  Autonomous driving has now made great strides thanks to artificial
intelligence, and numerous advanced methods have been proposed for vehicle end
target detection, including single sensor or multi sensor detection methods.
However, the complexity and diversity of real traffic situations necessitate an
examination of how to use these methods in real road conditions. In this paper,
we propose RMMDet, a road-side multitype and multigroup sensor detection system
for autonomous driving. We use a ROS-based virtual environment to simulate
real-world conditions, in particular the physical and functional construction
of the sensors. Then we implement muti-type sensor detection and multi-group
sensors fusion in this environment, including camera-radar and camera-lidar
detection based on result-level fusion. We produce local datasets and real sand
table field, and conduct various experiments. Furthermore, we link a
multi-agent collaborative scheduling system to the fusion detection system.
Hence, the whole roadside detection system is formed by roadside perception,
fusion detection, and scheduling planning. Through the experiments, it can be
seen that RMMDet system we built plays an important role in vehicle-road
collaboration and its optimization. The code and supplementary materials can be
found at: https://github.com/OrangeSodahub/RMMDet
