Innoviz

What Innoviz Reveals About Next-Generation Counter-UAS

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By Yohai Schwiger

Innoviz this week announced another partnership in the counter-unmanned aerial systems (Counter-UAS) market, this time with Israeli company AeroNous, developer of an open command-and-control (C2) platform designed to protect critical infrastructure, strategic facilities and sensitive sites. Under the agreement, Innoviz’s LiDAR technology will be integrated into AeroNous’ C2 platform to enhance real-time localization by providing highly accurate 3D positioning of aerial targets.

At first glance, this appears to be just another integration announcement. Viewed alongside the company’s recent string of partnerships, however, it points to a much broader shift in how modern Counter-UAS systems are being designed.

The AeroNous announcement follows several similar collaborations unveiled in recent weeks. Regulus is integrating Innoviz LiDAR to improve drone tracking in complex environments; Givon Defense is using it to enhance precision localization; Cogniteam has incorporated it into a new AI-powered aerial target classification module; and AeroNous is now embedding it within an open C2 platform. While these are different companies addressing different applications, the announcements repeatedly emphasize the same concepts: Localization, Perception, 3D Spatial Awareness, Classification and Situational Awareness.

Taken together, these partnerships suggest something more significant than Innoviz’s expansion into defense. They highlight the emergence of a new generation of Counter-UAS architectures.

From Single Sensors to Multi-Sensor Perception

Until only a few years ago, most counter-drone systems were built around a single primary sensor. In some cases it was radar for initial detection; in others, electro-optical cameras or RF sensors capable of identifying communication links between a drone and its operator. Once the target was detected, the system activated an appropriate jammer or interceptor.

Today’s battlefield has changed dramatically.

Small FPV drones, autonomous drones operating without RF links, drone swarms and low-altitude flight through dense urban environments have exposed the limitations of that approach. The challenge is no longer simply detecting that an object exists in the air. Modern systems must rapidly determine what the object is, precisely where it is located, where it is heading and how its trajectory is likely to evolve over the next few seconds.

As a result, Counter-UAS platforms are increasingly adopting an architecture that closely resembles autonomous driving perception systems.

Rather than relying on a single sensor, they build a sensor fusion layer that combines multiple information sources. Radar provides long-range detection and tracking; RF sensors identify communication signals when available; EO/IR cameras contribute visual identification and classification; AI algorithms correlate the incoming data; and the command-and-control system produces a unified operational picture from which engagement decisions are made.

Within this architecture, LiDAR plays a distinctly different role from the other sensors.

Across nearly every Innoviz announcement, the technology is described not primarily as a detection sensor but as a localization and perception sensor. In other words, its purpose is not simply to indicate that an aerial threat exists, but to provide the system’s spatial perception layer.

LiDAR contributes information unavailable from the other sensors: a detailed 3D point cloud of the environment, precise spatial coordinates, distance, altitude, shape and motion. This enables highly accurate localization, continuous target tracking and real-time positional updates for the C2 platform.

In Cogniteam’s implementation, for example, the point cloud also serves as the foundation for an AI module capable of distinguishing drones from birds and other airborne objects, significantly reducing false alarms.

In this sense, LiDAR is becoming the perception layer of modern Counter-UAS systems. It does not replace radar, cameras or RF sensors. Instead, it complements them by providing the spatial information upon which sensor fusion and tracking algorithms depend.

It is also noteworthy that most of Innoviz’s recent partners are not building interceptors. AeroNous, Cogniteam and Regulus focus on software layers, perception engines and command-and-control platforms. This reflects a broader shift in the industry, where competitive advantage is increasingly moving away from the interceptor itself and toward the software architecture that fuses multiple sensors into a single operational model of the airspace.

Why LiDAR? Lessons from Autonomous Driving

The comparison with autonomous vehicles is difficult to ignore.

The automotive industry reached a similar conclusion years ago: no single sensor can safely perceive the surrounding environment. Radar excels at measuring range and velocity but provides limited shape information. Cameras deliver rich visual context but depend heavily on lighting conditions. LiDAR adds precise three-dimensional mapping. Sensor fusion combines all of these inputs into a unified representation of the world, enabling autonomous driving decisions.

Counter-UAS systems are now following a remarkably similar path.

