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A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 10855

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Special Issue Editors

Dr. Nicolae Vlad Burnete

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Guest Editor

Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania
Interests: internal combustion engines; renewable and green energy; thermoelectric generators; mobility

Prof. Dr. Florin Mariasiu

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Guest Editor

Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania
Interests: Li-ion batteries; electric vehicles; means of transportation; renewable and green energy; automotive; transport; pollutants
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Dr. Calin Iclodean

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Guest Editor

Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania
Interests: electric vehicles; fuel cell vehicles; powertrain concept; electronic control unit; in-vehicle communication network; energy efficiency; autonomous vehicles; computer modeling and simulation in the automotive field
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Vehicles is dedicated to disseminating the high-quality research presented at the AMMA 2025 Congress, as well as contributions from the broader automotive engineering community. Encompassing all congress topics, it reflects the multidisciplinary character of modern automotive research and development.

The Automotive Mobility, Management and Automation (AMMA 2025) Congress—organized by the Romanian Society of Automotive Engineers (SIAR) and hosted by the Technical University of Cluj-Napoca, Romania on October 23–25, 2025—brings together leading experts, researchers, and practitioners from academia, industry, and government. AMMA 2025 continues the tradition of showcasing cutting-edge research, technological advancements, and forward-thinking strategies in the automotive sector.

The congress covers a broad spectrum of topics, including but not limited to the following:

Advanced engineering, software, and simulation
Cyber–physical systems in automotive
Green vehicle solutions
Hybrid and electric vehicles
Manufacturing technologies and materials
Powertrain and propulsion
Road safety, traffic management, and transportation engineering

AMMA 2025 provides an excellent platform for participants to present scientific and technical papers, exchange ideas, and forge new collaborations. Beyond the technical sessions, keynote presentations, and workshops, the event will feature an industry exhibition showcasing novel products, solutions, and services. Attendees, including students, will have opportunities to network with potential partners and engage with industry leaders, fostering a deeper understanding of the future trends shaping automotive mobility and automation.

This Special Issue welcomes original research papers and review articles that address advancements and emerging challenges in the following areas:

Autonomous and Connected Vehicles: AI-driven automation, V2X communication, and cyber-physical vehicle systems.
Hybrid and Electric Vehicles: Design, control systems, battery management, infrastructure, and energy optimization for hybrid and full-electric vehicles.
Manufacturing Technologies and Materials: New production methods, additive manufacturing, lightweight materials, and sustainable fabrication processes.
Powertrain and Propulsion: Engine technologies, alternative fuels, and propulsion systems that enhance efficiency and performance.

Expanded and high-quality conference papers can be considered if the paper has been expanded to the size of a research article and has not undergone peer review.

The objective of this Special Issue is to provide a comprehensive view of the scientific and technological developments shaping future vehicles and mobility solutions. Submissions that bridge the gap between theoretical foundations and industrial applications are particularly encouraged. By bringing together diverse perspectives and expertise, this Special Issue aims to foster collaboration and drive forward the vision of a safer, cleaner, and more efficient automotive world. We look forward to receiving your contributions and thank you for helping shape the future of automotive mobility and automation.

Dr. Nicolae Vlad Burnete
Prof. Dr. Florin Mariasiu
Dr. Calin Iclodean
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. Vehicles 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 1800 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

automotive engineering
advanced simulation and modeling
cyber–physical systems
hybrid and electric vehicles
powertrain and propulsion
manufacturing technologies
advanced materials
green vehicle solutions
road safety and traffic management
transportation engineering

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Published Papers (12 papers)

