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Vehicle Sensing and Dynamic Control
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Vehicle Sensing and Dynamic Control

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (15 July 2024) | Viewed by 6295

Special Issue Editors

Prof. Dr. Juan A. Cabrera

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Málaga, 29071 Malaga, Spain
Interests: vehicle dynamics; control of active safety systems; tire parameters estimation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Javier Perez Fernandez

E-Mail Website
Guest Editor
Department of Mechanic Engineering and Fluid Mechanics, University of Málaga, 29071 Malaga, Spain
Interests: mechanical engineering; modeling and control of vehicles; electric vehicles; spiking neural networks

Special Issue Information

Dear Colleagues,

When a vehicle controller is developed, many aspects of the vehicle have to be taken into account, including the following: the vehicle model, the iteration between the tire and the road, the aerodynamics, the steering system, the braking system, the powertrain, and the suspension system. All of these aspects determine the development of novel control systems and algorithms, and help to avoid errors in the implementation of these systems in real vehicles.

Additionally, sensors play a pivotal role in providing sufficient information to control vehicle states. In addition, sensor filtering or observers provide the indirect measurements necessary for optimal control.

This Special Issue will address innovative research in the following areas:

The modeling and control of vehicle behavior: vehicle model, tire dynamic model, online learning and adaptation, model-based controller, linear-quadratic regulator (LQR), sliding mode control (SMC), H-infinity, and model predictive control (MPC).

Vehicle state estimation: Sensor fusion, Kalman Filtering (EKF, UKF), Recursive Least Squares (RLSs), Particle Filter, and Complementare Filter.

 Active safety systems: development of intelligent control algorithms for anti-lock braking systems (ABSs), traction control systems (TCSs), electronic stability program (ESP), advanced driver assistance systems (ADASs), and the integration of related active safety features/devices into new vehicles.

The topics of interest include, but are not limited to, the following:

  • Vehicle Dynamics Control
  • Active Safety Systems
  • Autonomous Driving Systems
  • Identification and Estimation
  • Steering, Braking, Tires, Suspension
  • Advanced Driver Assistance Systems
  • Driver–Vehicle Systems
  • Electric Vehicles Model

Prof. Dr. Juan A. Cabrera
Dr. Javier Perez Fernandez
Guest Editors

Manuscript Submission Information

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Published Papers (6 papers)