The difference is that, instead of detecting pedestrians and vehicles, they must identify small, fast and often autonomous drones navigating among trees, buildings and power lines. Here too, success depends on maintaining an accurate, continuously updated 3D representation of the environment.

This may also explain why Innoviz’s technology appears particularly well suited to these applications.

For more than a decade, the company developed its LiDAR platform for one of the world’s most demanding perception challenges: autonomous driving. Beyond the optical hardware itself, Innoviz built an extensive software stack that includes firmware, signal processing, point cloud processing, perception algorithms and object-level data generation.

Rather than outputting millions of raw laser points, the system can produce structured object data—including position, velocity and direction—ready for integration into higher-level command-and-control software.

The hardware itself offers additional advantages. Automotive-grade LiDAR must operate continuously under harsh environmental conditions while maintaining high reliability, low power consumption and resistance to vibration and temperature extremes—qualities equally valuable in military applications.

Ultimately, Innoviz’s recent partnerships reveal less about the LiDAR market than about the future direction of Counter-UAS technology.

The next generation of systems is no longer being built around a single sensor or a single interceptor. Instead, it relies on a multi-sensor perception architecture in which each component contributes a different layer of information to a unified operational picture.

If the previous objective was simply to detect a drone, the new objective is to construct an accurate, continuously updated three-dimensional model of the airspace—and LiDAR is rapidly becoming one of the key building blocks of that architecture.

Innoviz Appoints Former Rafael CEO Yoav Har-Even to Its Board of Directors

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By Yohai Schwiger

Innoviz Technologies has appointed Yoav Har-Even to its Board of Directors. Har-Even replaces James Sheridan, who stepped down after Perception Capital Partners’ right to appoint a representative to the board expired.

According to the company, Har-Even’s extensive experience in defense, autonomous systems, and business development will help accelerate Innoviz’s expansion into the defense and homeland security markets. Chairman Amichai Steinberg described these sectors as one of the company’s key strategic growth engines for the coming years.

Har-Even is widely regarded as one of Israel’s leading defense industry executives. He served as President and CEO of Rafael Advanced Defense Systems from 2016 to 2024, overseeing the company’s international expansion and the development and commercialization of advanced defense systems, including Iron Dome, David’s Sling, and the Trophy active protection system. Prior to Rafael, he served for decades in the Israel Defense Forces, retiring with the rank of Major General after holding several senior positions, including Head of the Operations Directorate.

Since leaving Rafael, Har-Even has expanded his business activities. He was appointed CEO of energy company PowerGen Energy, joined the advisory board of Ondas Autonomous Systems—a defense technology holding company that has recently completed several acquisitions in Israel—and, in early 2026, became Chairman of the Board of Sindiana Technologies. With his appointment to Innoviz, he adds another strategic board role to his portfolio.

The appointment comes as Innoviz is actively expanding beyond its traditional automotive business. For years, the company was identified almost exclusively with the automotive LiDAR market. Over the past year, however, it has been building a second growth engine focused on defense, perimeter security, and the protection of critical infrastructure.

A major milestone in that strategy was the launch of InnovizSMART, a version of the company’s LiDAR technology tailored for defense, perimeter security, and infrastructure protection applications. Innoviz has since announced its first commercial partnerships in the sector, including an agreement with Israeli defense technology company Kela, which develops AI-powered operational systems, as well as a collaboration with the Drive Group on the Barak LightGuard platform for border security and critical infrastructure protection. Under that agreement, Drive placed an initial purchase order and set a sales target of approximately $20 million by the end of 2027.

Against this backdrop, Har-Even’s appointment appears to be part of a broader strategic shift rather than an isolated governance change. Beyond his executive experience, he brings deep familiarity with defense procurement processes, government customers, and global defense markets. For Innoviz, his addition further strengthens the board in line with the company’s evolving strategic direction.

What Does the Appointment of a Director Actually Mean?

Announcements of board appointments are common, yet their significance is often overlooked. A director is not involved in a company’s day-to-day operations or routine management decisions. Instead, the board is responsible for setting the company’s strategic direction, overseeing management, approving major corporate decisions, and contributing experience, industry expertise, and valuable business relationships. As a result, companies often recruit former CEOs, senior industry executives, investors, or recognized experts in areas they view as strategic priorities.

