Intelligent Mobility and Sustainable Automotive Technologies

1. Introduction

The automotive industry is undergoing a profound transformation driven by decarbonization objectives, digitalization, automation, connectivity, and the demand for safe and sustainable transportation systems. Modern transportation is evolving into a complex ecosystem in which vehicles, infrastructure, users, energy systems, and digital services interact in real time [1,2,3,4,5]. In this context, electrification, automation, artificial intelligence, advanced manufacturing technologies, and alternative propulsion systems are no longer independent research directions, but interdependent elements of modern mobility systems [6,7,8,9,10].

The Special Issue “Intelligent Mobility and Sustainable Automotive Technologies” was launched with the objective of gathering recent research contributions addressing the scientific and engineering challenges associated with modern mobility.

Associated with the AMMA 2025 Congress (Automotive Mobility, Management and Automation), this Special Issue brings together contributions from researchers, engineers, and specialists from academia, industry, and research institutions. The result is a collection of papers reflecting the diversity and complexity of contemporary automotive research, covering intelligent vehicle systems, advanced control strategies, alternative propulsion technologies, digital engineering methodologies, and emerging transportation concepts.

The published articles address several aspects of the transition toward a future characterized by low- and zero-emission mobility, data-driven decision-making processes, and automated driving functions. Furthermore, they demonstrate that achieving such ambitious goals depends on the integration of expertise from multiple disciplines, including mechanical engineering, electrical and electronic systems, computer science, artificial intelligence, materials engineering, energy systems, and transportation sciences. This interdisciplinary collaboration is essential for addressing the technological, environmental, economic, and societal challenges associated with the mobility of the future.

The findings presented in the included contributions also point to several important trends in automotive research and development: the accelerated adoption of electrified propulsion systems, the growing importance of the Software-Defined Vehicle (SDV) paradigm, the expansion of connected and cooperative mobility ecosystems, the increasing importance of cybersecurity and functional safety, and the need for sustainable manufacturing and lifecycle-oriented design approaches [11,12,13,14].

2. Overview of Contributions

The papers featured in this Special Issue address complementary aspects of intelligent mobility and sustainable automotive engineering. This section presents an overview of the fourteen published contributions, focusing on their objectives, methodological approaches, main findings, contributions, and relevance to the scope of the present issue. Collectively, the papers address various topics of intelligent mobility and sustainable automotive technologies, such as vehicle perception and control, electrified propulsion, emissions reduction, digital engineering, reliability, road safety, traffic systems, and transport optimization.

Csato et al. [15] investigated the influence of image resolution on traffic lane detection performance for Advanced Driver Assistance Systems (ADASs) based on a combination of synthetic data generated in the CARLA simulation environment and real-world data from the TuSimple dataset. Using a U-Net convolutional neural network for image segmentation, the authors evaluated the trade-off between detection accuracy and computational efficiency across different image resolutions. Their results indicated that a resolution of 512 × 256 offers an effective balance between segmentation performance and real-time processing capability. The study also supports the use of combined synthetic and real datasets for training robust lane detection models in autonomous driving applications.

Pusztai et al. [16] proposed and evaluated a Linear Time-Varying Linear Quadratic Gaussian (LTV-LQG) control strategy for energy-efficient electric vehicles operating under realistic driving conditions. Based on a nonlinear vehicle model and a predefined near-optimal driving trajectory, the authors developed a time-varying controller incorporating a Kalman filter to estimate external disturbances such as wind. The simulation results showed that the proposed approach satisfied travel-time constraints and reduced energy consumption compared with a conventional rule-based driving strategy, particularly under favorable wind conditions.

Antonya et al. [17] addressed the integration of sustainable public transportation into historic urban centers characterized by narrow streets, heritage preservation constraints, and limited infrastructure adaptability. The authors proposed the use of autonomous electric buses and developed a vehicle dynamics and optimization framework that incorporates mobility demand, road network geometry, and vehicle energy consumption. For a set of operational scenarios, the study identified vehicle characteristics that balance energy efficiency with passenger service quality. The proposed methodology provides a decision-support framework for planning autonomous transit solutions in urban environments with multiple constraints.

Stoica et al. [18] studied the condition monitoring of rolling bearings using robust linear regression techniques applied to vibration-based statistical indicators, including RMS, skewness, kurtosis, and crest factor. Experimental data collected under different alignment and rotational speed conditions were analyzed to assess the relationship between operating parameters and vibration behavior. Although bearing misalignment was associated with a slight increase in vibration levels, the statistical analysis indicated no significant influence of either rotational speed or alignment on the measured acceleration signals. The study indicates that robust data analysis methods and appropriate feature selection can improve the reliability of condition monitoring systems in rotating machinery, which makes it relevant to predictive maintenance and reliability assessment in automotive systems.