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26 pages, 2135 KB

Open AccessArticle

Mapping Research Trends in Road Safety: A Topic Modeling Perspective

by Iulius Alexandru Tudor and Florin Gîrbacia

Vehicles 2026, 8(4), 69; https://doi.org/10.3390/vehicles8040069 - 27 Mar 2026

Abstract

Over the past decade, road safety research has experienced rapid development due to the rapid expansion of large crash databases, the adoption of artificial intelligence techniques, and the demand for proactive and predictive safety solutions. This study conducts a data-driven review of recent research trends in transport safety. It focuses on main domains including crash severity analysis, human factors, vulnerable road users (VRUs), spatial modeling, and artificial intelligence applications. A systematic search of the Scopus database identified 15,599 relevant scientific papers published between 2016 and 2025. After constructing this corpus, titles, abstracts, and keywords were preprocessed using a natural language pipeline. The analysis employed BERTopic, a transformer-based topic modeling framework. The analysis identified 29 distinct research topics, further synthesized into five major thematic areas: (1) crash severity and injury analysis, (2) driver behavior and human factors, (3) vulnerable road users, (4) artificial intelligence, machine learning, and computer vision in intelligent transportation systems, and (5) spatial analysis and hotspot detection. A notable increase in publications related to artificial intelligence and machine learning has been evident since 2020. The results show a transition from descriptive, post-crash studies to integrated, multimodal, predictive analysis. Overall, the findings reveal a paradigm shift in the field. This study also identifies ethical and economic issues associated with the use of artificial intelligence in intelligent transportation systems, including data management, infrastructure requirements, system security, and model transparency. The results signify a transition from intuition-based models to explainable, spatially explicit, and data-intensive models, ultimately facilitating proactive risk assessment and informed decision-making. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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34 pages, 8241 KB

Open AccessArticle

System-Level Comparative Assessment of PMSM Rotor Topologies in Battery Electric Vehicles Under the WLTP Driving Cycle

by Elena-Daniela Lupu and Ștefan Lucian Tabacu

Vehicles 2026, 8(3), 66; https://doi.org/10.3390/vehicles8030066 - 20 Mar 2026

Viewed by 204

Abstract

Environmental regulations, rapid technological advancements, and evolving mobility trends have led to a significant transformation of the automotive industry in recent years. The adoption of battery-electric vehicles (BEVs) has been accelerated by these developments, which are becoming increasingly efficient and widely deployed. Evaluating BEV energy consumption and performance is essential for optimizing energy efficiency, extending driving range, and developing effective control strategies under real-world operating conditions. The analysis is based on the WLTP Class 3 driving cycle, in which the vehicle operating points are projected onto the motor efficiency map to evaluate the influence of real-world operating conditions on overall propulsion efficiency. Two operating scenarios are considered: with regenerative braking and without regenerative braking. The inverter and battery are modeled using quasi-static energy-based representations to ensure system-level energetic consistency while maintaining computational efficiency. The results show that rotor topology significantly influences vehicle-level energy consumption. The dual-layer IPM configuration reduces net WLTP energy demand by approximately 9% and increases the estimated driving range from about 489 km to 535 km compared to the single-layer V-shaped configuration. Variations in rotor topology led to different efficiency distributions, which leads to systematic differences in battery energy demand and achievable driving range. The results highlight the importance of aligning traction motor design with realistic operating-point distributions rather than optimizing solely for peak efficiency or marginal improvements in regenerative braking performance. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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33 pages, 8047 KB

Open AccessArticle

Probabilistic Modeling of Urban Vehicle Traffic Under COVID-19 Mobility Restrictions Using AI-Based Video Data: A Case Study in Cluj-Napoca

by Nicolae Filip, Calin Iclodean and Marius Deac

Vehicles 2026, 8(3), 59; https://doi.org/10.3390/vehicles8030059 - 15 Mar 2026

Viewed by 151

Abstract

The COVID-19 pandemic and the resulting mobility restrictions significantly disrupted urban traffic patterns. This study quantitatively assesses the impact of these restrictions on vehicle flow at a signalized central intersection in Cluj-Napoca, Romania, through an integrated methodology combining continuous radar-based traffic measurements and AI (Artificial Intelligence)-assisted video analysis. Traffic data were collected before the pandemic (November 2019) and during the lockdown period (April 2020), enabling a comparative evaluation of flow characteristics and vehicle arrival patterns. Under constrained observational conditions, vehicle arrivals were modeled using a probabilistic framework grounded in Poisson distribution. The findings indicate a dramatic contraction of mobility demand, with traffic volumes declining in 2020 to 9.55% of pre-pandemic levels. The probabilistic assessment highlights the predominance of free-flow regimes under reduced demand and confirms the adequacy of the Poisson model in low-density traffic scenarios. The obtained results contribute to a better understanding of urban traffic dynamics under extreme mobility disruptions and provide a transferable methodological framework for probabilistic traffic modeling, resilience-oriented urban mobility planning, and data-driven traffic management. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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17 pages, 1796 KB