Board appointments can also signal where a company is heading. When a semiconductor company appoints a manufacturing expert—or when an autonomous vehicle technology company recruits the former CEO of one of the world’s leading defense contractors—the message may extend well beyond corporate governance. From the candidate’s perspective, joining a board is rarely a casual decision. While it is a non-executive role, it carries legal responsibilities and reputational risk, making such appointments a meaningful indication of confidence in the company’s long-term strategy.

Photo: Yoav Har-Even

Innoviz LiDAR Sensors to Be Integrated Into Dataspeed Autonomous Vehicle Platforms

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[In the photo: Dataspeed’s autonomous vehicle platform. Credit: Dataspeed]

Innoviz announced the expansion of its partnership with U.S.-based company Dataspeed, under which its InnovizSMART LiDAR sensor will be integrated into Dataspeed’s drive-by-wire platforms used to develop and test autonomous vehicles.

According to the announcement, Innoviz’s sensor will become an integral component of Dataspeed’s vehicle platforms, which enable full computer control over core vehicle systems—including steering, braking, and throttle—and are used by companies and organizations developing autonomous technologies. As part of the collaboration, Dataspeed will offer InnovizSMART sensors as part of the systems it supplies to customers, primarily in North America.

The joint solution targets a range of autonomy applications, including vehicles used in defense, agriculture, mining, industry, and ground robotics—sectors where autonomous vehicles often operate in challenging environments such as farmland, open-pit mines, and desert terrain.

Dataspeed, headquartered in Michigan, specializes in developing vehicle platforms designed for the testing and development of autonomous driving systems. The company provides drive-by-wire systems that allow computers to control vehicles through electronic interfaces, effectively transforming production vehicles into testing platforms for automotive companies, startups, universities, and research labs.

The company’s platforms are used by a wide range of organizations in the autonomy industry to integrate sensors, AI computers, and algorithms for the development of autonomous driving systems. According to Dataspeed, its technology has been deployed in more than 500 vehicles worldwide. Vehicle models converted using its platform include the Ford Fusion, Lincoln MKZ, Chrysler Pacifica, Jeep Grand Cherokee, and Ford Ranger.

Beyond civilian uses, Dataspeed’s platforms are also employed in defense and ground robotics projects, including development programs for government agencies and initiatives linked to the U.S. Army focused on autonomous ground vehicles.

The InnovizSMART sensor that will be integrated into Dataspeed’s platforms is a 3D LiDAR system designed primarily for industrial and autonomous applications outside the passenger-car market. Unlike the company’s flagship sensors—InnovizOne and InnovizTwo—designed for integration into production vehicles by automakers, InnovizSMART was developed specifically for markets such as robotics, agriculture, mining, and defense, where durability and operational flexibility are often more critical than strict automotive certification.

The sensor provides high-resolution, long-range three-dimensional mapping of the environment and is engineered to operate reliably in harsh conditions—including scenarios in which the sensor window may be covered with mud, water, or dust. These capabilities make it particularly suitable for autonomous vehicles operating in challenging environments such as mining sites, agricultural fields, or off-road terrain. The platform also enables Innoviz to address a broader market of autonomy and Physical AI applications beyond the traditional automotive industry.

Innoviz Projects Tenfold Surge in Security and Defense Revenue

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[Photo: Omer Keilaf, CEO of Innoviz. Credit: PR]

Innoviz estimates that its activity in the security and defense markets will grow more than tenfold in 2026, according to comments made during the company’s earnings call following the release of its annual results.

While revenue from non-automotive markets remains marginal — accounting for about 1% of total revenue in 2025 — the company expects these segments to represent roughly 10% of revenue in 2026, potentially amounting to several million dollars.

CEO Omer Keilaf linked the projection to a sharp increase in inbound interest from the defense sector. Speaking on the call, he described a recent event hosted at the company’s offices in Israel, attended by around 80 security consultants from airports, seaports and railway operators. “We probably left that event, only that event, with tens of opportunities,” he said.

The following week, Innoviz participated in a defense conference and, according to Keilaf, left with “a few tens of leads.”

