In-vehicle snesors, IMU, and Unsprung Mass vertical velocities measurements dataset from semi-active vehicle

Citation Author(s):
Eldar
Šabanovič
Vilnius Gediminas technical university
Paulius
Kojis
Vilnius Gediminas technical university
Valentin
Ivanov
Vilnius Gediminas technical university
Miguel
Dhaens
Tenneco Automotive Europe
Viktor
Skrickij
Vilnius Gediminas technical university
Submitted by:
Eldar Sabanovic
Last updated:
Wed, 06/26/2024 - 02:27
DOI:
10.21227/9w6h-bp73
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Abstract 

This dataset is made for traditional, machine learning, and deep neural-network-based virtual sensor development and evaluation. There are these data fields: Driver torque requirement, ESP regulation, Master cylinder pressure, Steering angle, Steering angle direction, "Steering angle optimized, Vehicle velocity, Front-Left (FL) wheel velocity, Front-Right(FR) wheel velocity, Rear-Left (RL) wheel velocity, Rear-right (RR) wheel velocity, Acceleration X axis, "Acceleration Y axis, Acceleration Z axis,  Body sideslip angle, Roll rate, Longitudinal velocity, Transversal velocity, Yaw rate, FL Unsprung-Mass (UM) vertical velocity, FR UM vertical velocity, RL UM vertical velocity, RR UM vertical velocity. Data was sampled and recorded with 100 Hz rate. UM vertical velocities were estimated from measurements made by physica UM vertical displacement sensors. Data was recorded during special maneuvers on Vehicle Testing Grounds. The tests and data is described in the associated article. The dataset consists of [Train, Validation, Test] parts that has [272, 61, 60] thausa

Funding Agency: 
European Union Horizon 2020 Framework Program, Marie Skłodowska-Curie actions
Grant Number: 
872907