A mobility map, which provides maximum achievable speed on a given terrain, is essential for path planning of autonomous ground vehicles in off-road settings. While physics-based simulations play a central role in creating next-generation, high-fidelity mobility maps, they are cumbersome and expensive. For instance, a typical simulation can take weeks to run on a supercomputer and each map requires thousands of such simulations. Recent work at the U.S. Army CCDC Ground Vehicle Systems Center has shown that trained machine learning classifiers can greatly improve the efficiency of this process. However, deciding which simulations to run in order to train the classifier efficiently is still an open problem. According to PAC learning theory, data that can be separated by a classifier is expected to require $\mathcal{O}(1/ε)$ randomly selected points (simulations) to train the classifier with error less than $ε$. In this paper, building on existing algorithms, we introduce an active learning paradigm that substantially reduces the number of simulations needed to train a machine learning classifier without sacrificing accuracy. Experimental results suggest that our sampling algorithm can train a neural network, with higher accuracy, using less than half the number of simulations when compared to random sampling.
Resource and Mobility Management in Hybrid LiFi and WiFi Networks: A User-Centric Learning Approach
School of Electrical and Electronic Engineering, University College Dublin（都柏林大学学院电气与电子工程学院）；School of Information Science and Engineering, Southeast University（东南大学信息科学与工程学院）；University College Dublin（都柏林大学学院）
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning Approach
University of Exeter（埃克塞特大学）；The Alan Turing Institute（阿兰·图灵研究所）；University of Warwick（华威大学）；Tongji University（同济大学）；University of Leeds（利兹大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University（哥伦比亚大学）
CityTrajBench: A Unified Benchmark for City-Scale Vehicle Trajectory Generation
Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China（香港理工大学建筑环境与能源工程系）；Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, China（宁波东部先进研究所）；International Centre of Urban Energy Nexus, The Hong Kong Polytechnic University, Hong Kong SAR, China（香港理工大学城市能源 nexus 中心）；Department of Computer and Systems Sciences, Stockholm University, Sweden（斯德哥尔摩大学计算机与系统科学系）；Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, Eastern Institute of Technology, Ningbo, China（浙江工业智能与数字孪生重点实验室）；Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China（宁波数字孪生研究所）；LocationMind Inc., Tokyo 101-0042, Japan（LocationMind公司）