The expansion is supported by the launch of InnovizSMART, a LiDAR sensor derived from the company’s automotive platform but tailored for perimeter security, critical infrastructure, smart cities and robotics applications. The company says the sensor offers long range and high resolution, enabling accurate detection and classification of objects even in complex environmental conditions and partially obscured areas. Innoviz recently reported initial deployments of the system at critical infrastructure sites in Israel.

Production Set to Increase 3–4x in 2026

Alongside its push into security markets, Innoviz is preparing a significant increase in manufacturing capacity. Keilaf said the company continues to ramp production with contract manufacturer Fabrinet, and expects 2026 production volumes to be three to four times higher than last year.

This scale-up is intended to support both autonomous vehicle programs — including collaborations with carmakers and truck manufacturers — and growth in non-automotive markets. The partnership with Fabrinet enables the company to transition from limited-volume manufacturing to full-scale serial production.

Humanoids and “World Models”: Why LiDAR Still Matters in the AI Era

Another key theme of the earnings call was the company’s positioning in what it calls “Physical AI” — encompassing robots, humanoids and intelligent infrastructure. Innoviz recently published the first part of a white paper outlining its view of LiDAR as a foundational layer for building “world models,” and plans to host a dedicated webinar on the subject in the coming weeks.

During the Q&A session, an analyst asked whether LiDAR remains necessary in an era of rapid AI advancement. Keilaf responded that the next stage of AI requires accurate, real-world data.

“When your AI is trained by data that is also provided by AI, you are creating a very big error that is inflated, and you create a bias in your models,” he said. High-resolution LiDAR, he argued, can “connect those world models … to life” by feeding them with real-time, accurate information from the physical world.

Keilaf added that no new dedicated product is required for humanoid robotics applications. The company’s existing automotive-grade LiDAR systems, developed to meet stringent reliability and safety standards, already meet the requirements of industrial and security markets.

Financial Summary

Innoviz closed 2025 with record revenue of $55.1 million — more than double the prior year. Gross margin reached 23%, compared with a negative margin in 2024. Operating expenses declined to $80.6 million from $100.8 million in 2024, a reduction of about 20%.

For 2026, the company forecasts revenue growth of approximately 27%, targeting a range of $67 million to $73 million.

InnovizSMART Security System Deployed at Critical Infrastructure Sites in Israel

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Israeli LiDAR developer Innoviz announced today, for the first time, that its security solution InnovizSMART has been operationally deployed across several critical-infrastructure sites in Israel. The disclosure marks the product’s first confirmed real-world installations. The system is designed to protect sensitive facilities using 3D LiDAR sensing combined with AI-based analytics. The company recently also reported ramping up mass production of the new platform.

According to Innoviz, the installations were carried out over the past three months and the system is now used to detect, classify and track potential threats around secured facilities. For Innoviz — best known for automotive LiDAR sensors — the move represents a major milestone in entering a new market: infrastructure and security protection.

Alongside the deployment announcement, the company released detailed technical performance data for the first time. InnovizSMART can detect and classify objects at ranges exceeding 450 meters while scanning more than one million data points per second to generate an accurate 3D situational picture.

The system relies on a LiDAR sensor that creates a virtual perimeter fence around a protected site. When suspicious movement is detected within predefined zones, it can automatically activate surveillance cameras for visual verification and distinguish in real time between different target types — humans, vehicles or animals. The company highlights its ability to detect movement even behind physical obstacles such as trees and bushes, as well as operate in challenging weather conditions.

The platform integrates with existing enterprise security systems, including video-management and access-control platforms, and can track multiple targets simultaneously — a key requirement in complex security environments.

InnovizSMART originated as an adaptation of the company’s core automotive technology to the security sector. Over the past year Innoviz has actively promoted the product as part of a broader strategy to expand beyond the automotive market, which is characterized by long sales cycles and heavy dependence on major car manufacturers.

The company views InnovizSMART as a strategic growth engine with commercial potential across infrastructure protection, smart-city and intelligent-transportation markets. Unlike automotive deals, security projects typically move faster and generate revenue on shorter timelines.

As a result, the current operational deployment in Israel carries significance beyond the technological aspect: it serves as the product’s first commercial proof-point and signals a shift from announcements and pilot programs to real installations at paying customers.