Open AccessArticle

Ultrasonic–Laser Hybrid Treatment for Cleaning Gasoline Engine Exhaust: An Experimental Study

by Bauyrzhan Sarsembekov, Madi Issabayev, Nursultan Zharkenov, Altynbek Kaukarov, Isatai Utebayev, Akhmet Murzagaliyev and Baurzhan Zhamanbayev

Vehicles 2026, 8(1), 22; https://doi.org/10.3390/vehicles8010022 - 20 Jan 2026

Viewed by 1018

Abstract

Vehicle exhaust gases remain one of the key sources of atmospheric air pollution and pose a serious threat to ecosystems and public health. This study presents an experimental investigation into reducing the toxicity of gasoline internal combustion engine exhaust using ultrasonic waves and infrared (IR) laser exposure. An original hybrid system integrating an ultrasonic emitter and an IR laser module was developed. Four operating modes were examined: no treatment, ultrasound only, laser only, and combined ultrasound–laser treatment. The concentrations of CH, CO, CO₂, and O₂, as well as exhaust gas temperature, were measured at idle and under operating engine speeds. The experimental results show that ultrasound provides a substantial reduction in CO concentration (up to 40%), while IR laser exposure effectively decreases unburned hydrocarbons CH (by 35–40%). The combined treatment produces a synergistic effect, reducing CH and CO by 38% and 43%, respectively, while increasing the CO₂ fraction and decreasing O₂ content, indicating more complete post-oxidation of combustion products. The underlying physical mechanisms responsible for the purification were identified as acoustic coagulation of particulates, oxidation, and photodissociation of harmful molecules. The findings support the hypothesis that combined ultrasonic and laser treatment can enhance real-time exhaust gas purification efficiency. It is demonstrated that physical treatment of the gas phase not only lowers the persistence of by-products but also promotes more complete oxidation processes within the flow. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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20 pages, 2472 KB

Open AccessArticle

Filtration System for Reducing CO₂ Concentration from Combustion Gases of Used Spark Ignition Engines

by Radu Tarulescu, Stelian Tarulescu, Razvan Gabriel Boboc and Mircea Nastasoiu

Vehicles 2026, 8(1), 19; https://doi.org/10.3390/vehicles8010019 - 15 Jan 2026

Viewed by 371

Abstract

This research paper proposes a solution to reduce CO₂ emissions from a spark ignition engine’s exhaust gases by installing a filtration system on the vehicle’s exhaust pipe. The analyzed filtration system was not patented and was in the testing stage. Tests will also be carried out on the stand. The tested system can be used to reduce CO₂ levels in automotive exhaust gases and for static applications (generators, internal combustion engine test stands, fossil fuel power generation systems). The need for a system to reduce pollutant emissions emerged with the average age in Europe. In proper conditions, some vehicles can use this type of filtration system. The tested vehicle is a vehicle (produced in 2009) equipped with a 75HP Spark Ignition Engine. The CO₂ filtration system consists of a container containing a reactive aqueous solution comprising water, CaO, and MgO. Four tests were performed: the first without a filter, and the other three with the filter placed at different distances from the exhaust pipe end to the reactive solution surface. The tests consisted of evaluating the exhaust gases from the cold start of the engine and running (idle engine speed) until the engine reached the optimal operating temperature. The test procedure involved saving the data collected by the analyzer every 10 s for each of the four tests performed (the duration of a test was 1050 s). The first test (No. 1) was performed without the use of the filtering system. Tests 2, 3, and 4 were carried out using the filtering system and changing the distance between the exhaust gases’ outlet point and the surface of the aqueous substance. All tests were carried out under similar conditions. Data specific to the test of engines were collected—emissions (CO₂, CO, NO_x), ambient temperature, and exhaust temperature. The tests were analyzed and compared, and the highest CO₂ reductions without increases in CO or NO_x were observed in Tests 3 and 4. Based on the detailed analysis of the values obtained from the four tests, the system was efficient. The tests will continue on experimental engines from test stands, to develop a prototype filter for primarily static applications with internal combustion engines: test stands for engines and generators, and, after homologation, directly on vehicles. The paper aims to partially solve an important problem—reducing the level of CO₂ from the exhaust gases. The presented solution may have applicability in the automotive industry but is also feasible for static applications. Another objective is to reduce emissions from older vehicles, which are widespread in certain regions of Europe and worldwide. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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39 pages, 17546 KB

