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Active and Passive Safety and Noise, Vibration, and Harshness (NVH)...
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Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 8990

Special Issue Editors

Dr. Yan Wang

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: vehicle active safety control; vehicle state estimation; human machine shared control
Dr. Hui Zhang

E-Mail Website
Guest Editor
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: multimodal perception; multi-agent collaboration; computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lanchun Zhang

E-Mail Website
Guest Editor
School of Automotive and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, China
Interests: new energy vehicle control and optimisation; new energy vehicle energy management
Dr. Liwei Xu

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing, China
Interests: vehicle dynamics and control; intelligent and connected vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of intelligent vehicles, driven by advanced technologies such as AI, sensors, and connectivity, has significantly transformed the landscape of automotive safety and comfort. In parallel with the development of autonomous and semi-autonomous systems, the importance of integrating both active and passive safety mechanisms has never been more critical. Furthermore, the focus on Noise, Vibration, and Harshness (NVH) has become a crucial aspect of enhancing the user experience in intelligent vehicles. This Special Issue aims to explore the state-of-the-art advancements in both active and passive safety systems and their integration with NVH in intelligent vehicles. Active safety systems, such as collision avoidance, adaptive cruise control, and lane-keeping assist, aim to prevent accidents by intervening in real-time to assist the driver or take over critical functions. Meanwhile, passive safety focuses on structural integrity, seatbelt technologies, and airbag systems to protect occupants in the event of a collision. Combined with NVH studies, which delve into noise reduction, vibration control, and the mitigation of harshness for a smoother ride, these elements contribute to improving the safety, comfort, and overall performance of modern vehicles. This Special Issue will provide a comprehensive platform for presenting novel research, case studies, and the latest technological advancements that address the intersection of safety and NVH challenges in intelligent vehicles. The focus will include both theoretical studies and practical applications of these concepts.

Research topics that are of interest for this Special Issue include but are not limited to the following:

Autonomous driving safety mechanisms and decision-making algorithms.

Integration of machine learning and sensor fusion in active safety.

Collision avoidance systems and advanced driver assistance systems.

Structural design improvements for crashworthiness.

Impact analysis and occupant protection in various crash scenarios.

Multi-material structures for lightweight and safety optimization.

Advancements in electric, hybrid, and internal combustion engines.

Integration of engine technologies in autonomous and electric vehicles.

NVH control methods for electric and autonomous vehicles.

Vibration reduction technologies for enhanced ride comfort.

Noise isolation techniques in cabin design.

Acoustic modeling and simulations for interior noise control.

Dr. Yan Wang
Dr. Hui Zhang
Prof. Dr. Lanchun Zhang
Dr. Liwei Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent vehicles
  • active safety systems
  • passive safety systems
  • autonomous driving
  • noise vibration  Harshness (NVH)
  • vehicle dynamics and control
  • electric and autonomous vehicles

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Published Papers (7 papers)

