Roads are complex, hiding many challenging scenarios for advanced ADAS systems to handle.
Mobileye REM™ provides a rich road intelligence representation built from multiple layers of data. Lane markings, drivable paths, road edges, vehicle motion patterns, driving behavior d attributes combine to create a detailed understanding of the driving environment.
This comprehensive road understanding helps support ADAS and autonomous driving systems across a wide range of real-world challenging conditions.
In this video, see how REM predicts the drivable trajectories at a toll booth, laying out data in advance to prepare for road borders, lane structure, crowd speed and a clear drivable path as the road merges on this multi-level bridge.
Mobileye REM™ provides a rich road intelligence representation built from multiple layers of data. Lane markings, drivable paths, road edges, vehicle motion patterns, driving behavior d attributes combine to create a detailed understanding of the driving environment.
This comprehensive road understanding helps support ADAS and autonomous driving systems across a wide range of real-world challenging conditions.
In this video, see how REM predicts the drivable trajectories at a toll booth, laying out data in advance to prepare for road borders, lane structure, crowd speed and a clear drivable path as the road merges on this multi-level bridge.
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Today Mobileye released financial results for the third quarter of 2026 and provided a business update.
Read more here: bit.ly/4idwRQh
Read more here: bit.ly/4idwRQh
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Animal-related collisions remain a significant safety challenge, particularly on rural roads where higher speeds, limited visibility, and unpredictable animal behavior leave drivers with little time to react.
Our latest blog explores why these wildlife collisions can occur, difficult road hazards, and how modern ADAS technologies are designed to support attentive drivers in detecting and responding to potential collisions: okt.to/TLABlU
Our latest blog explores why these wildlife collisions can occur, difficult road hazards, and how modern ADAS technologies are designed to support attentive drivers in detecting and responding to potential collisions: okt.to/TLABlU
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Amnon Shashua and Shai Shalev-Shwartz have published new research on a fundamental challenge in AI—alignment, ensuring that better performance on training objectives translates into the behavior we actually intend.
In self-driving, this means asking whether better imitation of human drivers translates into safe, comfortable driving—including in rare situations.
Their paper introduces a mathematical framework for studying whether AI can generalize from training requirements to unseen requirements.
Shai explains below.
@AmnonShashua @shai_s_shwartz
In self-driving, this means asking whether better imitation of human drivers translates into safe, comfortable driving—including in rare situations.
Their paper introduces a mathematical framework for studying whether AI can generalize from training requirements to unseen requirements.
Shai explains below.
@AmnonShashua @shai_s_shwartz
In Disney’s Fantasia (1940), Mickey Mouse enchants a broom to carry water.
The broom does exactly what it’s told. But things quickly spiral out of control.
This 85-year-old story captures one of the deepest unsolved problems in AI: alignment. 🧵
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NEW: Mobileye has achieved the first inspection certificate from safety auditor TÜV SÜD for its automatic emergency braking (AEB) development.
TÜV SÜD’s inspection certificate provides third-party confirmation that Mobileye’s AEB development process meets the requirements of f the Intended Functionality (SOTIF) standard.
It places Mobileye’s longstanding AEB safety-development practices in the framework of ISO 21448, raising the industry standard for safety development processes.
Read more: okt.to/bNVkPF
TÜV SÜD’s inspection certificate provides third-party confirmation that Mobileye’s AEB development process meets the requirements of f the Intended Functionality (SOTIF) standard.
It places Mobileye’s longstanding AEB safety-development practices in the framework of ISO 21448, raising the industry standard for safety development processes.
Read more: okt.to/bNVkPF
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The hardest part of scaling autonomous driving is not just collecting more data. It is learning from the rare, safety-critical scenarios that are easy to miss inside massive datasets.
Mobileye’s approach includes Meteor, which uses AI agents and vision-language models to help arios across our large-scale driving data.
Stay tuned as we explore how Meteor helps turn those discoveries into actionable learning signals.
Learn more: bit.ly/4BzbGjt
Mobileye’s approach includes Meteor, which uses AI agents and vision-language models to help arios across our large-scale driving data.
Stay tuned as we explore how Meteor helps turn those discoveries into actionable learning signals.
Learn more: bit.ly/4BzbGjt
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Thanks to @TheRideshareGuy for hosting another great event bringing together the AV ecosystem.
Mobileye was proud to serve as the official technology partner of the Urban Autonomy Summit in Austin this week, joining industry leaders working to bring safer, more efficient utions to cities.
Mobileye was proud to serve as the official technology partner of the Urban Autonomy Summit in Austin this week, joining industry leaders working to bring safer, more efficient utions to cities.
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Physical AI is quickly becoming one of the most important frontiers in AI, but building machines that can safely operate in the real world takes more than powerful models.
