RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving

TL;DR

RMMDet is a comprehensive roadside detection system for autonomous driving that integrates multi-type sensors, multi-group fusion, and multi-agent scheduling within a simulated environment, enhancing vehicle-road collaboration.

Contribution

The paper introduces RMMDet, a novel roadside detection system combining multi-type sensors, multi-group fusion, and multi-agent scheduling for improved autonomous driving perception.

Findings

Effective multi-sensor fusion improves detection accuracy.

Simulation results demonstrate system robustness in diverse conditions.

Multi-agent scheduling enhances real-time perception and decision-making.

Abstract

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