Thomas Griebel, Dominik Authaler, Markus Horn, Matti Henning, Michael Buchholz, Klaus Dietmayer
备注：
Accepted for presentation at the 2021 IEEE 24th International Conference on Intelligent Transportation Systems (ITSC), September 19-22, 2021, Indianapolis, USA
For autonomous driving, radar is an important sensor type. On the one hand, radar offers a direct measurement of the radial velocity of targets in the environment. On the other hand, in literature, radar sensors are known for their robustness against several kinds of adverse weather conditions. However, on the downside, radar is susceptible to ghost targets or clutter which can be caused by several different causes, e.g., reflective surfaces in the environment. Ghost targets, for instance, can result in erroneous object detections. To this end, it is desirable to identify anomalous targets as early as possible in radar data. In this work, we present an approach based on PointNets to detect anomalous radar targets. Modifying the PointNet-architecture driven by our task, we developed a novel grouping variant which contributes to a multi-form grouping module. Our method is evaluated on a real-world dataset in urban scenarios and shows promising results for the detection of anomalous radar targets.
Skeleton Detection Using Dual Radars with Integration of Dual-View CNN Models and mmPose
Graduated School of Computer and Information Sciences, Faculty of Computer and Information Sciences, Hosei University（法政大学计算机与信息科学研究生院、计算机与信息科学学院）；Hosei University（法政大学）
Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Research Institute of Mine Artificial Intelligence, Chinese Institute of Coal Science（中国煤炭科学研究院矿山人工智能研究院）
Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models
H. Milton Stewart School of Industrial and Systems Engineering（H. Milton Stewart工业与系统工程学院）；Georgia Institute of Technology（佐治亚理工学院）；Department of Mechanical Engineering（机械工程系）；Polytechnic University of Milan（米兰理工学院）
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving
University of Kassel（卡塞尔大学）；Technische Universität Braunschweig（布伦瑞克工业大学）；FZI Research Center for Information Technology（FZI信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
