本数据集用于记录联网车辆编队算例中分布式受控不变集计算随车辆规模变化的 CPU 时间。数据基于异构车辆纵向编队模型生成，车辆状态包括相对平衡车头间距的增量 Δh 和相对平衡速度的增量 Δv，控制量为牵引力增量 ΔF。算例通过对车辆参数进行随机采样、连续时间模型线性化、以 0.05 s 采样周期离散化，并在给定安全集、输入约束与信息结构下求解线性规划形式的 DCIS 合成问题，记录不同车辆数量下的计算耗时。该数据可用于评估分布式安全控制算法在稀疏互联系统中的可扩展性、计算复杂度与工程可复现实验对比。
", "ds_source": "数据来源于论文《Computing Distributed Controlled Invariant Sets for Interconnected Linear Systems With Information Structures》的联网车辆编队数值算例。车辆质量、阻力系数等参数在论文给定区间内随机生成，平衡车头间距为 3 m，平衡速度为 20 m/s，连续时间模型经线性化和离散化后用于 DCIS 计算。PlatoonData 记录不同车辆数量下 DCIS 计算 CPU 时间；实验运行环境为 1.80 GHz 笔记本电脑、16 GB 内存。
", "ds_process_way": "采用仿真生成与优化求解相结合的方法。首先根据论文给定参数区间生成异构车辆参数，并构造线性化车辆编队模型；然后采用 0.05 s 采样周期得到离散时间系统矩阵，设定车头间距、速度和牵引力约束；进一步通过后向可达集递推获得各子系统多面体模板，并将 DCIS 合成问题转化为线性规划；最后对不同车辆数量重复求解，记录 CPU 时间并形成 PlatoonData 所示的对数坐标曲线数据。
", "ds_quality": "数据为论文数值实验产生的计算性能数据，非实地观测数据。各数据点在相同计算硬件环境和相同算法流程下生成，主要质量控制包括：模型参数范围与论文设置一致；线性规划可行性与 DCIS 约束满足性检查；计算时间单位和车辆数量索引统一；记录表中无缺失值、重复值或明显异常值。该数据适合用于复现 PlatoonData 的趋势性结论，但不宜直接外推为所有硬件环境下的绝对运行时间。
", "ds_acq_start_time": "2025-01-01 00:00:00", "ds_acq_end_time": null, "ds_acq_place": "仿真生成，无固定地理采集地点；计算环境为 1.80 GHz 笔记本电脑、16 GB 内存", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 9826, "ds_files_count": 0, "ds_format": "CSV", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "332af69b-c46c-4e47-9bf1-357469fcaa32.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据用于复现和分析论文 Fig. 2 中 DCIS 计算耗时随车辆规模增长的变化趋势，可用于分布式安全控制、不变集计算和信息结构影响分析的算法对比。使用时应说明 CPU 时间依赖硬件环境，绝对数值仅代表论文实验配置下的结果。", "ds_from_station": "", "organization_id": "9ecaaa78-39e9-411e-9f24-274e12aa643f", "ds_serv_man": "何心", "ds_serv_phone": "18961373056", "ds_serv_mail": "xinh@hust.edu.cn", "doi_value": "", "subject_codes": [ "410" ], "quality_level": 0, "publish_time": "2026-07-09 10:58:01", "last_updated": "2026-07-09 10:58:01", "protected": false, "protected_to": "2028-06-30 00:00:00", "lang": "zh", "cstr": "", "i18n": { "en": { "title": "CPU Time Dataset for DCIS Computation in the Connected Vehicle Platooning Example", "ds_format": "CSV", "ds_source": "The data are derived from the connected vehicle platooning numerical example in the paper \"Computing Distributed Controlled Invariant Sets for Interconnected Linear Systems With Information Structures.\" Vehicle masses and drag-related coefficients are randomly generated within the intervals specified in the paper. The equilibrium headway is 3 m and the equilibrium velocity is 20 m/s. The continuous-time model is linearized and discretized for DCIS computation. PlatoonData reports the CPU time of DCIS computation for different numbers of vehicles, with all experiments conducted on a 1.80-GHz laptop with 16 GB RAM.", "ds_quality": "The dataset consists of computational-performance records from numerical experiments rather than field observations. All data points are generated under the same hardware environment and algorithmic pipeline. Quality control includes consistency checks against the model-parameter ranges specified in the paper, feasibility checks of the LP and DCIS constraints, unified CPU-time units and vehicle-count indices, and validation that the records contain no missing, duplicated, or apparent anomalous values. The dataset is suitable for reproducing the trend shown in PlatoonData, but the absolute runtime should not be directly generalized to different hardware environments.", "ds_ref_way": "", "ds_abstract": "This dataset corresponds to Fig. 2 of the paper and records the CPU time for computing distributed controlled invariant sets (DCISs) in the connected vehicle platooning example as the number of vehicles varies. The data are generated from a heterogeneous longitudinal platooning model, where each vehicle state consists of the incremental headway Δh and incremental velocity Δv, and the control input is the incremental driving force ΔF. The example samples vehicle parameters, linearizes the continuous-time dynamics, discretizes the model with a 0.05 s sampling period, and solves a linear-programming-based DCIS synthesis problem under prescribed safety sets, input constraints, and information structures. The dataset supports scalability and computational-complexity evaluation of distributed safety-control algorithms for sparse interconnected systems.", "ds_time_res": "", "ds_acq_place": "Simulation-based dataset with no fixed geographic acquisition site; computation performed on a 1.80-GHz laptop with 16 GB RAM", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The dataset is produced through simulation and optimization. Heterogeneous vehicle parameters are generated according to the intervals specified in the paper, and a linearized platooning model is constructed. The model is then discretized with a sampling period of 0.05 s, with constraints imposed on headway, velocity, and driving force. Polytopic templates are obtained through backward-reachable-set recursions, and the DCIS synthesis problem is formulated as a linear program. The LP is solved for different numbers of vehicles, and the CPU time records are used to generate the log-scale curve in PlatoonData.", "ds_ref_instruction": "This dataset can be used to reproduce and analyze the trend in Fig. 2, namely the growth of DCIS computation time with the number of vehicles. It is suitable for algorithmic comparison in distributed safety control, invariant-set computation, and information-structure analysis. Users should note that CPU time depends on the hardware environment, and the absolute values represent the experimental setup used in the paper." } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "联网车辆编队", "分布式受控不变集", "CPU时间", "可扩展性" ], "ds_subject_tags": [ "工程与技术科学基础学科" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "ds_contributors": [ { "true_name": "杨立仁", "email": "yliren@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" }, { "true_name": "叶林涛", "email": "yelintao93@hust.edu.cn", "work_for": "华中科技大学人工智能与自动化学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "杨立仁", "email": "yliren@seu.edu.cn", "work_for": "东南大学数学学院", "country": "中国" }, { "true_name": "叶林涛", "email": "yelintao93@hust.edu.cn", "work_for": "华中科技大学人工智能与自动化学院", "country": "中国" } ], "ds_managers": [ { "true_name": "何心", "email": "xinh@hust.edu.cn", "work_for": "华中科技大学", "country": "中国" } ], "category": "其他" }