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Research

19 pages, 5459 KiB  
Article
An Improved ELOS Guidance Law for Path Following of Underactuated Unmanned Surface Vehicles
by Shipeng Wu, Hui Ye, Wei Liu, Xiaofei Yang, Ziqing Liu and Hao Zhang
Sensors 2024, 24(16), 5384; https://doi.org/10.3390/s24165384 - 20 Aug 2024
Viewed by 447
Abstract
In this paper, targeting the problem that it is difficult to deal with the time-varying sideslip angle of an underactuated unmanned surface vehicle (USV), a line–of–sight (LOS) guidance law based on an improved extended state observer (ESO) is proposed. A reduced-order ESO is [...] Read more.
In this paper, targeting the problem that it is difficult to deal with the time-varying sideslip angle of an underactuated unmanned surface vehicle (USV), a line–of–sight (LOS) guidance law based on an improved extended state observer (ESO) is proposed. A reduced-order ESO is introduced into the identification of the sideslip angle caused by the environmental disturbance, which ensures a fast and accurate estimation of the sideslip angle. This enables the USV to follow the reference path with high precision, despite external disturbances from wind, waves, and currents. These unknown disturbances are modeled as drift, which the modified ESO-based LOS guidance law compensates for using the ESO. In the guidance subsystem incorporating the reduced-order state observer, the observer estimation and track errors are proved uniformly ultimately bounded. Simulation and experimental results are presented to validate the effectiveness of the proposed method. The simulation and comparison results demonstrate that the proposed ELOS guidance can help a USV track different types of paths quickly and smoothly. Additionally, the experimental results confirm the feasibility of the method. Full article
(This article belongs to the Special Issue Vehicle Sensing and Dynamic Control)
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25 pages, 9675 KiB  
Article
Strain Gauge Calibration for High Speed Weight-in-Motion Station
by Agnieszka Socha and Jacek Izydorczyk
Sensors 2024, 24(15), 4845; https://doi.org/10.3390/s24154845 - 25 Jul 2024
Viewed by 396
Abstract
The development of systems for weighing vehicles in motion aims to introduce systems allowing automatic enforcement of regulations. HSWIM (high speed weight-in-motion) systems enable measurement of a mass of vehicles passing through a measurement station without disturbing the traffic flow. This article focuses [...] Read more.
The development of systems for weighing vehicles in motion aims to introduce systems allowing automatic enforcement of regulations. HSWIM (high speed weight-in-motion) systems enable measurement of a mass of vehicles passing through a measurement station without disturbing the traffic flow. This article focuses on the calibration of a weighing station for moving vehicles, where strain gauge sensors are used to measure pressures. A solution was proposed to replace the calibration coefficients with calibration functions. The analysis was performed for two methods of determining wheel loads: based on the maximum of the signal from strain gauge sensors and on a method using the field under the signal and the vehicle’s speed. Calibration functions were determined jointly for all test vehicles and separately for each of them. The use of a calibration function for a specific vehicle type made it possible to determine wheel pressure and gross weight with a level of accuracy that allowed the weigh-in-motion station to be classified as a direct enforcement system. The achieved improvement in the accuracy of weighing in motion did not require any interference with the measurement station. The proposed change in the method of calibration and, ultimately, determination of wheel loads required only a change in the algorithm for determining wheel loads. Full article
(This article belongs to the Special Issue Vehicle Sensing and Dynamic Control)
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20 pages, 1958 KiB  
Article
Integrating LiDAR Sensor Data into Microsimulation Model Calibration for Proactive Safety Analysis
by Morris Igene, Qiyang Luo, Keshav Jimee, Mohammad Soltanirad, Tamer Bataineh and Hongchao Liu
Sensors 2024, 24(13), 4393; https://doi.org/10.3390/s24134393 - 6 Jul 2024
Viewed by 1025
Abstract
Studies have shown that vehicle trajectory data are effective for calibrating microsimulation models. Light Detection and Ranging (LiDAR) technology offers high-resolution 3D data, allowing for detailed mapping of the surrounding environment, including road geometry, roadside infrastructures, and moving objects such as vehicles, cyclists, [...] Read more.
Studies have shown that vehicle trajectory data are effective for calibrating microsimulation models. Light Detection and Ranging (LiDAR) technology offers high-resolution 3D data, allowing for detailed mapping of the surrounding environment, including road geometry, roadside infrastructures, and moving objects such as vehicles, cyclists, and pedestrians. Unlike other traditional methods of trajectory data collection, LiDAR’s high-speed data processing, fine angular resolution, high measurement accuracy, and high performance in adverse weather and low-light conditions make it well suited for applications requiring real-time response, such as autonomous vehicles. This research presents a comprehensive framework for integrating LiDAR sensor data into simulation models and their accurate calibration strategies for proactive safety analysis. Vehicle trajectory data were extracted from LiDAR point clouds collected at six urban signalized intersections in Lubbock, Texas, in the USA. Each study intersection was modeled with PTV VISSIM and calibrated to replicate the observed field scenarios. The Directed