For Mobileye, the foundations of physical AI have been decades in the making through autonomous driving. re how the same principles behind safe, scalable AVs are now informing the next generation of intelligent systems, from robotaxis to humanoid robots.
Read more here: okt.to/v0iWax
For Mobileye, the foundations of physical AI have been decades in the making through autonomous driving. re how the same principles behind safe, scalable AVs are now informing the next generation of intelligent systems, from robotaxis to humanoid robots.
Read more here: okt.to/v0iWax
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Johann Jungwirth, EVP of Autonomous Vehicles at Mobileye, took the stage at The Autonomous Main Event to discuss what it takes to scale autonomous mobility.
From safety-by-design and redundant systems to fleet operations, regulation, accessibility, and consumer trust, he shared hs will be critical for moving autonomous mobility from validation to real-world deployment.
Watch the full session to learn more about Mobileye’s vision for safer, scalable autonomous mobility: okt.to/gtx6QU
From safety-by-design and redundant systems to fleet operations, regulation, accessibility, and consumer trust, he shared hs will be critical for moving autonomous mobility from validation to real-world deployment.
Watch the full session to learn more about Mobileye’s vision for safer, scalable autonomous mobility: okt.to/gtx6QU
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At AutoSens Europe, Mobileye Senior Principal Engineer Avi Hendler unpacked one of autonomous driving’s hardest challenges: the long tail.
The difficult scenarios are not always the obvious ones. They can be rare, unexpected, or simply easy to miss in a dataset, and they often ession, "From Edge Cases to Scalable Intelligence: Driving Long-Tail Learning with VLMs", Avi introduced Meteor, an in-house framework for finding and analyzing long-tail driving scenarios. Using vision-language models, it helps identify rare events, investigate why a network fails, and surface the right examples to bring back into training.
The focus is not simply on adding more data, but on finding the data that can help systems learn and improve at scale.
Watch the full session here: okt.to/dnEYyj
The difficult scenarios are not always the obvious ones. They can be rare, unexpected, or simply easy to miss in a dataset, and they often ession, "From Edge Cases to Scalable Intelligence: Driving Long-Tail Learning with VLMs", Avi introduced Meteor, an in-house framework for finding and analyzing long-tail driving scenarios. Using vision-language models, it helps identify rare events, investigate why a network fails, and surface the right examples to bring back into training.
The focus is not simply on adding more data, but on finding the data that can help systems learn and improve at scale.
Watch the full session here: okt.to/dnEYyj
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Great to be at @AutoSens_ Europe, where Mobileye experts shared how AI can better understand the complex, dynamic conditions of the physical world.
From evaluating how automotive cameras perform in motion and challenging lighting conditions, to discovering rare scenarios that pact on driving safety, to advancing driver and occupant monitoring beyond compliance—real-world performance matters.
Building safer, more capable driving systems means looking beyond averages: identifying the cases that matter, testing against real-world conditions, and improving how vehicles understand both the road and the people inside them.
Thank you to everyone who joined Avi Hendler, Gabriel Bowers and Elad Osherov for the conversations.
From evaluating how automotive cameras perform in motion and challenging lighting conditions, to discovering rare scenarios that pact on driving safety, to advancing driver and occupant monitoring beyond compliance—real-world performance matters.
Building safer, more capable driving systems means looking beyond averages: identifying the cases that matter, testing against real-world conditions, and improving how vehicles understand both the road and the people inside them.
Thank you to everyone who joined Avi Hendler, Gabriel Bowers and Elad Osherov for the conversations.
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Approximately 95% of public roads in the U.S. and Europe are covered by Mobileye REM™, with more than 34 billion miles mapped.
Aggregated real-world driving data helps support continuously updated maps designed to support localization, map freshness, change detection, and conditions evolve.
Learn more about Mobileye REM™: okt.to/m3H96n
Aggregated real-world driving data helps support continuously updated maps designed to support localization, map freshness, change detection, and conditions evolve.
Learn more about Mobileye REM™: okt.to/m3H96n
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Physical AI is designed for the real world, where conditions are constantly changing and the complexity of physical environments cannot be fully scripted or predicted.
Mobileye’s approach to physical AI is built on safety by architecture, edge-case led learning, a purpose-built engineering.
Together, these principles are designed to enable autonomous vehicles and humanoids to perform in complex physical environments.
Learn more: okt.to/SMesxK
Mobileye’s approach to physical AI is built on safety by architecture, edge-case led learning, a purpose-built engineering.
Together, these principles are designed to enable autonomous vehicles and humanoids to perform in complex physical environments.
