Recently, as many studies of autonomous vehicles have been achieved for
levels 4 and 5, there has been also increasing interest in the advancement of
perception, decision, and control technologies, which are the three major
aspects of autonomous vehicles. As for the perception technologies achieving
reliable maneuvering of autonomous vehicles, object detection by using diverse
sensors (e.g., LiDAR, radar, and camera) should be prioritized. These sensors
require to detect objects accurately and quickly in diverse weather conditions,
but they tend to have challenges to consistently detect objects in bad weather
conditions with rain, snow, or fog. Thus, in this study, based on the
experimentally obtained raindrop data from precipitation conditions, we
constructed a novel dataset that could test diverse network model in various
precipitation conditions through the CARLA simulator. Consequently, based on
our novel dataset, YOLO series, a one-stage-detector, was used to
quantitatively verify how much object detection performance could be decreased
under various precipitation conditions from normal to extreme heavy rain
situations.

近年来，自动驾驶汽车的研究主要集中在级别 4 和 5，对感知、决策和控制技术的发展产生了越来越大的兴趣。在感知技术方面，使用多种传感器（例如 LiDAR、雷达和相机）进行准确和快速的物体检测是首要任务。然而，这些传感器在恶劣天气条件下（如雨、雪或雾）往往难以持续准确地检测物体。因此，本研究基于实验获取的降水数据，通过 CARLA 模拟器构建了一个新的数据集，能够在各种降水条件下测试不同的网络模型。最终，利用我们的新数据集，使用一阶段检测器 YOLO 系列对不同降水条件下，从正常到极度大雨情况下的物体检测性能进行了定量验证。

极端暴雨中 YOLO 系列目标检测的挑战：基于 CALRA 模拟器的综合评估数据集

Challenges of YOLO Series for Object Detection in Extremely Heavy Rain:  CALRA Simulator based Synthetic Evaluation Dat a set

Perception technologies in Autonomous Driving are experiencing their golden
age due to the advances in Deep Learning. Yet, most of these systems rely on
the semantically rich information of RGB images. Deep Learning solutions
applied to the data of other sensors typically mounted on autonomous cars (e.g.
lidars or radars) are not explored much. In this paper we propose a novel
solution to understand the dynamics of moving vehicles of the scene from only
lidar information. The main challenge of this problem stems from the fact that
we need to disambiguate the proprio-motion of the 'observer' vehicle from that
of the external 'observed' vehicles. For this purpose, we devise a CNN
architecture which at testing time is fed with pairs of consecutive lidar
scans. However, in order to properly learn the parameters of this network,
during training we introduce a series of so-called pretext tasks which also
leverage on image data. These tasks include semantic information about
vehicleness and a novel lidar-flow feature which combines standard image-based
optical flow with lidar scans. We obtain very promising results and show that
including distilled image information only during training, allows improving
the inference results of the network at test time, even when image data is no
longer used.

本文提出了一种用于理解场景中运动车辆动态的新方法，该方法通过设计卷积神经网络 (CNN)，并在训练期间引入一系列所谓的 “预文本任务”，以消除自我运动和外部车辆运动的混淆。我们在测试阶段使用 lidar 扫描的连续对来试验该系统，并在训练期间将形象信息开以提高系统推理结果精度。