Open AccessArticle

Dynamic Finite Element and Experimental Strain Analysis of a Passenger-Car Rear Axle for Durable and Sustainable Suspension Design

by Ionut Daniel Geonea, Ilie Dumitru, Laurentiu Racila and Cristian Copilusi

Vehicles 2026, 8(1), 9; https://doi.org/10.3390/vehicles8010009 - 3 Jan 2026

Viewed by 999

Abstract

This paper proposes an integrated numerical–experimental methodology for the durability assessment and optimisation of a passenger-car rear axle. A dedicated rear-suspension durability test bench was designed to impose a controlled cyclic vertical excitation on a dependent axle, reproducing service-like translational and rotational amplitudes of the beam and stabiliser bar. A detailed flexible multibody model of the bench–axle system was developed in MSC ADAMS 2023 and used to tune the kinematic excitation and determine an equivalent design load at the wheel spindles, consistent with the stiffness of the suspension assembly. Experimental strain measurements at nine locations on the axle, acquired with strain-gauge instrumentation on the bench, were converted into stresses and used to validate an explicit dynamic finite element model in ANSYS. The FE predictions agree with the experiments within about 10% at the beam mid-span and correctly identify a critical region at the junction between the side plate and the arm, where peak von Mises stresses of about 104 MPa occur. The validated model then supports a response-surface-based optimisation of the safety-critical wheel spindle, yielding an optimised geometry in which spindle-fillet stresses remain around 180–185 MPa under a severe loading case corresponding to the maximum admissible wheel load at the bearings, while the associated increase in mass is modest and compatible with practical design constraints. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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25 pages, 7504 KB

Open AccessArticle

Investigation on the Manufacturing, Testing, and Simulation Processes of the Hood Hinge Assembly

by Mihai Stirosu, Stefan Tabacu and Gabriel Cimpeanu

Vehicles 2025, 7(4), 157; https://doi.org/10.3390/vehicles7040157 - 8 Dec 2025

Cited by 1 | Viewed by 785

Abstract

The automotive industry is currently undergoing significant transformations driven by challenges such as fierce competition, supply chain disruptions, and stringent legislative regulations aimed at reducing pollutant emissions. The research employs a combination of theoretical analysis and numerical modeling to investigate the manufacturing processes of stamped automotive components. Data collection methods include experimental testing of materials, LS-DYNA simulations, and non-contact scanning for dimensional analysis. The study also utilizes a workflow diagram to illustrate the various phases involved in the design and validation of automotive assemblies. The findings detail the critical role of digital transformation in the automotive industry, particularly in enhancing the accuracy and reliability of manufacturing processes. Implementing digital twins improves product quality and reduces product development time. The experimental results were compared with simulation data, and a good correlation was identified, showing, for the numerical model with complete history (thickness and stress), a difference of

1.6 %

. Furthermore, to simplify the process of developing the numerical models for the initial iterations, a scale factor of

~ 1.1

is proposed for the testing load. This factor is not limited to the current design, as the manufacturing stages are similar for this range of products. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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16 pages, 2340 KB

