Metawave Showcases Long Range Automotive Radar Based on Infineon Platform

Metawave has demonstrated an advanced radar that can detect automobiles and their speed at distances of up to 300 meters, and pedestrians and bicycles as far as 180 meters. Integrated with Infineon’s 77 GHz radar chipset comprising of the RXS8160 MMIC and AURIX microcontroller, along with NVIDIA’s AI Processing Engine, Metawave’s development testing platform more than doubles the range of existing automotive sensors, which can only detect unidentifiable, often blurry objects at a much shorter range of around 100 meters.

According to Research and Markets, the automotive radar market is predicted to exceed $9,475.4 million by 2023. Advanced, smart radar plays a significant role in making autonomous driving safer, especially in challenging weather and operating conditions such as dense fog, heavy storms and dirty roads. Unlike cameras and LiDAR, radar can detect objects at a distance through difficult weather conditions. Today’s existing radar is limited in its ability to see high-resolution, making it impossible to determine and learn to classify through AI what is in front of the automobile, especially at 300 meters.

WARLORD, Metawave’s smart radar platform, uses one antenna and pushes the complexity to analog. With WARLORD, the antenna itself shapes and steers the beam, recognizes objects quickly in the analog space and leverages AI to learn as the radar sees.

Three sensors are fundamental components of the perception system for self-driving cars today: camera, LiDAR and radar. The camera is the highest resolution sensor but cannot see objects beyond 70 meters. LiDAR extends the range to about 180 meters with a fairly high resolution imaging capability. Radar operates at a lower frequency and sees long ranges sooner than any other sensor. Today's radar lacks resolution and cannot differentiate objects. These systems require multiple antennas, which are heavy and expensive, and need to analyze every signal in the digital space, which takes time.