The global infrastructure-security market is considered one of the faster-growing segments in the defense industry, with rising demand for advanced sensing capable of reliable detection in complex environments. Innoviz’s LiDAR technology competes with traditional solutions such as cameras and radar, offering higher accuracy and fewer false alerts.

Barak Light Guard security system unveiled

As part of its broader expansion into the defense and smart-perimeter-security markets, Innoviz also hosted an unveiling event this week at its headquarters for the Barak Light Guard system, developed jointly with Drive Group and Cogniteam. Senior representatives from the defense establishment and national infrastructure companies attended to evaluate the solution, which combines Innoviz LiDAR sensors with advanced AI algorithms.

Launched roughly six months ago, the system is designed to protect borders, communities and critical infrastructure, providing real-time alerts on intrusions and suspicious movement. It integrates Innoviz LiDAR with Cogniteam AI to identify and classify stationary and moving objects at distances of up to approximately 400 meters, even under poor visibility and harsh weather conditions.

NVIDIA’s Driving Model Poses a Challenge to Mobileye

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By Yohai Schweiger

While NVIDIA’s Rubin platform for next-generation AI infrastructure captured most of the attention at CES 2026 in Las Vegas last week, the company quietly unveiled another move with potentially far-reaching strategic implications for the automotive industry: the launch of Alpamayo, an open foundation model for autonomous driving designed to serve as the planning and decision-making layer in future driving systems.

The announcement is expected to influence not only how autonomous driving systems are developed, but also the balance of power among technology suppliers in the automotive value chain — with particular implications for Israeli auto-tech companies.

Most Israeli players, including sensor makers Innoviz and Arbe, as well as simulation and validation specialists Cognata and Foretellix, do not provide full vehicle systems but rather core components within the broader stack. For them, NVIDIA’s move could prove supportive. By contrast, the availability of an open and flexible planning model that allows automakers to assemble software-hardware stacks around a unified computing platform poses a strategic challenge to Mobileye, which has built its market position around a vertically integrated, end-to-end solution and full system responsibility.

NVIDIA DRIVE: An AI-First Ecosystem for Automotive

Alpamayo now joins the broader set of solutions NVIDIA groups under its NVIDIA DRIVE platform — a comprehensive ecosystem for developing intelligent vehicle systems. DRIVE includes dedicated automotive processors such as Orin and Thor, an automotive operating system, sensor data processing and fusion tools, simulation platforms based on Omniverse and DRIVE Sim, and cloud infrastructure for training and managing AI models. In other words, it is a full-stack platform designed to support automakers from development and validation through real-time deployment on the vehicle itself.

This aligns with NVIDIA’s broader push toward an AI-first vehicle stack — shifting away from systems built primarily around hand-crafted rules and task-specific algorithms toward architectures where large AI models become central components, even in layers traditionally handled by “classical” algorithms, such as decision-making.

In this context, Alpamayo plays a strategic role. For the first time, NVIDIA is offering its own foundation model for planning and decision-making, effectively re-centering the DRIVE platform around an end-to-end AI-driven architecture — from cloud training to execution on the in-vehicle computer.

The Vehicle’s Tactical Brain

Alpamayo is a large multimodal Vision-Language-Action (VLA) model that ingests data from multiple video cameras, LiDAR and radar sensors, as well as vehicle state information, and converts it into an internal representation that enables reasoning and action planning. Based on this, the model generates a future driving trajectory several seconds ahead. It does not directly control actuators such as steering or braking, but it determines the vehicle’s tactical behavior.

Unlike general-purpose language models, Alpamayo operates in a physical environment and combines perception with spatial and contextual reasoning. Its inputs include video sequences, motion data, and in some cases maps and navigation goals. The model performs scene understanding, risk assessment, and path planning as part of a single decision chain. Its primary output is a continuous trajectory passed to the vehicle’s classical control layer, which handles physical actuation and safety constraints.

Training such a model relies on a combination of real-world data and massive amounts of synthetic data generated using NVIDIA’s simulation platforms, Omniverse and DRIVE Sim.

