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
翻译:由于人工智能,自主驾驶现已取得长足进步,并提出了许多先进的车辆最终目标探测方法,包括单一传感器或多传感器探测方法,然而,由于实际交通情况复杂多样,需要审查如何在实际道路条件下使用这些方法;在本文件中,我们提议采用路边多式和多组自动驾驶探测系统RMDet,即路边多式多组和多组传感器探测系统;我们使用基于ROS的虚拟环境模拟现实世界状况,特别是传感器的物理和功能构造;然后,我们在这个环境中实施突变型传感器探测和多组传感器探测,包括基于结果级聚变的相机雷达和相机激光雷达探测;我们制作当地数据集和真正的沙子表字段,并进行各种实验;此外,我们把多剂合作安排系统与聚变探测系统连接起来;因此,整个路边探测系统是由路边感知、聚合探测和时间安排规划形成的;通过实验,我们建立了RMMDT系统,在车辆-公路合作及其优化方面发挥着重要作用。我们可以在http/RMB/MDA上找到代码和补充材料:https/RMDRB/RMD。</s>