](), [testing]() ### AI overview Lyft describes why standard A/B tests may not capture the long-term and market-mediated effects of pricing, payments, and incentive decisions in its multi-sided marketplace. The article introduces surrogacy and region splits as approaches for measuring those effects. ### Source excerpt Image generated with Gemini 3 Pro (Google), 2026. Written by Amber Wang and Yoonji Kim at Lyft. Background Whenever you use the Lyft app, there is a complex balancing act happening behind the scenes. Various levers are used to keep the marketplace running smoothly; Base prices and coupons for riders affect demand, while driver pay and bonuses impact the level of available supply. Since every change to prices and payments impacts Lyft's costs and revenue, they lead to key optimization problems, such as: How should we allocate budget between driver incentives and rider incentives? How do we invest resources to achieve x% rides growth, and how much does it cost in terms of short term profit? These are the questions the Foundational Models team at Lyft tries to answer in a systematic way. A key ingredient is understanding the effects of different types of investments -- for instance, what will happen if we increase the total budget for driver incentives by x%? What will happen if we increase the rider price of all rides by y%? It's worth noting that the long term effects of such decisions tend to dominate the short term effects: we may earn more short term profit from a ride if we charge riders more and pay drivers less, but lose riders and drivers in the long run. Estimating the long term effects of resource allocation decisions is challenging in a multi-sided marketplace such as Lyft. Because these decisions tend to be consequential, their effects go beyond first order effects on directly affected users. For example, if we increase driver incentive spending by x% in week 1, drivers will drive more in week 1 (short term effect), and may return to drive a bit more in the following weeks (direct long term effects). But this is not the full picture: in week 1, when there is a positive increase in driver hours as the result of more incentives, riders will enjoy better experiences (e.g. less surge pricing, shorter wait times) and may want to return to Lyft in the future. How ## How continuous product discovery works for us DevFeed: [How continuous product discovery works for us]() Original publisher: [Read original article]() Author: Sören Weber Senior Product Manager @ trivago; Core Product; AI Linkedin profile Published: 2023-02-01T00:00:00Z Content type: article Language: en Sources: [Trivago]() Topics: [Product Management](), [Self-organizing Team](), [Agile](), [User experience (UX)](), [Development]() Tags: [cross-functional-teams](), [delivery](), [development](), [discovery](), [driving](), [engineering](), [good-practices](), [product-management](), [research](), [scope](), [software](), [strategy]() ### AI overview A trivago product manager explains continuous product discovery, distinguishing it from product delivery and describing how user research, solution ideation, and solution testing help teams decide what to build. The article also discusses trivago's adoption of Teresa Torres's continuous discovery framework alongside cross-functional teams, outcome-driven OKRs, and agile software development. ### Source excerpt Hello, I am a product manager here at trivago. I have worked on different parts of the product such as apps, alternative accommodations, landing pages, and search & flow. We work in cross-fu... ## The Appsec Landscape in 2023 DevFeed: [The Appsec Landscape in 2023]() Original publisher: [Read original article]() Author: Adam Published: 2023-01-05T00:00:00Z Content type: article Language: en Sources: [Shostack & Friends Blog]() Topics: [Application Security](), [Security](), [Requirements](), [open-source-security]() Tags: [appsec](), [changes](), [circleci](), [driving](), [external](), [government](), [legacy](), [requirements](), [secure-software](), [ssdf](), [supply-chain]() ### AI overview This article examines how economic conditions, new regulations, and engineering considerations may shape application security decisions in 2023. It emphasizes threat modeling, secure software development requirements, legacy code, and software supply-chain concerns. ### Source excerpt External changes will be driving appsec in 2023. It's time to frame the decisions in front of you. ## 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.