Learn more: okt.to/SMesxK
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New roads, new driving environments, and real-world validation for hands-free, eyes-on driving. Powered by EyeQ™6H and production-designed hardware, our development vehicle tested Mobileye SuperVision™ across central U.S. urban streets, highways and varied conditions.
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We'll be in Barcelona for @AutoSens_ Europe 2026.
Across three sessions, Mobileye experts will be sharing insights on tackling the long tail in autonomous driving, enhancing in-cabin sensing with exterior intelligence, and innovation in automotive imaging.
If you're 📅 Wednesday, 23 September | 3:20 p.m. CEST
Avi Hendler
From Edge Cases to Scalable Intelligence: Driving Long-Tail Learning with VLMs
📅 Thursday, 24 September | 9:50 a.m. CEST
Elad Osherov
Panel Discussion - Beyond Compliance: The Next Generation of Driver & Occupant Monitoring
📅 Thursday, 24 September | 11:25 a.m. CEST
Gabriel Bowers
Motion-Aware Characterization of Automotive HDR Cameras: Quantifying the Trade-Off Between Flicker Mitigation and Dynamic Resolution
Check out more info here: okt.to/rSdWzJ
Across three sessions, Mobileye experts will be sharing insights on tackling the long tail in autonomous driving, enhancing in-cabin sensing with exterior intelligence, and innovation in automotive imaging.
If you're 📅 Wednesday, 23 September | 3:20 p.m. CEST
Avi Hendler
From Edge Cases to Scalable Intelligence: Driving Long-Tail Learning with VLMs
📅 Thursday, 24 September | 9:50 a.m. CEST
Elad Osherov
Panel Discussion - Beyond Compliance: The Next Generation of Driver & Occupant Monitoring
📅 Thursday, 24 September | 11:25 a.m. CEST
Gabriel Bowers
Motion-Aware Characterization of Automotive HDR Cameras: Quantifying the Trade-Off Between Flicker Mitigation and Dynamic Resolution
Check out more info here: okt.to/rSdWzJ
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Germany continues to set itself as one of the leading global markets in autonomous mobility, and Mobileye Drive™ is helping lead the way.
Official KBA data shows that more than 60% of Germany’s valid SAE Level 4 testing permits include Mobileye Drive™, our fully autonomous s a Service.
As autonomous transportation moves closer to commercial deployment, we’re proud to push for a future of safer, scalable driverless mobility.
Read more here: okt.to/m4rLsF
Official KBA data shows that more than 60% of Germany’s valid SAE Level 4 testing permits include Mobileye Drive™, our fully autonomous s a Service.
As autonomous transportation moves closer to commercial deployment, we’re proud to push for a future of safer, scalable driverless mobility.
Read more here: okt.to/m4rLsF
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The hardest part of scaling autonomous driving is not just collecting more data. It is learning from the rare, safety-critical scenarios that are easy to miss inside massive datasets.
At @CVPR, Mobileye CTO Prof. @shai_s_shwartz introduced Meteor, Mobileye’s approach to ng AI agents and vision-language models to discover difficult scenarios, identify reproducible failure patterns, and generate targeted variations for training and validation.
Watch the presentation to see how Mobileye is tackling one of autonomy’s biggest challenges: turning edge cases into scalable intelligence: youtube.com/watch?v=JTPpHu…
At @CVPR, Mobileye CTO Prof. @shai_s_shwartz introduced Meteor, Mobileye’s approach to ng AI agents and vision-language models to discover difficult scenarios, identify reproducible failure patterns, and generate targeted variations for training and validation.
Watch the presentation to see how Mobileye is tackling one of autonomy’s biggest challenges: turning edge cases into scalable intelligence: youtube.com/watch?v=JTPpHu…
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Mobileye’s next chapter in Physical AI is about turning advanced intelligence into robust, useful and economically viable systems in the real world. Humanoid robotics brings that challenge into a new domain.
In an exclusive interview with @IZONmedia, @liorwolf, EVP of Humanoids hy Mentee by Mobileye bet on simulation from day one, why it develops hardware and software together to help learned skills transfer reliably into the real world, and what it will take to build a truly general-purpose robot.
He also discusses how Mentee’s startup speed and vertically integrated approach can combine with Mobileye’s experience in productization, supply chain and commercial scale.
Read the full interview here:
okt.to/oFbMBl
In an exclusive interview with @IZONmedia, @liorwolf, EVP of Humanoids hy Mentee by Mobileye bet on simulation from day one, why it develops hardware and software together to help learned skills transfer reliably into the real world, and what it will take to build a truly general-purpose robot.
He also discusses how Mentee’s startup speed and vertically integrated approach can combine with Mobileye’s experience in productization, supply chain and commercial scale.
Read the full interview here:
okt.to/oFbMBl
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