The model is released as open source, including weights and training code, allowing automakers and Tier-1 suppliers to retrain it on their own data, adapt it to their system architectures, and integrate it into existing stacks — not as a closed product, but as a foundation for internal development. NVIDIA has also announced partnerships with industry players including Lucid Motors, Jaguar Land Rover (JLR), Uber, and research collaborations such as Berkeley DeepDrive to explore advanced autonomous driving technologies using Alpamayo.

Mobileye: A Challenge to the Full-Stack Model

An autonomous driving stack typically consists of several layers: sensors, perception, planning and decision-making, and control. Alpamayo sits squarely in the planning layer. It does not replace perception, nor does it replace safety-critical control systems — but it does replace, or at least challenge, the traditional algorithmic decision-making layer.

This enables a more modular system design: perception from one supplier, planning from NVIDIA’s model, and control from another Tier-1. This represents a conceptual shift away from closed, end-to-end “black box” solutions.

That is where the tension with Mobileye emerges. For years, Mobileye has offered a nearly complete stack — sensors, perception, mapping, planning, and proprietary EyeQ chips running the entire system with high energy efficiency. This model fits well with ADAS and L2+ systems, and even more advanced autonomous configurations.

However, foundation models for planning shift the balance. They require more flexible and powerful compute than dedicated ADAS chips typically provide, pushing architectures toward GPU-based computing.

While in some scenarios Mobileye perception components can be integrated into broader stacks, most of the company’s advanced autonomy solutions are offered as tightly integrated system units, which in practice limits the ability to swap out individual layers. Moreover, the very presence of an open planning model weakens the value proposition of proprietary planning software. Instead of developing or licensing dedicated planning algorithms, automakers can adapt an existing foundation model to their own data and operational requirements.

This is not an immediate threat to Mobileye’s core business, but over the longer term — as the market moves toward L3 and L4 autonomy and the decision layer becomes increasingly AI-driven — it represents a genuine strategic challenge to the closed, end-to-end model.

That said, Mobileye retains a significant structural advantage: it delivers a complete system and assumes full responsibility for safety and regulatory compliance. For many automakers, especially those without deep in-house AI and software capabilities, this is critical. They prefer a single supplier accountable for system performance rather than assembling and maintaining a complex “puzzle” of components from multiple vendors, with fragmented liability and higher regulatory risk.

Innoviz and Arbe: Sensors Gain Strategic Importance

For Israeli sensor suppliers such as Innoviz and Arbe, NVIDIA’s move could be distinctly positive. Advanced planning models benefit from rich, reliable, multi-sensor input. LiDAR provides precise three-dimensional geometry and depth, while advanced radar excels at detecting objects in poor lighting and adverse weather conditions.

This sensor data is essential for planning layers and decision-making models operating in dynamic physical environments. As a result, both companies are positioning themselves as part of NVIDIA’s ecosystem rather than alternatives to it. Both have demonstrated integration of their sensing and perception pipelines with NVIDIA’s DRIVE AGX Orin computing platform.

In a stack where decision-making becomes more computationally intensive and AI-driven, the value of high-quality sensing only increases. No matter how advanced the model, limited input inevitably leads to limited decisions.

Cognata and Foretellix: Who Verifies AI Safety?

Another layer gaining importance is simulation, verification and validation — where Israeli firms Cognata and Foretellix operate.

Cognata focuses on building synthetic worlds and complex driving scenarios for training and testing, while Foretellix provides verification and validation tools that measure scenario coverage, detect behavioral gaps, and generate quantitative safety metrics for regulators and safety engineers.

As AI models become central to driving stacks, the need for scenario-based safety validation grows, beyond simply accumulating road miles.

Both companies are aligned with NVIDIA’s simulation-centric development approach. Cognata integrates with DRIVE simulation and Hardware-in-the-Loop environments (where real vehicle hardware is connected to virtual scenarios) for large-scale testing, while Foretellix connects its validation tools to Omniverse and DRIVE to assess AI-based driving systems under diverse physical conditions.

Open Source, Semi-Closed Platform

Although Alpamayo is released as open source, it is deeply optimized for NVIDIA’s hardware platforms. Optimization for CUDA, TensorRT, and low-precision compute enables real-time execution on DRIVE computers, which are architecturally closer to GPUs than to traditional ADAS chips.

