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Learn more about IEEE - Join the world's largest professional organization devoted to engineering and applied sciences and get access to this e-book plus all of IEEE Spectrum's articles, archives, PDF downloads, and other benefits. Learn more about IEEE - CREATE AN ACCOUNTSIGN IN JOIN IEEESIGN IN Close Access Thousands of Articles -- Completely Free Create an account and get exclusive content and features: Save articles, download collections, and post comments -- all free! For full access and benefits, subscribe to Spectrum. CREATE AN ACCOUNTSIGN IN TransportationNews Sub-$200 Lidar Could Reshuffle Auto Sensor Economics MicroVision says its sensor could one day break the $100 barrier Willie D. Jones 14 Feb 2026 5 min read Willie Jones covers transportation for IEEE Spectrum and the history of technology for The Institute. Close-up of MicroVision's Movia S lidar unit. MicroVision hopes to produce a lidar unit costing under US $200--half of typical prices today. MicroVision MicroVision, a solid-state sensor technology company located in Redmond, Wash., says it has designed a solid-state automotive lidar sensor intended to reach production pricing below US $200. That's less than half of typical prices now, and it's not even the full extent of the company's ambition. The company says its longer-term goal is $100 per unit. MicroVision's claim, which, if realized, would place lidar within reach of advanced driver-assistance systems (ADAS) rather than limiting it to high-end autonomous vehicle programs. Lidar's limited market penetration comes down to one issue: cost. Comparable mechanical lidars from multiple suppliers now sell in the $10,000 to $20,000 range. That price roughly tenfold drop, from about $80,000, helps explain why suppliers now are now hopeful that another steep price reduction is on the horizon. For solid-state devices, "it is feasible to bring the cost down even more when manufacturing at high volume," says Hayder Radha, a professor of electrical and computer engineering at Michigan State University and director of the school's Connected & Autonomous Networked Vehicles for Active Safety program. With demand expanding beyond fully autonomous vehicles into driver-assistance applications, "one order or even two orders of magnitude reduction in cost are feasible." "We are focused on delivering automotive-grade lidar that can actually be deployed at scale," says MicroVision CEO Glen DeVos. "That means designing for cost, manufacturability, and integration from the start--not treating price as an afterthought." MicroVision's Lidar System Tesla CEO Elon Musk famously dismissed lidar in 2019 as "a fool's errand," arguing that cameras and radar alone were sufficient for automated driving. A credible path to sub-$200 pricing would fundamentally alter the calculus of autonomous-car design by lowering the cost of adding precise three-dimensional sensing to mainstream vehicles. The shift reflects a broader industry trend toward solid-state lidar designs optimized for low-cost, high-volume manufacturing rather than maximum range or resolution. Before those economics can be evaluated, however, it's important to understand what MicroVision is proposing to build. The company's Movia S is a solid-state lidar. Mounted at the corners of a vehicle, the sensor sends out 905-nanometer-wavelength laser pulses and measures how long it takes for light reflected from the surfaces of nearby objects to return. The arrangement of the beam emitters and receivers provides a fixed field of view designed for 180-degree horizontal coverage rather than full 360-degree scanning typical of traditional mechanical units. The company says the unit can detect objects at distances of up to roughly 200 meters under favorable weather conditions--compared with the roughly 300-meter radius scanned by mechanical systems--and supports frame rates suitable for real-time perception in driver-assistance systems. Earlier mechanical lidars, used spinning components to steer their beams but the Movia S is a phased-arraysystem. It controls the amplitude and phase of the signals across an array of antenna elements to steer the beam. The unit is designed to meet automotive requirements for vibration tolerance, temperature range, and environmental sealing. MicroVision's pricing targets might sound aggressive, but they are not without precedent. The lidar industry has already experienced one major cost reset over the past decade. "Automakers are not buying a single sensor in isolation... They are designing a perception system, and cost only matters if the system as a whole is viable." -Glen DeVos, MicroVision Around 2016 and 2017, mechanical lidar systems used in early autonomous driving research often sold for close to $100,000. Those units relied on spinning assemblies to sweep laser beams across a full 360 degrees, which made them expensive to build and difficult to ruggedize for consumer vehicles. "Back then, a 64-beam Velodyne lidar cost around $80,000," says Radha. Comparable mechanical lidars from multiple suppliers now sell in the $10,000 to $20,000 range. That roughly tenfold drop helps explain why suppliers now believe another steep price reduction is possible. "For solid-state devices, it is feasible to bring the cost down even more when manufacturing at high volume," Radha says. With demand expanding beyond fully autonomous vehicles into driver-assistance applications, "one order or even two orders of magnitude reduction in cost are feasible." Solid-State Lidar Design Challenges Lower cost, however, does not come for free. The same design choices that enable solid-state lidar to scale also introduce new constraints. "Unlike mechanical lidars, which provide full 360-degree coverage, solid-state lidars tend to have a much smaller field of view," Radha says. Many cover 180 degrees or less. That limitation shifts the burden from the sensor to the system. Automakers will need to deploy three or four solid-state lidars around a vehicle to achieve full coverage. Even so, Radha notes, the total cost can still undercut that of a single mechanical unit. What changes is integration. Multiple sensors must be aligned, calibrated, and synchronized so their data can be fused accurately. The engineering is manageable, but it adds complexity that price targets alone do not capture. DeVos says MicroVision's design choices reflect that reality. "Automakers are not buying a single