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Actuators
Special Issues
Autonomous Vehicles Impact on Roads and Control Strategies
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Autonomous Vehicles Impact on Roads and Control Strategies

A Special Issue of Actuators (ISSN 2076-0825) belonging to the section "Actuators for Surface Vehicles".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 3007

Editors

Dr. Stefano Coropulis

E-Mail Website
Guest Editor
Department of Civil, Environmental, Land, Construction and Chemistry, Politecnico di Bari, 70125 Bari, Italy
Interests: road safety; sustainable mobility; emerging technologies on roads; micromobility; traffic calming
Special Issues, Collections and Topics in MDPI journals
Dr. Md Abdus Samad Kamal

E-Mail Website
Guest Editor
Cluster of Electronics and Mechanical Engineering, Graduate School of Science and Technology, Gunma University, Maebashi, Japan
Interests: autonomous driving; intelligent transportation systems (ITS); model predictive control (MPC); reinforcement learning; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Introducing autonomous vehicles into the overall panorama of transportation systems raises several challenges and possibilities. Urban design can be modified according to moving vehicles that do not require parking spots, meaning that an increase in accessibility and equity for all the passengers can be reached, and different strategies can be deployed for sharing, pooling, and purchasing these vehicles, giving more space and safety to vulnerable users. In both urban and rural contexts, autonomous vehicles are believed to drastically reduce crash occurrences, especially in multi-vehicle scenarios. One of the main focuses of this Special Issue is the passive and active vehicle safety systems and actuators that are used for collision avoidance. In fact, to improve safety, the sensors must be accurately designed, preventing malfunctioning and misperceptions, for instance, under bad light or adverse weather conditions. In this regard, considering the aspects of the vehicle becomes extremly important. Therefore, research on autonomous vehicles is a complex task that requires examinations of multiple different aspects (safety, environment, planning, operation, equity, technology), but also requires different perspectives—on the one hand from the vehicle side, and on the other hand from the road-user side—accounting for regular and vulnerable road users. Tackling all of these aspects and different views is the main aim of this Special Issue. We also aim to promote new driving system solutions and collision-avoidance strategies based on actuators. This Special Issue will bring together original and high-quality articles through an international standard peer-review process. Topics of interest include, but are not limited to, the following:

  • Planning of autonomous vehicles;
  • ADAS and ADS;
  • Vehicle detection;
  • Vehicle interactions;
  • Sensor performance;
  • Active and passive vehicle safety systems.;
  • Dynamical modeling, control, and optimization of autonomous vehicles;
  • Cooperative and connected vehicle systems;
  • Intelligent transportation systems. 

Dr. Stefano Coropulis
Dr. Md Abdus Samad Kamal 
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Actuators 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
  • vehicle control strategies
  • remote operators
  • active and passive safety systems

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

Published Papers (2 papers)

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Research

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29 pages, 6307 KB  
Article
An Efficient and Lightweight Model for Traffic Object Detection in Autonomous Vehicles Under Nighttime Conditions
by Ruiyang Ou, Luyao Du, Wei Chen and Huiheng Liu
Actuators 2026, 15(6), 313; https://doi.org/10.3390/act15060313 - 2 Jun 2026
Viewed by 1071
Abstract
Traffic object detection based on camera sensors is a critical task for autonomous vehicles. However, in nighttime conditions with adverse lighting, several challenges arise: blurred object edges, large-scale variations, and complex lighting conditions involving both overexposure and underexposure. As a result, it remains [...] Read more.
Traffic object detection based on camera sensors is a critical task for autonomous vehicles. However, in nighttime conditions with adverse lighting, several challenges arise: blurred object edges, large-scale variations, and complex lighting conditions involving both overexposure and underexposure. As a result, it remains difficult for vision-based perception tasks to ensure reliable precision and rapid inference simultaneously. This paper proposes a novel, efficient, and lightweight vision module for detecting traffic objects in challenging nighttime environments, developed by enhancing the YOLOv8n architecture. Firstly, a bidirectional weighted feature fusion method (BiFPN) is incorporated in the path aggregation network, and an additional shallow P2 feature map is introduced to fully utilize key information from features at different scales. Then, the coordinate attention (CA) module is inserted between the end of the feature pyramid and the detection head to capture both semantic and spatial information of the object. Finally, the dynamic upsampler (DySample) is employed to guide the model in focusing on the detailed features of challenging samples, thereby balancing accuracy across different object categories. A subset of nighttime traffic scenes is curated from the BDD100K dataset for the evaluation of the proposed approach. The experiments demonstrate that, relative to the baseline, our method raises the mean average precision (mAP50) from 51.5% to 56.6%, achieves a 7.3% decrease in parameter quantity, and maintains a fast inference speed of 208 FPS. For the challenging bike and motorbike categories, notable improvements in detection accuracy are achieved. Compared with other advanced YOLO-series models such as YOLOv11, the proposed model also exhibits significant performance advantages with a 3.7% higher mAP50. Furthermore, our model demonstrates good generalization performance on the larger BDD100K nighttime partition. The findings confirm that our approach significantly improves detection accuracy without compromising real-time processing, highlighting its potential as a lightweight vision module providing reliable perceptual inputs for autonomous vehicle control and safety actuators in challenging nighttime scenarios. Full article
(This article belongs to the Special Issue Autonomous Vehicles Impact on Roads and Control Strategies)
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Review

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41 pages, 2311 KB  
Review
A Comprehensive Review of End-to-End Autonomous Driving: Architectures and Emerging Trends
by Yunxing Chen, Guo Yu, Pengfei Ran and Zhijun Chen
Actuators 2026, 15(8), 427; https://doi.org/10.3390/act15080427 - 6 Aug 2026
Viewed by 1289
Abstract
End-to-end autonomous driving is an emerging technology and a prominent research focus in both industry and academia. By integrating perception, localization, decision-making, and control into a single model, end-to-end systems aim to streamline the traditional modular pipeline while introducing new challenges in safety [...] Read more.
End-to-end autonomous driving is an emerging technology and a prominent research focus in both industry and academia. By integrating perception, localization, decision-making, and control into a single model, end-to-end systems aim to streamline the traditional modular pipeline while introducing new challenges in safety validation and interpretability. Unlike existing surveys that predominantly catalog algorithms, this review proposes a novel function-oriented taxonomy by categorizing architectures into perception-integrated and planning-integrated paradigms. Beyond the architectural dimension, the analysis delves into critical safety and interpretability, emphasizing the fundamental gap between theoretical design and the reliability required for real-world deployment. Industrial applicability is examined through real-world examples of data closed-loop workflows and simulation testing, addressing practical constraints in latency and computing resources. Finally, the review addresses critical challenges, particularly long-tail data scarcity and the deficiency in human-like decision-making and outlines future directions toward achieving robust autonomy. Full article
(This article belongs to the Special Issue Autonomous Vehicles Impact on Roads and Control Strategies)
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