
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.
complete driving information, intelligent driving decision, multiradar sensor adapting, dynamic obstacle tracking, traffic engineering computing, static obstacles detection, intelligent driving testing platforms, intelligent driving radar perception, sensors, road vehicles, dynamic obstacle detection, QA76.75-76.765, mobile robots, variable granularity road ownership radar, intelligent driving vehicle, collision avoidance, Computer software, intelligent driving environment perception, intelligent driving system, sensor fusion, intelligent transportation systems, information processing modules, object detection, driving brain, intelligent driving hardware testing platforms, important sensor, radar information fusion, Computational linguistics. Natural language processing, platform radar sensors, installing location, remotely operated vehicles, radar sensor model, P98-98.5, road safety
complete driving information, intelligent driving decision, multiradar sensor adapting, dynamic obstacle tracking, traffic engineering computing, static obstacles detection, intelligent driving testing platforms, intelligent driving radar perception, sensors, road vehicles, dynamic obstacle detection, QA76.75-76.765, mobile robots, variable granularity road ownership radar, intelligent driving vehicle, collision avoidance, Computer software, intelligent driving environment perception, intelligent driving system, sensor fusion, intelligent transportation systems, information processing modules, object detection, driving brain, intelligent driving hardware testing platforms, important sensor, radar information fusion, Computational linguistics. Natural language processing, platform radar sensors, installing location, remotely operated vehicles, radar sensor model, P98-98.5, road safety
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 10 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