Open AccessArticle

Investigation of Bearing Condition by Means of Robust Linear Regression and Informative Predictors

by Ramona-Monica Stoica, Daniela Voicu and Radu Vilău

Vehicles 2025, 7(4), 127; https://doi.org/10.3390/vehicles7040127 - 2 Nov 2025

Viewed by 542

Abstract

This study addresses the condition monitoring of rolling bearings by applying robust linear regression to statistically derived features from vibration data. Four datasets of acceleration signals were collected under varying operating conditions: aligned and misaligned bearings at rotational speeds of 1000 rpm and 1500 rpm. From each signal, key statistical indicators were extracted, including root mean square (RMS), skewness, kurtosis and crest factor, to capture signal characteristics that were relevant to fault detection. To follow-up, we applied the Kolmogorov–Smirnov test to assess data normality and the results confirmed significant deviations from a Gaussian distribution, motivating the use of robust regression techniques for further investigations. The regression model created incorporated rotational speed and alignment conditions as predictors of acceleration and the results indicated that while the coefficient associated with misalignment suggested a possible increase in acceleration (~1.115 units), statistical testing (p = 0.5233) indicated that neither speed nor alignment had a significant influence on the measured vibration levels within the dataset. The findings suggest that under the tested conditions, misalignment does not manifest as a strong linear change in acceleration magnitude, and the study underscores the importance of robust modeling techniques and feature selection in the condition monitoring of rotating machinery. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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37 pages, 6550 KB

Open AccessArticle

Defining the Optimal Characteristics of Autonomous Vehicles for Public Passenger Transport in European Cities with Constrained Urban Spaces

by Csaba Antonya, Radu Tarulescu, Stelian Tarulescu and Silviu Butnariu

Vehicles 2025, 7(4), 125; https://doi.org/10.3390/vehicles7040125 - 29 Oct 2025

Cited by 1 | Viewed by 1289

Abstract

This research addresses the complex challenge of integrating modern public transport into historic medieval city centers. These unique urban environments are characterized by narrow streets, protected heritage status, and topographical constraints, which are incompatible with conventional transit vehicles. The introduction of standard bus routes often aggravates traffic congestion and fails to meet the specific mobility needs of residents and visitors. This paper suggests that autonomous electric buses represent a viable and sustainable solution, capable of navigating these constrained environments while aligning with modern energy efficiency goals. The central challenge lies in the optimal selection of an autonomous electric bus that can operate safely and efficiently within the tight streets of historic city centers while satisfying the travel demands of passengers. To address this, a comprehensive study was conducted, analyzing resident mobility patterns—including key routes and hourly passenger loads—and the specific geometric constraints of the road network. Based on this empirical data, a vehicle dynamics model was developed in Matlab^®. This model simulates various operational scenarios by calculating the instantaneous forces (rolling resistance, aerodynamic drag, inertial forces) and the corresponding power required for different electric bus configurations to follow pre-established speed profiles. The core of this research is an optimization analysis, designed to identify the balance between minimizing total energy consumption and maximizing the quality of passenger service. The findings provide a quantitative framework and clear procedures for urban planners to select the most suitable autonomous transit system, ensuring that the chosen solution enhances mobility and accessibility without compromising the unique character of historic cities. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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20 pages, 4269 KB

