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Machines
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Intelligent Control and Active Safety Techniques for Road Vehicles
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Intelligent Control and Active Safety Techniques for Road Vehicles

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 8159

Special Issue Editors

Prof. Dr. Yuping He

E-Mail Website
Guest Editor
Department of Automotive, Mechanical and Manufacturing Engineering, University of Ontario Institute of Technology, 2000 Simecoe Street North, Oshawa, ON L1H 7K4, Canada
Interests: vehicle dynamics; driver–vehicle–road interactions; design optimization; active safety systems; autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Qinghui Zhou

E-Mail Website
Guest Editor
School of Mechanical-Electronic and vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing, China
Interests: vehicle active safety; autonomous vehicles; vehicle safety control

Special Issue Information

Dear Colleagues,

The past three decades have witnessed the rapid development of semi-autonomous and autonomous vehicles. The two main design criteria for these vehicles are increasing road safety and improving transportation efficiency. To some extent, increasing road safety and improving transportation efficiency may be simply understood as enhancing vehicle stability and increasing traveling speed, respectively. From the view of vehicle dynamics, there is a trade-off between vehicle stability and traveling speed. A higher traveling speed can be attained at the expense of dropping vehicle stability, and vice versa. To simultaneously satisfy the two main design criteria and address the trade-off, effective and promising solutions are intelligent control and active safety techniques.

In light of this, we propose a Special Issue entitled "Intelligent Control and Active Safety Techniques for Road Vehicles" to showcase the latest original achievements, foster the exchange of cutting-edge perspectives, and promote interdisciplinary research in this field. This Special Issue aims to explore the potential of using emerging and advanced techniques in intelligent control and active safety to address the complex dynamics and design challenges faced by vehicle systems designers and developers.  

The topics to be covered in this Special Issue include, but are not limited to, the following:

  • Autonomous and intelligent vehicles;
  • Semi-autonomous driving control;
  • Vehicle networking;
  • Driver–vehicle–road coordinated control;
  • Vehicle dynamics and control;
  • Active vehicle safety;
  • Control and calibration of engines and powertrains;
  • Automotive electrification and electronic control;
  • Active safety designs for electric vehicles, hybrid electric vehicles, fuel cell vehicles.

We invite researchers from academia and industry to contribute their original research, methodologies, and perspectives to this Special Issue. By bringing together these diverse contributions, we aim to accelerate the development and application of intelligent control and active safety techniques for road vehicles.

We look forward to receiving your valuable contributions and sharing ground-breaking advancements in the field of intelligent energy vehicle control.

Prof. Dr. Yuping He
Dr. Qinghui Zhou
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 control
  • active safety
  • autonomous vehicles
  • semi-autonomous vehicles
  • driver–vehicle–road interactions
  • AI-based control
  • coordinated control
  • vehicle networking
  • driver models

