As the automotive industry is heading toward greater autonomy and increasingly advanced driver-assistance systems, expectations placed on vehicle perception systems have never been higher. Yet, the traditional perspective on lidar sensors still dominates the markets: one-lidar-fits-all. MicroVision takes a different view, pursuing a Tri-Lidar Architecture – a new strategic approach that combines advanced performance, intelligent integration, and scalable cost-efficiency within a single, flexible platform.
Challenging the traditional one-size-fits-all sensor model
Instead of relying on a single lidar unit to serve all sensing roles, the Tri-Lidar Architecture combines several highly specialized lidar units into a coordinated architecture. Typically, two short-range units are positioned for near-field perception, while one long-range sensor covers detection at highway speeds.
By dedicating specific lidar modules to specific tasks, the Tri-Lidar Architecture eliminates the need to overengineer individual sensors and simplifies the requirements of the long-range sensor. This focus results in a system that delivers better performance, is highly cost effective, and consumes less power. In this configuration, the solid-state sensors can be offered at a price that aligns with OEM cost structure. Using MicroVision’s MOVIA™ S and MAVIN®, the entire architecture remains compact and inconspicuous, designed for seamless integration behind windshields, within grilles, or under hoods.
Open Platform Design: Reducing Complexity, Designed for Flexibility
Beyond the hardware, what truly sets the Tri-Lidar Architecture apart is its open platform design. It has the potential to reduce software and system complexity, and is designed to support integration with further lidar sensors as well as other sensor types such as radar and cameras.
For OEMs and Tier1s ready to move beyond the limitations of traditional lidar, the Tri-Lidar Architecture offers a clear path forward. The Tri-Lidar Architecture combines performance where it matters, adaptability where it's needed, and cost-efficiency where it counts. Its low power consumption also makes it an ideal choice for electric and hybrid platforms, where energy efficiency is critical.
For any team developing next-generation ADAS or autonomous solutions, now is the time to explore how this architecture can serve as a foundation for more intelligent and cost effective sensing.
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