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Research

16 pages, 1260 KB  
Article
DAR-Swin: Dual-Attention Revamped Swin Transformer for Intelligent Vehicle Perception Under NVH Disturbances
by Xinglong Zhang, Zhiguo Zhang, Huihui Zuo, Chaotan Xue, Zhenjiang Wu, Zhiyu Cheng and Yan Wang
Machines 2026, 14(1), 51; https://doi.org/10.3390/machines14010051 - 31 Dec 2025
Viewed by 514
Abstract
In recent years, deep learning-based image classification has made significant progress, especially in safety-critical perception fields such as intelligent vehicles. Factors such as vibrations caused by NVH (noise, vibration, and harshness), sensor noise, and road surface roughness pose challenges to robustness and real-time [...] Read more.
In recent years, deep learning-based image classification has made significant progress, especially in safety-critical perception fields such as intelligent vehicles. Factors such as vibrations caused by NVH (noise, vibration, and harshness), sensor noise, and road surface roughness pose challenges to robustness and real-time deployment. The Transformer architecture has become a fundamental component of high-performance models. However, in complex visual environments, shifted window attention mechanisms exhibit inherent limitations: although computationally efficient, local window constraints impede cross-region semantic integration, while deep feature processing obstructs robust representation learning. To address these challenges, we propose DAR-Swin (Dual-Attention Revamped Swin Transformer), enhancing the framework through two complementary attention mechanisms. First, Scalable Self-Attention universally substitutes the standard Window-based Multi-head Self-Attention via sub-quadratic complexity operators. These operators decouple spatial positions from feature associations, enabling position-adaptive receptive fields for comprehensive contextual modeling. Second, Latent Proxy Attention integrated before the classification head adopts a learnable spatial proxy to integrate global semantic information into a fixed-size representation, while preserving relational semantics and achieving linear computational complexity through efficient proxy interactions. Extensive experiments demonstrate significant improvements over Swin Transformer Base, achieving 87.3% top-1 accuracy on CIFAR-100 (+1.5% absolute improvement) and 57.0% mAP on COCO2017 (+1.3% absolute improvement). These characteristics are particularly important for the active and passive safety features of intelligent vehicles. Full article
(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)
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29 pages, 28063 KB  
Article
Braking Energy Recovery Control Strategy Based on Instantaneous Response and Dynamic Weight Optimization
by Lulu Cai, Pengxiang Yan, Xiaopeng Yang, Liyu Yang, Yi Liu, Guanfu Huang, Shida Liu and Jingjing Fan
Machines 2026, 14(1), 10; https://doi.org/10.3390/machines14010010 - 19 Dec 2025
Cited by 2 | Viewed by 723
Abstract
Multi-axle electric heavy-duty trucks face significant challenges in maintaining braking stability and achieving real-time control during regenerative braking due to their large mass and complex inter-axle coupling dynamics. To address these issues, this paper proposes an improved model predictive control (IMPC) strategy that [...] Read more.
Multi-axle electric heavy-duty trucks face significant challenges in maintaining braking stability and achieving real-time control during regenerative braking due to their large mass and complex inter-axle coupling dynamics. To address these issues, this paper proposes an improved model predictive control (IMPC) strategy that enhances computational efficiency and control responsiveness through an instantaneous response mechanism. The approach integrates a first-order error attenuation term within the MPC framework and employs an extended Kalman filter to estimate tire–road friction in real time, enabling adaptive adjustment between energy recovery and stability objectives under varying road conditions. A control barrier function constraint is further introduced to ensure smooth and safe regenerative braking. Simulation results demonstrate improved energy recovery efficiency and faster convergence, while real-vehicle tests confirm that the IMPC maintains superior real-time performance and adaptability under complex operating conditions, reducing average computation time by approximately 14% compared with conventional MPC and showing strong potential for practical deployment. Full article
(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)
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18 pages, 6974 KB  
Article
Prior-Guided Residual Reinforcement Learning for Active Suspension Control
by Jiansen Yang, Shengkun Wang, Fan Bai, Min Wei, Xuan Sun and Yan Wang
Machines 2025, 13(11), 983; https://doi.org/10.3390/machines13110983 - 24 Oct 2025
Cited by 1 | Viewed by 1258
Abstract
Active suspension systems have gained significant attention for their capability to improve vehicle dynamics and energy efficiency. However, achieving consistent control performance under diverse and uncertain road conditions remains challenging. This paper proposes a prior-guided residual reinforcement learning framework for active suspension control. [...] Read more.
Active suspension systems have gained significant attention for their capability to improve vehicle dynamics and energy efficiency. However, achieving consistent control performance under diverse and uncertain road conditions remains challenging. This paper proposes a prior-guided residual reinforcement learning framework for active suspension control. The approach integrates a Linear Quadratic Regulator (LQR) as a prior controller to ensure baseline stability, while an enhanced Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm learns the residual control policy to improve adaptability and robustness. Moreover, residual connections and Long Short-Term Memory (LSTM) layers are incorporated into the TD3 structure to enhance dynamic modeling and training stability. The simulation results demonstrate that the proposed method achieves better control performance than passive suspension, a standalone LQR, and conventional TD3 algorithms. Full article
(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)
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15 pages, 4026 KB  
Article
Reducing Pressure Pulsation and Noise in Micro-Hydraulic Systems of Machine Equipment
by Michał Stosiak, Krzysztof Towarnicki, Paulius Skačkauskas and Mykola Karpenko
Machines 2025, 13(11), 981; https://doi.org/10.3390/machines13110981 - 24 Oct 2025
Cited by 3 | Viewed by 1319
Abstract
The paper highlights that hydraulic systems are widely used in various machine applications. Among the evaluation criteria for these systems, the noise-related criterion is also considered. This criterion also applies to micro-hydraulic systems as the permissible level of noise emitted into the environment [...] Read more.