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

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17 pages, 6714 KiB  
Article
Development of Deterministic Communication for In-Vehicle Networks Based on Software-Defined Time-Sensitive Networking
by Binqi Li, Yuan Zhu, Qin Liu and Xiangxi Yao
Machines 2024, 12(11), 816; https://doi.org/10.3390/machines12110816 - 15 Nov 2024
Viewed by 507
Abstract
To support more advanced functionality in vehicles, there is the challenge of deterministic and reliable transmission of sensor data and control signals. Time-sensitive networking (TSN) is the most promising candidate to meet this demand by leveraging IEEE 802.1 ethernet standards, which include time [...] Read more.
To support more advanced functionality in vehicles, there is the challenge of deterministic and reliable transmission of sensor data and control signals. Time-sensitive networking (TSN) is the most promising candidate to meet this demand by leveraging IEEE 802.1 ethernet standards, which include time synchronization, traffic shaping, and low-latency forwarding mechanisms. To explore the implementation of TSN for in-vehicle networks (IVN), this paper proposes a robust integer linear programming (ILP)-based scheduling model for time-sensitive data streams to mitigate the vulnerabilities of the time-aware shaper (TAS) mechanism in practice. Furthermore, we integrate this scheduling model into a software-defined time-sensitive networking (SD-TSN) architecture to automate the scheduling computations and configurations in the design phase. This SD-TSN architecture can offer a flexible and programmable approach to network management, enabling precise control over timing constraints and quality-of-service (QoS) parameters for time-sensitive traffic. Firstly, data transmission requirements are gathered by the centralized user configuration (CUC) module to acquire traffic information. Subsequently, the CNC module transfers the computed results of routing and scheduling to the YANG model for configuration delivery. Finally, automotive TSN switches can complete local configuration by parsing the received configuration messages. Through an experimental validation based on a physical platform, this study demonstrates the effectiveness of the scheduling algorithm and SD-TSN architecture in enhancing deterministic communication for in-vehicle networks. Full article
(This article belongs to the Special Issue Intelligent Control and Active Safety Techniques for Road Vehicles)
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15 pages, 4907 KiB  
Article
KCS-YOLO: An Improved Algorithm for Traffic Light Detection under Low Visibility Conditions
by Qinghui Zhou, Diyi Zhang, Haoshi Liu and Yuping He
Machines 2024, 12(8), 557; https://doi.org/10.3390/machines12080557 - 15 Aug 2024
Viewed by 718
Abstract
Autonomous vehicles face challenges in small-target detection and, in particular, in accurately identifying traffic lights under low visibility conditions, e.g., fog, rain, and blurred night-time lighting. To address these issues, this paper proposes an improved algorithm, namely KCS-YOLO (you only look once), to [...] Read more.
Autonomous vehicles face challenges in small-target detection and, in particular, in accurately identifying traffic lights under low visibility conditions, e.g., fog, rain, and blurred night-time lighting. To address these issues, this paper proposes an improved algorithm, namely KCS-YOLO (you only look once), to increase the accuracy of detecting and recognizing traffic lights under low visibility conditions. First, a comparison was made to assess different YOLO algorithms. The benchmark indicates that the YOLOv5n algorithm achieves the highest mean average precision (mAP) with fewer parameters. To enhance the capability for detecting small targets, the algorithm built upon YOLOv5n, namely KCS-YOLO, was developed using the K-means++ algorithm for clustering marked multi-dimensional target frames, embedding the convolutional block attention module (CBAM) attention mechanism, and constructing a small-target detection layer. Second, an image dataset of traffic lights was generated, which was preprocessed using the dark channel prior dehazing algorithm to enhance the proposed algorithm’s recognition capability and robustness. Finally, KCS-YOLO was evaluated through comparison and ablation experiments. The experimental results showed that the mAP of KCS-YOLO reaches 98.87%, an increase of 5.03% over its counterpart of YOLOv5n. This indicates that KCS-YOLO features high accuracy in object detection and recognition, thereby enhancing the capability of traffic light detection and recognition for autonomous vehicles in low visibility conditions. Full article
(This article belongs to the Special Issue Intelligent Control and Active Safety Techniques for Road Vehicles)
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25 pages, 12062 KiB  
Article
Designing an Experimental Platform to Assess Ergonomic Factors and Distraction Index in Law Enforcement Vehicles during Mission-Based Routes
by Marvin H. Cheng, Jinhua Guan, Hemal K. Dave, Robert S. White, Richard L. Whisler, Joyce V. Zwiener, Hugo E. Camargo and Richard S. Current
Machines 2024, 12(8), 502; https://doi.org/10.3390/machines12080502 - 24 Jul 2024
Viewed by 839
Abstract
Mission-based routes for various occupations play a crucial role in occupational driver safety, with accident causes varying according to specific mission requirements. This study focuses on the development of a system to address driver distraction among law enforcement officers by optimizing the Driver–Vehicle [...] Read more.
Mission-based routes for various occupations play a crucial role in occupational driver safety, with accident causes varying according to specific mission requirements. This study focuses on the development of a system to address driver distraction among law enforcement officers by optimizing the Driver–Vehicle Interface (DVI). Poorly designed DVIs in law enforcement vehicles, often fitted with aftermarket police equipment, can lead to perceptual-motor problems such as obstructed vision, difficulty reaching controls, and operational errors, resulting in driver distraction. To mitigate these issues, we developed a driving simulation platform specifically for law enforcement vehicles. The development process involved the selection and placement of sensors to monitor driver behavior and interaction with equipment. Key criteria for sensor selection included accuracy, reliability, and the ability to integrate seamlessly with existing vehicle systems. Sensor positions were strategically located based on previous ergonomic studies and digital human modeling to ensure comprehensive monitoring without obstructing the driver’s field of view or access to controls. Our system incorporates sensors positioned on the dashboard, steering wheel, and critical control interfaces, providing real-time data on driver interactions with the vehicle equipment. A supervised machine learning-based prediction model was devised to evaluate the driver’s level of distraction. The configured placement and integration of sensors should be further studied to ensure the updated DVI reduces driver distraction and supports safer mission-based driving operations. Full article
(This article belongs to the Special Issue Intelligent Control and Active Safety Techniques for Road Vehicles)
