HiRain Declares It Will Reach Full Mass Production of Perception Radars by 2023
Arbe Robotics Ltd., a global leader in Perception Radar Solutions, announced that HiRain Technologies, the leading Chinese ADAS Tier 1 supplier, has declared that it is undertaking major OEM 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 radar system by 2023.
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 crucial sensors in the ADAS and autonomous perception suite, due to their adaptability in 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, and a dedicated processor, which expands the MIMO array to greatly improve the radar’s ability to map the environment and sense the surroundings of the vehicle in high resolution.
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“China has the largest vehicle market in the world and is leading the way toward an autonomous vehicle future,” said Kobi Marenko, Chief Executive Officer of Arbe. “HiRian is one of the most experienced Tier 1s in the Chinese automotive industry, and we are honored they have chosen our chipset. HiRain’s transition into mass production and their progress with key OEMs is an important step toward achieving true safety on the roads, driving vehicle autonomy, and providing solutions for the tremendous demand in the region.”
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HiRain’s radar detection distance reaches 350m and achieves a physical resolution of 1° in azimuth and 1.5° in elevation – a resolution higher than that of traditional radars by an order of magnitude. HiRain stated that it provides a dense point cloud, comparable to the imaging of LiDAR, that can meet the needs of L3 and higher autonomy.
The HiRain radar, based on Arbe’s 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 AI algorithm for sensor fusion.
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