Technology of intelligent driving radar perception based on driving brain

• Main Authors: Hongbo Gao, Xinyu Zhang, Jianhui Zhao, Deyi Li
• Format: Article
• Language: English
• Published: Wiley 2019-03-01
• Series: CAAI Transactions on Intelligence Technology
• Subjects: remotely operated vehicles; sensors; collision avoidance; mobile robots; object detection; road safety; sensor fusion; traffic engineering computing; road vehicles; intelligent transportation systems; intelligent driving radar perception; driving brain; important sensor; intelligent driving environment perception; installing location; platform radar sensors; information processing modules; intelligent driving system; intelligent driving vehicle; variable granularity road ownership radar; radar information fusion; complete driving information; radar sensor model; intelligent driving decision; multiradar sensor adapting; static obstacles detection; dynamic obstacle detection; dynamic obstacle tracking; intelligent driving testing platforms; intelligent driving hardware testing platforms
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/trit.2017.0010

Radar is an important sensor to realise intelligent driving environment perception, enabling the detection of static obstacles and dynamic obstacles, and the tracking of a dynamic obstacle. The models, quantities, and installing location of the platform radar sensors as well as the information processing modules differ from each other on different intelligent driving testing platforms, resulting in different quantities and interfaces on the intelligent driving system. Here, the authors build the software architecture of intelligent driving vehicle based on driving brain which is used to adapt to different types of radar sensors and use the variable granularity road ownership radar for radar information fusion. Under the condition of complete driving information, increasing or reducing the number of radar sensors and changing the radar sensor model or installing location will not affect the intelligent driving decision directly. Therefore, the authors meet the demands of multi-radar sensor adapting to different intelligent driving hardware testing platforms.