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19 pages, 2460 KiB  
Article
RBF-Based Fractional-Order SMC Fault-Tolerant Controller for a Nonlinear Active Suspension
by Weipeng Zhao and Liang Gu
Machines 2024, 12(4), 270; https://doi.org/10.3390/machines12040270 - 18 Apr 2024
Viewed by 940
Abstract
Active suspension control technologies have become increasingly significant in improving suspension performance for driving stability and comfort. An RBF-based fractional-order SMC fault-tolerant controller is developed in this research to guarantee ride comfort and handling stability when faced with the partial loss of actuator [...] Read more.
Active suspension control technologies have become increasingly significant in improving suspension performance for driving stability and comfort. An RBF-based fractional-order SMC fault-tolerant controller is developed in this research to guarantee ride comfort and handling stability when faced with the partial loss of actuator effectiveness due to failure. To obtain better control performance, fractional-order theory and the RBF algorithm are discussed to solve the jitter vibration problem in SMC, and the RBF is exploited to obtain a more appropriate switching gain. First, a half-nonlinear active suspension model and a fault car model are presented. Then, the design process of the RBF-based fractional-order SMC fault-tolerant controller is described. Next, a simulation is presented to demonstrate the effectiveness of the proposed strategy. According to the simulation, the proposed method can improve performance in the case of a healthy suspension, and the fault-tolerant controller can guarantee the capabilities when actuators go wrong. Full article
(This article belongs to the Special Issue Intelligent Control and Active Safety Techniques for Road Vehicles)
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27 pages, 14638 KiB  
Article
Simulation and Validation of an 8 × 8 Scaled Electric Combat Vehicle
by Junwoo Kim, Moustafa El-Gindy and Zeinab El-Sayegh
Machines 2024, 12(2), 146; https://doi.org/10.3390/machines12020146 - 19 Feb 2024
Cited by 1 | Viewed by 1690
Abstract
In this research, an 8 × 8 scaled electric combat vehicle (SECV) is built. The scaled vehicle is evaluated in both experimental and simulated methods to analyze its performance. The scaled vehicle is developed to apply the Ackermann condition by implementing the individual [...] Read more.
In this research, an 8 × 8 scaled electric combat vehicle (SECV) is built. The scaled vehicle is evaluated in both experimental and simulated methods to analyze its performance. The scaled vehicle is developed to apply the Ackermann condition by implementing the individual steering and individual wheel speed control system at low speed. Individual eight-wheel rotational velocity control and individual eight-wheel steering angle control in real time are developed and installed on the remotely controlled scaled vehicle to meet a perfect Ackermann condition. Three different steering scenarios are developed and applied: a traditional steering scenario (first and second axle steering), fixed third axle steering scenario (first, second, and fourth axle steering), and all-wheel steering scenario. Stationary evaluation, turn radius evaluation, and double lane change evaluation are conducted to verify the application of the Ackermann condition. The differences between the experimental results and the simulated data are within an acceptable range. An important demonstration of this research is the novel validation of physical and simulated data in the application of the Ackermann condition for eight-wheel steering and velocity control for the three steering scenarios. Full article
(This article belongs to the Special Issue Intelligent Control and Active Safety Techniques for Road Vehicles)
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18 pages, 5097 KiB  
Article
Simultaneous Estimation of Vehicle Sideslip and Roll Angles Using an Event-Triggered-Based IoT Architecture
by Fernando Viadero-Monasterio, Javier García, Miguel Meléndez-Useros, Manuel Jiménez-Salas, Beatriz López Boada and María Jesús López Boada
Machines 2024, 12(1), 53; https://doi.org/10.3390/machines12010053 - 11 Jan 2024
Cited by 7 | Viewed by 1742
Abstract
In recent years, there has been a significant integration of advanced technology into the automotive industry, aimed primarily at enhancing safety and ride comfort. While a notable proportion of these driver-assist systems focuses on skid prevention, insufficient attention has been paid to addressing [...] Read more.
In recent years, there has been a significant integration of advanced technology into the automotive industry, aimed primarily at enhancing safety and ride comfort. While a notable proportion of these driver-assist systems focuses on skid prevention, insufficient attention has been paid to addressing other crucial scenarios, such as rollovers. The accurate estimation of slip and roll angles plays a vital role in ensuring vehicle control and safety, making these parameters essential, especially with the rise of modern technologies that incorporate networked communication and distributed computing. Furthermore, there exists a lag in the transmission of information between the various vehicle systems, including sensors, actuators, and controllers. This paper outlines the design of an IoT architecture that accurately estimates the sideslip angle and roll angle of a vehicle, while addressing network transmission delays with a networked control system and an event-triggered communication scheme. Experimental results are presented to validate the performance of the IoT architecture proposed. The event-triggered scheme of the IoT solution is used to decrease data transmission and prevent network overload. Full article
(This article belongs to the Special Issue Intelligent Control and Active Safety Techniques for Road Vehicles)
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Review

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21 pages, 723 KiB  
Review
Model Predictive Control Used in Passenger Vehicles: An Overview
by Meaghan Charest-Finn and Shabnam Pejhan
Machines 2024, 12(11), 773; https://doi.org/10.3390/machines12110773 - 4 Nov 2024
Viewed by 549
Abstract
The following article presents a high-level overview of how Model Predictive Control (MPC) is leveraged in passenger vehicles and their subsystems for improved performance. This overview presents the fundamental concepts of MPC algorithms and their common variants. After building some understanding of MPC [...] Read more.
The following article presents a high-level overview of how Model Predictive Control (MPC) is leveraged in passenger vehicles and their subsystems for improved performance. This overview presents the fundamental concepts of MPC algorithms and their common variants. After building some understanding of MPC methods, the paper discusses state-of-the-art examples of how MPC methods are leveraged to perform low- to high-level tasks within a typical passenger vehicle. This review also aims to provide the reader with intuition in formulating MPC systems based on the strengths and weaknesses of the different formulations of MPC. The paper also highlights active areas of research and development. Full article
(This article belongs to the Special Issue Intelligent Control and Active Safety Techniques for Road Vehicles)
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