Brute Force method was used to calibrate two car-following and two lane-change parameters of the Wiedemann 1999 model in VISSIM, resulting in an average accuracy of 92.7%. Rear-end conflicts extracted from the calibrated models combined with a ten-year historical crash dataset were fitted into a Negative Binomial (NB) model to estimate the model’s parameters. In all the six intersections, rear-end conflict count is a statistically significant predictor (p-value < 0.05) of observed rear-end crash frequency. The outcome of this study provides a framework for the combined use of LiDAR-based vehicle trajectory data, microsimulation, and surrogate safety assessment tools to transportation professionals. This integration allows for more accurate and proactive safety evaluations, which are essential for designing safer transportation systems, effective traffic control strategies, and predicting future congestion problems. Full article
(This article belongs to the Special Issue Vehicle Sensing and Dynamic Control)
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30 pages, 15278 KiB  
Article
Quantitative Detection of Vertical Track Irregularities under Non-Stationary Conditions with Variable Vehicle Speed
by Qiushi Wang, Hui Zhao, Dao Gong, Jinsong Zhou and Zhongmin Xiao
Sensors 2024, 24(12), 3804; https://doi.org/10.3390/s24123804 - 12 Jun 2024
Viewed by 578
Abstract
Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The traditional signal analysis [...] Read more.
Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The traditional signal analysis methods are unsuitable for non-stationary processes, making the quantitative detection of the wavelength and amplitude of track irregularities difficult. To solve the above problems, this paper proposes a quantitative detection method of track irregularities under non-stationary conditions with variable vehicle speed by order tracking analysis for the first time. Firstly, a simplified wheel–rail dynamic model is established to derive the quantitative relationship between the axle-box vertical vibration and the track vertical irregularities. Secondly, the Simpson double integration method is proposed to calculate the axle-box vertical displacement based on the axle-box vertical acceleration, and the process error is optimized. Thirdly, based on the order tracking analysis theory, the angular domain resampling is performed on the axle-box vertical displacement time-domain signal in combination with the wheel rotation speed signals, and the quantitative detection of the track irregularities is achieved. Finally, the proposed method is validated based on simulation and field test analysis cases. We provide theoretical support and method reference for the quantitative detection method of track irregularities. Full article
(This article belongs to the Special Issue Vehicle Sensing and Dynamic Control)
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22 pages, 7663 KiB  
Article
Research on Electric Oil–Pneumatic Active Suspension Based on Fractional-Order PID Position Control
by Yaozeng Hu, Jianze Liu, Zhuang Wang, Jingming Zhang and Jiang Liu
Sensors 2024, 24(5), 1644; https://doi.org/10.3390/s24051644 - 2 Mar 2024
Cited by 1 | Viewed by 910
Abstract
In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. An FOPID algorithm is used to [...] Read more.
In this study, an electric oil and gas actuator based on fractional-order PID position feedback control is proposed, through which the damping coefficient of the suspension system is adjusted to realize the active control of the suspension. An FOPID algorithm is used to control the motor’s rotational angle to realize the damping adjustment of the suspension system. In this process, the road roughness is collected by the sensors as the criterion of damping adjustment, and the particle swarm algorithm is utilized to find the optimal objective function under different road surface slopes, to obtain the optimal cornering value. According to the mathematical and physical model of the suspension system, the simulation model and the corresponding test platform of this type of suspension system are built. The simulation and experimental results show that the simulation results of the fractional-order nonlinear suspension model are closer to the actual experimental values than those of the traditional linear suspension model, and the accuracy of each performance index is improved by more than 18.5%. The designed active suspension system optimizes the body acceleration, suspension dynamic deflection, and tire dynamic load to 89.8%, 56.7%, and 73.4% of the passive suspension, respectively. It is worth noting that, compared to traditional PID control circuits, the FOPID control circuit designed for motors has an improved control performance. This study provides an effective theoretical and empirical basis for the control and optimization of fractional-order nonlinear suspension systems. Full article
(This article belongs to the Special Issue Vehicle Sensing and Dynamic Control)
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24 pages, 5940 KiB  
Article
An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres
by Aymen Alshawi, Stefano De Pinto, Pietro Stano, Sebastiaan van Aalst, Kylian Praet, Emilie Boulay, Davide Ivone, Patrick Gruber and Aldo Sorniotti
Sensors 2024, 24(2), 436; https://doi.org/10.3390/s24020436 - 10 Jan 2024
Cited by 3 | Viewed by 1629
Abstract
This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, [...] Read more.
This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre–road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre–road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism. Full article
(This article belongs to the Special Issue Vehicle Sensing and Dynamic Control)
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