Developing a Taxonomy of Elements Adversarial to Autonomous Vehicles
Michigan State University（密歇根州立大学）；Detroit Country Day High School（底特律乡村走读高中）
Mohammadali Saffary, Nishan Inampudi, Joshua E. Siegel
备注：
18 pages total, 4 pages of references, initial page left blank for IEEE submission statement. Includes 4 figures and 2 tables. Written using IEEEtran document class
As highly automated vehicles reach higher deployment rates, they find themselves in increasingly dangerous situations. Knowing that the consequence of a crash is significant for the health of occupants, bystanders, and properties, as well as to the viability of autonomy and adjacent businesses, we must search for more efficacious ways to comprehensively and reliably train autonomous vehicles to better navigate the complex scenarios with which they struggle. We therefore introduce a taxonomy of potentially adversarial elements that may contribute to poor performance or system failures as a means of identifying and elucidating lesser-seen risks. This taxonomy may be used to characterize failures of automation, as well as to support simulation and real-world training efforts by providing a more comprehensive classification system for events resulting in disengagement, collision, or other negative consequences. This taxonomy is created from and tested against real collision events to ensure comprehensive coverage with minimal class overlap and few omissions. It is intended to be used both for the identification of harm-contributing adversarial events and in the generation thereof (to create extreme edge- and corner-case scenarios) in training procedures.
A new Taxonomy for Automated Driving: Structuring Applications based on their Operational Design Domain, Level of Automation and Automation Readiness
Technical University Munich（慕尼黑工业大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；TU Darmstadt（达姆施塔特工业大学）；Institute of Automotive Engineering（汽车工程研究所）
Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Under Dynamic Lighting and Weather Conditions Using Electric Vehicles
Rice University（莱斯大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Willamette University（威拉姆特大学）；Louisiana State University（路易斯安那州立大学）；Michigan State University（密歇根州立大学）；Lawrence Technological University（劳伦斯理工大学）
Vehicle-in-Virtual-Environment (VVE) Method for Developing and Evaluating VRU Safety of Connected and Autonomous Driving with Focus on Bicyclist Safety
Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving
FZI Research Center for Information Technology（FZI信息技术研究中心）；University of Kassel（卡塞尔大学）；University of Wuppertal（伍珀塔尔大学）；German Aerospace Center (DLR)（德国航空航天中心（DLR））；Robert Bosch GmbH（罗伯特·博世有限公司）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software
Technical University of Munich（慕尼黑技术大学）；School of Engineering & Design（工程与设计学院）；Department of Mobility Systems Engineering（移动系统工程系）；Institute of Automotive Technology（汽车技术研究所）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与智能机械研究所）
ASVLite: a high-performance simulator for autonomous surface vehicles
University of Southampton（南安普顿大学）
与湖泊和河流等其他水生环境相比，海洋波浪的能量是海洋环境的关键区别因素。波浪显著地影响海洋车辆的动力学;因此，在开发自主水面车辆（ASV）的有效控制策略时，必须考虑车辆在波浪中的动力学。然而，大多数可用的开源海洋模拟器要么排除波浪中车辆的动力学，要么使用计算开销高的方法。本文介绍了ASVLite，计算效率的ASV模拟器，使用频域分析的波浪力计算。ASVLite适用于需要低计算开销和高运行时性能的应用程序。我们在Raspberry Pi 2和中档台式计算机上的测试表明，该模拟器具有很高的运行时性能，可以有效地模拟不规则波，其分量波数高达260，大规模群高达500 ASV。
ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research
IDLab, Faculty of Applied Engineering, University of Antwerp - imec（ID实验室，应用工程学院，安特卫普大学 - imec）；Cosys-Lab, Faculty of Applied Engineering, University of Antwerp（Cosys实验室，应用工程学院，安特卫普大学）；Flanders Make Strategic Research Centre（弗拉芒制造战略研究中心）
