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Four Trends Driving the Future of Radar Perception
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Radar is evolving fast. From sensing modules to intelligent platforms, the technology behind automotive radar is undergoing a fundamental shift. Here are four trends—supported by recent Yole Group insights—that highlight where the industry is headed.
According to Yole Group’s Status of the Radar Industry 2024 report, “the standard 77GHz radar's price could plummet to as low as $30 by 2030.” This cost reduction makes it possible to deploy radar more broadly across vehicles—but traditional radar still suffers from limited angular resolution.
One common path to improving radar resolution has been the development of imaging radars, such as Continental’s ARS 640 or the massive-MIMO systems from Arbe and Mobileye. While these systems offer significant resolution gains, they do so at a much higher cost— requiring specialized hardware and complex processing.
Zendar takes a different approach to enhancing radar performance. Instead of building larger, more expensive sensors , Zendar’s system enhances the performance of standard, off-the-shelf automotive radars—the same models that have been in production for years and benefit from global economies of scale. By combining multiple spatially separated sensors into a unified system, Zendar’s Distributed Aperture Radar (DAR) achieves 10x greater angular resolution than conventional systems—delivering high-performance sensing that scales.
Resolution Comparison: DAR vs. 4-Chip Radar vs. MRR
Traditional radar systems rely on Doppler-based methods, which primarily track objects in motion by measuring velocity and range. These systems are typically unable to identify hazards when they are stationary and lack semantic context, which is useful for predicting how objects might move and where they will be in the future.
Yole Group notes that “automotive radar sensors are starting a paradigm shift” from basic motion tracking to “generating perceptual maps” of the full driving environment.
Zendar’s Semantic Spectrum technology exemplifies this new paradigm. It classifies people, vehicles, and objects whether they’re moving or not. This enables full 360º spatial intelligence that supports smarter decision-making and safer autonomy.
As vehicles become more software-defined, OEMs are rethinking radar architecture. According to Yole Group, there is a “significant transition toward vehicle centralization, anticipated to be fully implemented between 2030 and 2035”. Instead of relying on multiple distributed modules with their own processors, centralized radar computing aggregates data from multiple radar units into a unified processing unit.
Zendar’s technologies align with this shift, making the most of centralized architecture. With centralized data, multiple radar units may be operated as one coherent system, enhancing resolution to rival lidar. Additionally, AI perception models benefit from the greater data availability, leveraging full spectrum data from multiple sensors.
Yole Group notes that the industry is moving beyond ADAS and into Level 3 autonomous driving: “Mercedes isn’t the only automaker pursuing the technology. Ford has said it would turn to internally developed L2+/L3 technology.” Stellantis has unveiled their own L3 automated driving technology and “Audi, BMW, and Volvo have all said they are working on their own Level 3 systems”.
Delivering safe and scalable L3 functionality means solving for long-range, high-speed operation in all weather—and doing it with a sensor stack that can scale across vehicle lines.
Zendar’s radar is engineered with these challenges in mind. By delivering high-resolution, long-range perception and full-scene semantic understanding—we’re helping automakers take the next step from assisted to autonomous driving.
Radar is no longer just about detecting moving objects in front of the vehicle—it’s about understanding the 360º driving environment with precision, confidence, and scalability.
With technologies like Distributed Aperture Radar and Semantic Spectrum, Zendar is helping OEMs move beyond traditional radar limitations and build toward reliable, long-range, high-resolution perception for Level 3 and beyond.