RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving
This work addresses the problem of integrating sensor detection in complex real-world traffic for autonomous driving systems, but it appears incremental as it builds on existing sensor fusion methods.
The authors tackled the challenge of using diverse sensor methods in real traffic conditions for autonomous driving by proposing RMMDet, a road-side multitype and multigroup sensor detection system, which they tested in a simulated environment and found to play an important role in vehicle-road collaboration and optimization.
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