Autonomous vehicle platoons present near- and long-term opportunities to enhance operational efficiencies and save lives. The past 30 years have seen rapid development in the autonomous driving space, enabling new technologies that will alleviate the strain placed on human drivers and reduce vehicle emissions. This paper introduces a testbed for evaluating and benchmarking platooning algorithms on 1/10th scale vehicles with onboard sensors. To demonstrate the testbed's utility, we evaluate three algorithms, linear feedback and two variations of distributed model predictive control, and compare their results on a typical platooning scenario where the lead vehicle tracks a reference trajectory that changes speed multiple times. We validate our algorithms in simulation to analyze the performance as the platoon size increases, and find that the distributed model predictive control algorithms outperform linear feedback on hardware and in simulation.
Failure detection in automated image classification is a critical safeguard for clinical deployment. Detected failure cases can be referred to human assessment, ensuring patient safety in computer-aided clinical decision making. Despite its paramount importance, there is insufficient evidence about the ability of state-of-the-art confidence scoring methods to detect test-time failures of classification models in the context of medical imaging. This paper provides a reality check, establishing the performance of in-domain misclassification detection methods, benchmarking 9 widely used confidenc…
Cyclops, introduced in this paper, is an open research platform for everyone that wants to validate novel ideas and approaches in the area of self-driving heavy-duty vehicle platooning. The platform consists of multiple 1/14 scale semi-trailer trucks, a scale proving ground, and associated computing, communication and control modules that enable self-driving on the proving ground. A perception system for each vehicle is composed of a lidar-based object tracking system and a lane detection/control system. The former is to maintain the gap to the leading vehicle and the latter is to maintain the…
4DCodeBench: Benchmarking Agents on Inverse Graphics of Dynamic Scenes
Johns Hopkins University（约翰斯·霍普金斯大学）；Stanford University（斯坦福大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Massachusetts Institute of Technology（麻省理工学院）
University of Southern California（南加州大学）；J.P.Morgan AI Research（摩根大通人工智能研究院）
In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine learning tasks (e.g., forecasting). However, dealing with out-of-distribution (OOD) LOB data is challenging since distributional shifts are unlabeled in current publicly available LOB datasets. Therefore, it is critical to build a synthetic LOB dataset with labeled OOD samples serving as a testbed for developing models that generalize well to unseen s…
Glioma C6: A Novel Dataset for Training and Benchmarking Cell Segmentation
Pavlov Institute of Physiology, Russian academy of science（俄罗斯科学院帕夫洛夫生理研究所）；Saint-Petersburg State University of Aerospace Instrumentation（圣彼得堡航空航天仪器州大学）；Institute of Physiology, NAS of Belarus（白俄罗斯国家科学院生理研究所）
Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez（工程与科学学院，阿道弗·伊巴涅斯大学）；Dirección de Programas, Investigación y Desarrollo, Armada de Chile（智利海军计划、研究与开发 Directorate）；Millennium Nucleus for Social Data Science (SODAS)（社会科学数据科学千年核基金（SODAS））；Millennium Nucleus in Data Science for Plant Resilience (PhytoLearning)（植物韧性数据科学千年核基金（PhytoLearning））
BAGEL: Benchmarking Animal Knowledge Expertise in Language Models
Earth Species Project（地球物种项目）；NYU Center for Data Science（纽约大学数据科学中心；纽约大学上海）；NYU Shanghai（纽约大学上海数据科学中心；纽约大学-复旦大学数学科学研究所）；NYU Shanghai Center for Data Science；NYU-ECNU Institute of Mathematical Sciences at NYU Shanghai
Multisource human-in-the-loop digital twin testbed for connected and autonomous vehicles in mixed traffic flow
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动性学院）；Department of Civil and Environmental Engineering, University of Michigan（密歇根大学土木与环境工程系）
