# Aeronautical and astronautical engineering Published articles for Aeronautical and astronautical engineering. 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. ## MIT's JARVIS Challenge tests AI copilots in jet-engine design and manufacturing DevFeed: [MIT's JARVIS Challenge tests AI copilots in jet-engine design and manufacturing]() Original publisher: [Read original article]() Author: Department of Aeronautics and Astronautics Published: 2026-07-14T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI Development](), [Artificial Intelligence](), [Hardware](), [Software Engineering](), [Large language models (LLMs)]() Tags: [3-d-printing](), [aeronautical-and-astronautical-engineering](), [aerospace](), [ai-and-rapid-prototyping](), [ai-copilots](), [ai-native-engineer](), [aircraft](), [andreea-bobu](), [artificial-intelligence](), [chatgpt](), [classes-and-programs](), [claude](), [contests-and-academic-competitions](), [design](), [design-build-test-cycle](), [education-teaching-academics](), [engineering](), [gas-turbine-aero-engine](), [generative-ai](), [hardware](), [independent-activities-period](), [jet-engines](), [large-language-models-llms](), [lincoln-laboratory](), [logistics](), [machine-learning](), [manufacturing](), [masha-folk](), [mechanical-engineering](), [mit-aeroastro](), [mit-gas-turbine-laboratory](), [mit-iap](), [mit-jarvis-challenge](), [mit-lincoln-laboratory](), [mit-meche](), [mit-motorsports-team](), [mit-parley](), [mit-rocket-team](), [robotics](), [school-of-engineering](), [stem-education](), [students](), [undergraduate](), [zachary-cordero](), [zoltan-spakovszky]() ### AI overview MIT's JARVIS Challenge asked undergraduate teams to design, fabricate, assemble, and test small gas turbine aero engines with AI as their primary engineering partner. The challenge found that AI could accelerate parts of safety-critical hardware engineering, while engineering judgment remained essential and manufacturing was the main rate-limiting step. ### Source excerpt MIT students designed, built, and tested a jet engine with AI copilots, assessing AI's usefulness in developing high-performance aerospace systems. ## 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.