We are motivated by the problem of autonomous vehicle performance validation. A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it could encounter, including rare events, to provide a strong case for safety and show there is no edge-case pathological behavior. Autonomous vehicle companies rely on potentially millions of miles driven in realistic simulation to expose the driving stack to enough miles to estimate rates and severity of collisions. To address scalability and coverage, we propose the use of a behavior foundation model, specifically a masked autoencoder (MAE), trained to reconstruct driving scenarios. We leverage the foundation model in two complementary ways: we (i) use the learned embedding space to group qualitatively similar scenarios together and (ii) fine-tune the model to label scenario difficulty based on the likelihood of a collision upon simulation. We use the difficulty scoring as importance weighting for the groups of scenarios. The result is an approach which can more rapidly estimate the rates and severity of collisions by prioritizing hard scenarios while ensuring exposure to every kind of driving scenario.
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology（信息与决策实验室，麻省理工学院）；Institute of Data, Systems, and Society, Massachusetts Institute of Technology（数据、系统与社会研究所，麻省理工学院）；Department of Mechanical Engineering, University of California, Berkeley（机械工程系，加州大学伯克利分校）
Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives
University of Toronto（多伦多大学）；NVIDIA Research（英伟达研究院）；Chinese University of Hong Kong（香港中文大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Riverside（加州大学河滨分校）；Stanford University（斯坦福大学）
Artificial Intelligence for Modeling and Simulation of Mixed Automated and Human Traffic
Department of Transport & Planning, Delft University of Technology（代尔夫特理工大学交通与规划系）；Department of Industrial and Systems Engineering, University of Michigan, Dearborn（密歇根大学迪尔伯恩分校工业与系统工程系）；Tandon School of Engineering, New York University（纽约大学Tandon工程学院）
The Autonomy-Alignment Problem in Open-Ended Learning Robots: Formalising the Purpose Framework
Institute of Cognitive Sciences and Technologies, National Research Council（认知科学与技术研究所，国家研究理事会）；Integrated Group for Engineering Research, CITIC, Universidade da Coruña（工程研究联合组，CITIC，科鲁纳大学）；Institute of Intelligent Systems and Robotics, Sorbonne University / CNRS（智能系统与机器人研究所，索邦大学 / CNRS）
Agricultural Industry Initiatives on Autonomy: How collaborative initiatives of VDMA and AEF can facilitate complexity in domain crossing harmonization needs
Kempten University of Applied Sciences（肯普滕应用技术大学）；Bernard Krone GmbH & Co. KG（伯纳德·克罗恩有限两合公司）；Osnabrück University of Applied Sciences（奥斯纳布吕克应用技术大学）
Multi-Agent Autonomy: Advancements and Challenges in Subterranean Exploration
University of Colorado Boulder（科罗拉多大学博尔德分校）；University of California Santa Cruz（加利福尼亚大学圣克鲁兹分校）；Aerospace Engineering Sciences（航空航天工程科学（注：此处为系/专业，按要求不提取，仅保留大学/学院级别机构））；Mechanical Engineering（机械工程（注：此处为系/专业，按要求不提取，仅保留大学/学院级别机构））；Computer Science（计算机科学（注：此处为系/专业，按要求不提取，仅保留大学/学院级别机构））；Electrical & Computer Engineering（电气与计算机工程（注：此处为系/专业，按要求不提取，仅保留大学/学院级别机构））
Fast and Modular Autonomy Software for Autonomous Racing Vehicles
Carnegie Mellon University（卡内基梅隆大学）；Cornell University（康奈尔大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Pittsburgh（匹兹堡大学）；Rochester Institute of Technology（罗切斯特理工学院）；University of Waterloo（滑铁卢大学）
Underwater Embodied Intelligence for Autonomous Robots: A Constraint-Coupled Perspective on Planning, Control, and Deployment
Department of Mechanical Engineering, The University of Hong Kong（香港大学机械工程系）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生学院）；Department of Data and Systems Engineering, The University of Hong Kong（香港大学数据与系统工程系）；Department of Data Science, New Jersey Institute of Technology（新泽西理工学院数据科学系）
A phase-aware AI car-following model for electric vehicles with adaptive cruise control: Development and validation using real-world data
Department of Civil Engineering（土木工程系）；Saint Louis University（圣路易斯大学）；Department of Civil, Environmental, and Architectural Engineering（土木、环境与建筑工程系）；University of Kansas（堪萨斯大学）；Department of Computer Science（计算机科学系）
