military radar technology to be commercialised for autonomous driving

context: China is trying to improve the conversion rate of its R&D into commercial solutions. In sci-tech megaprojects and other R&D, the state funds private or semi-private enterprises, so projects have a greater chance of finding industrial applications. Public–private alliances are common in areas including new energy vehicles (NEV), IT, integrated circuits, and now the unmanned vehicle industry. 


The National Key Laboratory (NKL) for Microwave Imaging Technology and Beijing Autoroad Tech Ltd. announced a public–private joint laboratory on 10 May 2018, reports Caixin. The NKL is the only state-level research institute in microwave imaging and is affiliated with Chinese Academy of Sciences (CAS) Institute of Electronics. Beijing Autoroad Tech is a leading millimeter wave and sub-millimeter wave radar technology company.

The joint lab will commercialise 77GHz microwave radar applications and synthetic aperture radar (SAR) technologies for connected and automated vehicles (CAV), explains Zhao Jie 赵捷 Autoroad Tech CEO. Domestically developed radar cannot yet surpass conditional (L3) autonomous driving, as advanced (L4) and complete (L5) autopilot require higher precision, lower latency and more intricate coordination with other types of sensors, explains Science and Technology Daily. Microwave radar is one of the few CAV components China can produce itself, says Ding Chibiao 丁赤飚 CAS Institute of Electronics vice director, noting there is still a wide gap in lidar.

The joint lab introduces a new model of civil–military integration, explains Liang Xingdong 梁兴东 Microwave Imaging NKL vice director. China has distinctive competencies in radar technology, but such strengths have never extended beyond areas of military, defence, aerospace, aeronautics, remote sensing and mapping, says Liang. The NKL has improved radar resolution, imaging and identification capabilities, and this expertise will help make auto radar systems better, smaller, cheaper and more reliable, explains Liang.