sensor in isolation," he says. "They are designing a perception system, and cost only matters if the system as a whole is viable." Those system-level tradeoffs help explain where low-cost lidar is most likely to appear first. Most advanced driver assistance systems today rely on cameras and radar, which are significantly cheaper than lidar. Cameras provide dense visual information, while radar offers reliable range and velocity data, particularly in poor weather. Radha estimates that lidar remains roughly an order of magnitude more expensive than automotive radar. But at prices in the $100 to $200 range, that gap narrows enough to change design decisions. "At that point, lidar becomes appealing because of its superior capability in precise 3D detection and tracking," Radha says. Rather than replacing existing sensors, lower-cost lidar would likely augment them, adding redundancy and improving performance in complex environments that are challenging for electronic perception systems. That incremental improvement aligns more closely with how ADAS features are deployed today than with the leap to full vehicle autonomy. MicroVision is not alone in pursuing solid-state lidar, and several suppliers including Chinese firms Hesai and RoboSense and other major suppliers such as Luminar and Velodyne have announced long-term cost targets below $500. What distinguishes current claims is the explicit focus on sub-$200 pricing tied to production volume rather than future prototypes or limited pilot runs. Some competitors continue to prioritize long-range performance for autonomous vehicles, which pushes cost upward. Others have avoided aggressive pricing claims until they secure firm production commitments from automakers. That caution reflects a structural challenge: Reaching consumer-level pricing requires large, predictable demand. Without it, few suppliers can justify the manufacturing investments needed to achieve true economies of scale. Evaluating Lidar Performance Metrics Even if low-cost lidar becomes manufacturable, another question remains: How should its performance be judged? From a systems-engineering perspective, Radha says cost milestones often overshadow safety metrics. "The key objective of ADAS and autonomous systems is improving safety," he says. Yet there is no universally adopted metric that directly expresses safety gains from a given sensor configuration. Researchers instead rely on perception benchmarks such as mean Average Precision, or mAP, which measures how accurately a system detects and tracks objects in its environment. Including such metrics alongside cost targets, says Radha, would clarify what performance is preserved or sacrificed as prices fall. IEEE Spectrum has covered lidar extensively, often focusing on technical advances in scanning, range, and resolution. What distinguishes the current moment is the renewed focus on economics rather than raw capability If solid-state lidar can reliably reach sub-$200 pricing, it will not invalidate Elon Musk's skepticism--but it will weaken one of its strongest foundations. When cost stops being the dominant objection, automakers will have to decide whether leaving lidar out is a technical judgment or a strategic one. That decision, more than any single price claim, may determine whether lidar finally becomes a routine component of vehicle safety systems. From Your Site Articles * MIT Spinoff Building New Solid-State Lidar-on-a-Chip System > * MIT and DARPA Pack Lidar Sensor Onto Single Chip > * Cheap Lidar: The Key to Making Self-Driving Cars Affordable > Related Articles Around the Web * MicroVision: Advanced lidar & perception solutions in autonomy ... > adassolid-state-lidarteslalidarself-driving-cars Willie D. Jones Willie Jones is an associate editor at IEEE Spectrum. In addition to editing and planning daily coverage, he manages several of Spectrum's newsletters and contributes regularly to the monthly Big Picture section that appears in the print edition. The Conversation (4) James Sanders James Sanders16 Feb, 2026 SM This misleading article contains numerous factual errors regarding automotive lidar. Here are the most glaring: There are multiple manufacturers, including Hesai, that use mechanical means for at least one scan axis and are already sold for a fraction of the "$10k - $20k" price noted by the author. Luminar itself built this class of scanners before going bankrupt. Per Microvision's own website, the Movia-S does not use a phased array and also does not have a range anywhere near 200m. Velodyne and Luminar do not even exist as companies anymore. Both have gone bankrupt and been acquired by competitors. 1 Reply Hide replies Show More Replies Mark Bowers Mark Bowers19 Feb, 2026 LM Tesla vision only is a quite remarkable solution to self driving. It really does work well in reasonable weather. Earlier HW3 computer designs, radar and ultrasonic sensors were included in production releases. Radar was dropped because of false reading (especially turning corners). My opinion is that they should have used a phased array radar. Ultrasonic sensors were dropped for self parking, later being replace by vision only, to no detriment. LIDAR might be a good supplement to vision in the forward direction (turning trajectory). This reader response was made by a Tesla owner with FSD. 0 Replies Hide replies Show More Replies W George Mckee W George Mckee19 Feb, 2026 LM Musk's claims about the effectiveness of driving without lidar were always problematic. While it's true that humans don't emit light from our eyes, we rely on angular convergence, focus accommodation, and stereoscopic parallax and motion parallax to estimate ranges up to about 200 meters. But Tesla cars can't move their cameras to create motion parallax, and their cameras are fixed-focus, and it's not even clear that they exploit stereopsis in their vision processing. Maybe stereopsis is hidden in their neural net weights, but it's hard to tell. 0 Replies Hide replies Show More Replies Low-Cost Computers Nearly Double in Price as RAM Shortage Hits Consumer ElectronicsSemiconductorsNews Rising Memory Price Hits Low-Cost Computer Makers 8h 4 min read Abstract image with colorful bar graphs, numbers, and lines on a pastel background. ComputingAIMagazineInterview AI's Math Tricks Don't Work for Scientific Computing 9h 3 min read Conceptual collage of an iPhone featuring a carnival-esuqe "fool the guesser" sign. 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