The paper highlights that hydraulic systems are widely used in various machine applications. Among the evaluation criteria for these systems, the noise-related criterion is also considered. This criterion also applies to micro-hydraulic systems as the permissible level of noise emitted into the environment is linked to the installed power, which in micro-hydraulic systems is at least an order of magnitude lower than in conventional hydraulic systems. Failure to comply with EU ambient noise emission standards may result in the machine not being approved for use. It is therefore important to identify noise sources and minimize them. It has been noted that, in hydraulic systems, the primary source of noise is pressure pulsation across a wide frequency range. Moreover, it has been pointed out that low-frequency noise and vibrations are particularly harmful to humans. Thus, pressure pulsation dampers are proposed that are effective both at specific forcing frequencies and across a wide frequency range. Experimental results of a micro-hydraulic system are presented. Full article
(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)
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31 pages, 5103 KB  
Article
Multi-Objective Optimization of Battery Pack Mounting System for Construction Machinery
by Dunhuang Lin, Run Sun, Hai Wei and Yujiang Wang
Machines 2025, 13(8), 705; https://doi.org/10.3390/machines13080705 - 9 Aug 2025
Viewed by 930
Abstract
With the accelerated electrification of engineering machinery, the battery pack mounting system plays a critical role in enhancing the vehicle’s structural safety and vibration-damping performance. This paper proposes an optimization framework for the multi-layer battery pack mounting systems used in such machinery. The [...] Read more.
With the accelerated electrification of engineering machinery, the battery pack mounting system plays a critical role in enhancing the vehicle’s structural safety and vibration-damping performance. This paper proposes an optimization framework for the multi-layer battery pack mounting systems used in such machinery. The framework integrates a multi-degree-of-freedom (MDOF) dynamic model, uncertainty analysis, and a multi-objective evolutionary algorithm (MOEA) to resolve the vibration suppression challenges associated with large-mass battery packs under harsh operating conditions. A parameter optimization method is introduced with the objectives of increasing natural frequencies, enhancing modal decoupling, and avoiding resonance. By identifying key influencing parameters and performing a comprehensive optimization of mount locations and stiffness, this approach achieves a highly efficient improvement in dynamic performance. Simulation and analysis results demonstrate that, compared to the initial design, the proposed method significantly elevates the system’s first six natural frequencies (by 13.6%, 7.8%, 3.3%, 2.5%, 11.7%, and 9.4%, respectively). Furthermore, it enhances the energy decoupling between modes, with the decoupling rates for Y-direction translation and Z-axis rotation both increasing by 11.3%. This achieves a synergistic improvement in the system’s vibration avoidance and decoupling performance. The methodology offers an effective means to optimize the safety and operational stability of battery systems in electric engineering machinery. Full article
(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)
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17 pages, 5504 KB  
Article
Multi-Objective Optimization of Acoustic Black Hole Plate Attached to Electric Automotive Steering Machine for Maximizing Vibration Attenuation Performance
by Xiaofei Du, Weilong Li, Fei Hao and Qidi Fu
Machines 2025, 13(8), 647; https://doi.org/10.3390/machines13080647 - 24 Jul 2025
Viewed by 1341
Abstract
This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, [...] Read more.
This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, we conceive an integrated vibration suppression framework synergizing advanced computational modeling with intelligent optimization algorithms. A high-fidelity finite element (FEM) model integrating ABH-attached steering machine system was developed and subjected to experimental validation via rigorous modal testing. To address computational challenges in design optimization, a hybrid modeling strategy integrating parametric design (using Latin Hypercube Sampling, LHS) with Kriging surrogate modeling is proposed. Systematic parameterization of ABH geometry and damping layer dimensions generated 40 training datasets and 12 validation datasets. Surrogate model verification confirms the model’s precise mapping of vibration characteristics across the design space. Subsequent multi-objective genetic algorithm optimization targeting RMS velocity suppression achieved substantial vibration attenuation (29.2%) compared to baseline parameters. The developed methodology provides automotive researchers and engineers with an efficient suitable design tool for vibration-sensitive automotive component design. Full article
(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)
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27 pages, 4076 KB  
Article
Horizontal and Vertical Coordinated Control of Three-Axis Heavy Vehicles
by Lanchun Zhang, Fei Huang, Hao Cui, Yaqi Wang and Lin Yang
Machines 2025, 13(2), 123; https://doi.org/10.3390/machines13020123 - 7 Feb 2025
Cited by 1 | Viewed by 1562
Abstract
In order to coordinate the transverse motion control and longitudinal motion control in the tracking control process and ensure the yaw stability and roll stability in the tracking process, a transverse and longitudinal coordinated control method of three-axis heavy vehicles is designed based [...] Read more.
In order to coordinate the transverse motion control and longitudinal motion control in the tracking control process and ensure the yaw stability and roll stability in the tracking process, a transverse and longitudinal coordinated control method of three-axis heavy vehicles is designed based on model predictive control. The lateral motion controller is designed based on the phase plane method. The upper controller calculates the front wheel angle and additional yaw moment, which ensures the yaw stability while tracking the vehicle. The lower controller calculates the driving force and braking force of the three-axis heavy vehicle. The velocity planning method is designed with the coupling point of longitudinal velocity to coordinate the lateral and longitudinal motion controllers and prevent vehicle rollover. By building the vehicle model in Trucksim (2016.1) and establishing the horizontal and vertical coordination control in Matlab (R2016b), the designed horizontal and vertical coordination control method is simulated and verified. The simulation results show that the designed method can accurately track the reference trajectory while ensuring the yaw stability and roll stability of the three-axis heavy vehicle. Full article
(This article belongs to the Special Issue Active and Passive Safety and Noise, Vibration, and Harshness (NVH) of Intelligent Vehicles)
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