This fits into NVIDIA’s broader open-model strategy: the company releases open models for robotics, climate science, healthcare and automotive — but after deep optimization for its own computing platforms. The approach enables broad ecosystem adoption while preserving a performance advantage for those building on NVIDIA hardware.

In practice, this allows NVIDIA to expand AI into physical industries while shaping the computing infrastructure those industries will rely on.

A Threat to One Model, an Opportunity for Others

NVIDIA’s driving model does not herald an immediate transformation on public roads, but it does signal a deeper shift in how the automotive industry approaches autonomy: fewer hand-crafted rules, more general AI models, more in-vehicle compute, and heavier reliance on simulation and validation.

For much of the Israeli auto-tech sector — sensor providers, simulation vendors and validation specialists — this trajectory aligns well with existing products and strategies, and could accelerate adoption and partnerships within the DRIVE ecosystem. For Mobileye, by contrast, it signals the emergence of an alternative path to building the “driving brain” — one that does not necessarily rely on a closed, vertically integrated stack.

If autonomous driving once appeared destined to be dominated by a small number of players controlling entire systems, NVIDIA’s move points toward a more modular future — with different layers supplied by different vendors around a central AI platform. At least in the Israeli auto-tech landscape, many players appear well positioned for that scenario.

Innoviz Unveils InnovizThree, Expanding Its Vision Beyond Autonomous Vehicles

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Innoviz has officially unveiled InnovizThree, the next generation in its LiDAR sensor portfolio, marking a significant step forward both technologically and strategically. The new sensor, which will be demonstrated publicly for the first time at the upcoming CES exhibition, was designed from the ground up to be integrated inside the vehicle cabin, behind the windshield—a location long considered particularly challenging for LiDAR systems but increasingly favored by automakers.

InnovizThree is based on 905-nanometer Time of Flight technology and delivers a roughly 60% reduction in volume compared with its predecessor, alongside improved performance and lower power consumption. The sensor comes in an exceptionally compact form factor, measuring about 34 millimeters in height and weighing roughly 600 grams, enabling seamless behind-the-windshield integration. According to the company, it offers a detection range of up to 300 meters, high angular resolution of around 0.05 degrees, a wide horizontal field of view of up to 120 degrees, and a scanning rate of up to 10.6 million points per second. It supports operating modes of 10 or 20 frames per second and enables configurable regions of interest, allowing higher point density and resolution to be concentrated in critical areas of the field of view. The system is also designed to deliver a continuous point cloud without gaps within these regions and to detect multiple returns from a single laser pulse, improving performance in rain, fog, glass interference, and complex lighting conditions.

The emphasis on behind-the-windshield installation goes beyond aesthetics. In addition to improving vehicle design, internal placement protects the sensor from physical damage, dirt, and harsh weather, enables the use of existing heating and cleaning systems, and simplifies installation and maintenance. At the same time, it presents a significant optical challenge due to glass distortion and signal attenuation—challenges Innoviz says it has addressed through a combination of optical design, algorithms, and thermal management.

Alongside its core focus on autonomous driving, Innoviz is clearly signaling an expansion into new markets. InnovizThree is positioned as a general-purpose, high-precision 3D sensing platform suitable not only for vehicles but also for humanoid robots, drones, and physical AI systems—domains that demand compact, lightweight, low-power sensors with industrial-grade reliability.

Although this marks the product’s official launch, Innoviz CEO Omer Keilaf revealed InnovizThree several weeks ago during the company’s latest earnings call, as previously reported by TechTime. At the time, Keilaf emphasized that the product was born directly out of discussions with automakers, noting that placing LiDAR behind the windshield requires a smaller, more efficient sensor with sufficient performance headroom to compensate for optical signal degradation.

During the call, Keilaf also addressed competing sensing technologies such as FMCW and OPA, arguing that they are not yet mature enough for mass production. According to him, Innoviz’s choice of 905-nanometer Time of Flight enables the use of proven, widely available components, making the technology suitable for large-scale manufacturing and compliance with automotive standards.

The launch comes amid strong business momentum. Innoviz reported a 238% year-over-year increase in quarterly revenue to $15.3 million, a sharp rise in unit shipments, and continued progress in pilot programs and validation processes with major automakers for Level 3 and Level 4 systems, ahead of a planned start of serial production in 2027.