Chinese Tier 1 Supplier Produces Radars Based On Israel’s Arbe
Arbe Robotics, the Israeli company providing next-generation 4D Imaging Radar Solutions, announced last month that Chinese ADAS Tier 1 supplier HiRain Technologies has said it will take on major OEM (original equipment manager) and autonomous driving projects with the Imaging Radar system it developed using Arbe’s Perception Radar Chipset.
HiRain expects to reach full mass production of the system by 2023, Arbe said in the announcement.
HiRain leverages years of research and delivery experience in ADAS and autonomous vehicles to develop its 4D imaging radar systems based on Arbe’s chipset and technology. Advanced radar systems are one of the most crucial sensors in the perception suite for ADAS and autonomous driving, due to their adaptability to all weather and environmental conditions, as well as their accurate detection of distance and speed.
HiRain’s radar system utilizes Arbe’s chipset with 48 transmitting channels and 48 receiving channels, expanding the MIMO array to greatly improve the radar’s ability to acquire environment information.
“China is the largest vehicle market in the world, and is leading the way toward an autonomous vehicle future. HiRain is one of the most experienced Tier 1s in the Chinese automotive industry, and we are honored to partner with them,” says Kobi Marenko, Chief Executive Officer of Arbe. “HiRain’s work with key OEMs and transition into mass production is a great step toward achieving true safety on the roads, driving vehicle autonomy, and providing a solution for the tremendous demand in the region.”
HiRain’s radar detection distance reaches 350 meters, and achieves a physical resolution of 1° in azimuth and 1.5° in elevation – a resolution higher than that of traditional millimeter-wave radar by an order of magnitude. HiRain stated that they provide a dense point cloud, comparable to the imaging of LiDAR, that can meet the needs of L3
and higher autonomy.
HiRain Radar, based on the Arbe chipset, can accurately detect the boundaries of obstacles in the lane, trucks under a bridge, fences, and pedestrians. The rich point cloud data serves as the information source of the back-end AI algorithm for sensor fusion.