COMAP: A SYNTHETIC DATASET FOR COLLECTIVE MULTI-AGENT PERCEPTION OF AUTONOMOUS DRIVING
COMAP: A SYNTHETIC DATASET FOR COLLECTIVE MULTI-AGENT PERCEPTION OF AUTONOMOUS DRIVING
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COMAP：自动驾驶集体多智能体感知的综合数据集
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Monika Sester
中科院分区：
文献类型：
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作者：
Y. Yuan;Monika Sester
抽象。联网车辆的集体感知可以通过共享感知信息来充分提高自动驾驶的安全性和可靠性。然而，为这种情况收集真实的实验数据是极其昂贵的。因此，我们通过CARLA和SUMO模拟器构建了一个计算高效的协同模拟合成数据生成器。模拟数据包含图像和点云数据以及用于对象检测和语义分割任务的地面实况。为了验证集体感知相对于单一车辆感知的上级性能增益，我们在此数据集上进行了车辆检测实验，这是自动驾驶最重要的感知任务之一。训练了一个3D目标检测器和一个鸟瞰图（BEV）检测器，然后用不同配置的合作车辆数量和车辆通信范围进行测试。实验结果表明，集体感知不仅可以显着提高整体的平均检测精度，但也检测到的边界框的定位精度。此外，通过车辆检测对比实验表明，传感器观测噪声引起的检测性能下降可以通过多车辆采集的冗余信息来抵消。
Abstract. Collective perception of connected vehicles can sufficiently increase the safety and reliability of autonomous driving by sharing perception information. However, collecting real experimental data for such scenarios is extremely expensive. Therefore, we built a computational efficient co-simulation synthetic data generator through CARLA and SUMO simulators. The simulated data contain image and point cloud data as well as ground truth for object detection and semantic segmentation tasks. To verify the superior performance gain of collective perception over single-vehicle perception, we conducted experiments of vehicle detection, which is one of the most important perception tasks for autonomous driving, on this data set. A 3D object detector and a Bird’s Eye View (BEV) detector are trained and then test with different configurations of the number of cooperative vehicles and vehicle communication ranges. The experiment results showed that collective perception can not only dramatically increase the overall mean detection accuracy but also the localization accuracy of detected bounding boxes. Besides, a vehicle detection comparison experiment showed that the detection performance drop caused by sensor observation noise can be canceled out by redundant information collected by multiple vehicles.
DOI：
10.1109/percomworkshops48775.2020.9156225
发表时间：
2020-03
期刊：
2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
影响因子：
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作者：
Shi-hua Yang;Emily Bailey;Zhengye Yang;J. Ostrometzky;G. Zussman;I. Seskar;Z. Kostić
通讯作者：
Shi-hua Yang;Emily Bailey;Zhengye Yang;J. Ostrometzky;G. Zussman;I. Seskar;Z. Kostić