真车试验中车速、车重等交通信息数据集

数据集 2023YFE0202400-002 为自主开展的真车试验采集数据，在质量控制方面设计了多维度验证机制以确保数据的物理真实性、计算一致性与时序完整性。数据源验证方面，试验前对所有测试车辆进行了静态称重标定，确保车重数据具备高精度参考标准；同时对用于速度计算的波形检测传感器和静电计采集装置进行了统一校准，并记录设备序列号与校准编号，建立数据溯源体系。数据生产过程中，所有试验均在同一封闭测试场内的沥青路段上完成，采样频率为0.01s，采用定速定点启动-行驶-停驶标准流程，确保车辆运动状态可控。速度数据通过波形间隔自主计算得到，并与GPS辅助测速仪进行对照核验，抽检样本中速度误差控制在±0.3 km/h范围内。为保障数据时序与信号强度稳定，采集过程设立冗余通道，采用双通道采集与本地/云端并行存储机制。原始开路电压数据经由滑动平均算法剔除瞬态毛刺值，保持整体电压序列平稳性；对异常信号段（如突变/断点）建立自动标注机制，供后续人工审核。样本抽检方面，每组试验完成后，选取10%的车重-车速组合进行全字段人工复核，比对车重设置值、速度计算结果与电压波形的一致性，确保数据标签正确。同时在数据集发布前，统一对各字段进行逻辑规则校验，包括时间戳连续性、采样间隔稳定性、速度-电压曲线物理相关性等多维校核，进一步强化数据的结构完整性与物理合理性。总体上，本数据集在车速准确性、电压采集连续性、实验控制标准化等方面具备高度可靠性，可为后续多物理量耦合建模提供支撑数据基础。

Dataset 2023YFE0202400-002 is data collected from independently conducted real-vehicle tests. A multi-dimensional verification mechanism is designed for quality control to ensure the physical authenticity, computational consistency and temporal integrity of the data. For data source verification: Before each test, all test vehicles were subjected to static weighing calibration to ensure that the vehicle weight data has a high-precision reference standard. Meanwhile, the waveform detection sensors and electrometer acquisition devices used for speed calculation were uniformly calibrated, and the device serial numbers and calibration numbers were recorded to establish a data traceability system. During the data production process, all tests were conducted on an asphalt road section within the same closed test site. The sampling frequency was set to 0.01s, and a standard process of fixed-speed and fixed-point start-driving-stop was adopted to ensure the controllability of the vehicle's motion state. Speed data was independently calculated based on waveform intervals, and was cross-checked with a GPS-assisted speedometer. The speed error in the sampled test samples was controlled within ±0.3 km/h. To ensure the stability of data timeliness and signal strength, redundant channels were set up during the acquisition process, adopting a dual-channel acquisition and local/cloud parallel storage mechanism. The original open-circuit voltage data was processed with a moving average algorithm to remove transient spike values, maintaining the smoothness of the overall voltage sequence; an automatic annotation mechanism was established for abnormal signal segments (such as mutations/breakpoints) for subsequent manual review. For sample spot checks: After each group of tests was completed, 10% of the vehicle weight-speed combinations were selected for full-field manual review, comparing the consistency of the vehicle weight setting values, speed calculation results and voltage waveforms to ensure the correctness of the data labels. Meanwhile, before the dataset was released, unified logical rule checks were conducted on each field, including multi-dimensional verifications such as timestamp continuity, sampling interval stability, and physical correlation of speed-voltage curves, further enhancing the structural integrity and physical rationality of the data. Overall, this dataset has high reliability in terms of speed accuracy, voltage acquisition continuity, experimental control standardization, etc., and can provide a supporting data foundation for subsequent multi-physical quantity coupling modeling.

搜集汇总

背景与挑战

背景概述

该数据集源于自主开展的真车试验，采集了车速、车重等关键交通信息。通过多维度验证机制确保数据准确性，包括设备校准、标准流程和误差控制。数据集具备高可靠性，可为多物理量耦合建模提供支撑。