Open AccessArticle

LTV-LQG Control for an Energy Efficient Electric Vehicle

by Zoltán Pusztai, Tamás Gábor Luspay and Ferenc Friedler

Vehicles 2025, 7(4), 113; https://doi.org/10.3390/vehicles7040113 - 2 Oct 2025

Cited by 1 | Viewed by 1199

Abstract

This paper presents the design and evaluation of a Linear Time-Varying Linear Quadratic Gaussian (LTV-LQG) controller for an energy efficient electric vehicle, using a predetermined driving strategy as the reference trajectory. The proposed approach begins with the development of a structured nonlinear vehicle model based on relevant subsystems, enabling accurate energy consumption estimation with a deviation of less than 2% from experimental measurements. This model serves as the basis for computing a near-optimal driving trajectory. The nonlinear model is linearized along the predefined trajectory to support control design. A time-varying control structure is then developed, integrating a Kalman filter that estimates unmeasured external disturbances, such as wind, and enhances feedback performance. The proposed control strategy is evaluated through simulations and compared to a rule-based switching controller that replicates human-like driving behavior. The simulation results demonstrate that the LTV-LQG controller consistently satisfies the time constraints in both headwind- and tailwind-dominant scenarios, where the switching controller tends to exceed the time limit. Moreover, in tailwind-dominant cases, the LTV-LQG controller achieves lower energy consumption (up to 15.4%). The proposed framework represents a computationally efficient and practically feasible control solution for electric vehicles operating under realistic disturbance conditions. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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13 pages, 2716 KB

Open AccessArticle

Analysis of the Influence of Image Resolution in Traffic Lane Detection Using the CARLA Simulation Environment

by Aron Csato, Florin Mariasiu and Gergely Csiki

Vehicles 2025, 7(2), 60; https://doi.org/10.3390/vehicles7020060 - 16 Jun 2025

Cited by 1 | Viewed by 1450

Abstract

Computer vision is one of the key technologies of advanced driver assistance systems (ADAS), but the incorporation of a vision-based driver assistance system (still) poses a great challenge due to the special characteristics of the algorithms, the neural network architecture, the constraints, and the strict hardware/software requirements that need to be met. The aim of this study is to show the influence of image resolution in traffic lane detection using a virtual dataset from virtual simulation environment (CARLA) combined with a real dataset (TuSimple), considering four performance parameters: Mean Intersection over Union (mIoU), F1 precision score, Inference time, and processed frames per second (FPS). By using a convolutional neural network (U-Net) specifically designed for image segmentation tasks, the impact of different input image resolutions (512 × 256, 640 × 320, and 1024 × 512) on the efficiency of traffic line detection and on computational efficiency was analyzed and presented. Results indicate that a resolution of 512 × 256 yields the best trade-off, offering high mIoU and F1 scores while maintaining real-time processing speeds on a standard CPU. A key contribution of this work is the demonstration that combining synthetic and real datasets enhances model performance, especially when real data is limited. The novelty of this study lies in its dual analysis of simulation-based data and image resolution as key factors in training effective lane detection systems. These findings support the use of synthetic environments in training neural networks for autonomous driving applications. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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37 pages, 3062 KB

Open AccessSystematic Review

Autonomous Vehicles in the Traffic Ecosystem: A Comprehensive Review of Integration, Impacts, and Policy Implications

by Eugen Valentin Butilă, Gheorghe-Daniel Voinea, Răzvan Gabriel Boboc and Grigore Ambrosi

Vehicles 2026, 8(2), 41; https://doi.org/10.3390/vehicles8020041 - 19 Feb 2026

Viewed by 1074

Abstract

Autonomous vehicles (AVs) are expected to significantly influence road safety, traffic efficiency, and urban mobility. However, their real-world impacts depend not only on vehicle-level automation but also on interactions within the broader traffic ecosystem, including human-driven vehicles, vulnerable road users, infrastructure, and governance frameworks. This review provides a system-level synthesis of recent research on the integration of autonomous and connected autonomous vehicles in mixed traffic environments. Following PRISMA 2020 guidelines, 51 peer-reviewed studies published between 2016 and 2025 were systematically reviewed and thematically analyzed. The review addresses technological foundations, safety impacts, traffic flow and network performance, mixed traffic dynamics, infrastructure and urban systems, and policy and governance challenges. The findings indicate that AV impacts are highly non-linear and sensitive to market penetration rates, control strategies, and human behavioral adaptation. While high levels of automation and connectivity can improve safety, capacity, and traffic stability, early-stage deployment may temporarily increase delays and traffic conflicts. Policy measures—such as pricing, shared mobility integration, and regulatory oversight—are therefore critical to ensuring that AV deployment delivers sustainable and equitable mobility outcomes. Full article

(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)

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