Stirosu et al. [19] investigated the role of digital transformation in the manufacturing and validation of stamped automotive components. The study combined material testing, LS-DYNA simulations, non-contact dimensional analysis, and digital workflow modeling to evaluate the accuracy and reliability of modern manufacturing processes. The strong agreement between experimental and simulation data confirmed the usefulness of the digital twin method for improving product quality and reducing development time in automotive component design and manufacturing.

Geonea et al. [20] presented an integrated numerical–experimental methodology for assessing and optimizing the durability of a passenger-car rear axle. The authors combined a dedicated suspension test bench with multibody dynamics and a finite element model to evaluate structural behavior under realistic operating conditions. Experimental strain measurements were used to validate the numerical models, allowing the identification of critical stress regions and supporting the optimization of safety-critical suspension components, thereby contributing to durability-oriented vehicle design.

Tarulescu et al. [21] proposed and experimentally evaluated a filtration system designed to reduce CO₂ emissions from spark-ignition engine exhaust gases. The system, based on a reactive aqueous solution containing water, CaO, and MgO, was tested under different operating configurations on a gasoline-powered vehicle. The results showed a measurable reduction in CO₂ emissions without increasing other major pollutants, indicating the potential relevance of the proposed approach for internal combustion engines used in both vehicles and stationary applications. The contribution is aligned with ongoing efforts to mitigate the environmental impact of existing vehicle fleets.

Sarsembekov et al. [22] investigated a hybrid exhaust gas treatment system combining ultrasonic waves and infrared laser irradiation to reduce gasoline engine emissions. Through experimental testing under different operating conditions, the authors assessed the individual and combined effects of ultrasound and laser exposure on exhaust gas composition. The results demonstrated significant reductions in CO and unburned hydrocarbon emissions, with the combined treatment leading to the lowest emission values. The results suggest that physical exhaust treatment technologies could complement existing emission-control solutions to reduce the environmental impact of internal combustion engines.

Butilă et al. [23] presented a comprehensive review of the integration of connected and autonomous vehicles in mixed traffic environments. The review considered implications for road safety, traffic efficiency, infrastructure requirements, urban mobility, and policy development. The authors concluded that the benefits of automation depend strongly on market penetration rates, control strategies, and human behavioral adaptation, noting that early deployment stages may generate new operational challenges. The paper underscores the importance of appropriate regulatory frameworks and integrated mobility policies to ensure that autonomous vehicles contribute to safe, efficient, and sustainable transportation systems.

Filip et al. [24] investigated the impact of COVID-19 mobility restrictions on urban traffic patterns using a combination of radar-based traffic monitoring and AI-assisted video analysis at a signalized intersection in Cluj-Napoca, Romania. By comparing traffic conditions before and during the lockdown period, the authors developed a probabilistic traffic flow model based on the Poisson distribution to characterize vehicle arrival patterns. The results showed a notable reduction in traffic demand and confirmed the predominance of free-flow conditions under low-density scenarios. The study provides insights into urban traffic behavior during extreme mobility disruptions and offers a framework that may support data-driven traffic management and resilient urban mobility planning.

Lupu et al. [25] performed a system-level assessment of permanent magnet synchronous motor rotor topologies for battery electric vehicles operating under the WLTP driving cycle. Using detailed energy-based models of the traction motor, inverter, and battery, the authors evaluated the influence of rotor design and regenerative braking on overall vehicle efficiency and driving range. The results showed that rotor topology has a significant impact on energy consumption, with the dual-layer interior permanent magnet configuration providing improved efficiency and extended driving range compared with a conventional design. The authors emphasize the importance of considering realistic operating conditions when optimizing electric powertrain components for next-generation battery electric vehicles.

Tudor et al. [26] conducted a large-scale bibliometric and topic-modeling analysis of road safety research published between 2016 and 2025 to identify emerging trends in the field. Using a transformer-based BERTopic framework, the authors analyzed more than 15,000 scientific publications and identified key research themes related to crash severity, human factors, vulnerable road users, artificial intelligence applications, and spatial safety analysis. The results indicate a growing emphasis on predictive and AI-supported approaches to road safety within intelligent transportation systems, reflecting a shift from traditional post-crash investigations toward proactive and data-driven risk assessment. The study also provides an overview of the evolving road safety landscape as well as the opportunities and challenges associated with the adoption of advanced analytical technologies in intelligent transportation systems.

Stanciuc-Otat et al. [27] investigated occupant injury risk in full-overlap frontal collisions through a combined experimental and numerical analysis. Using FMVSS 208 crash tests with Hybrid III anthropomorphic test devices and complementary LS-DYNA simulations, the authors evaluated the influence of impact speed, seat belt use, and occupant anthropometry on injury outcomes. The results identified the parameters with the greatest effect on occupant safety and highlighted the importance of physical crash testing for validating injury assessment methodologies. The study also provides insights relevant to the development of safer vehicle restraint systems and broader road safety objectives.

Racila et al. [28] presented an algorithmic framework for the classification of constrained extrema in low-dimensional optimization problems, with applications in transport and logistics location planning. The proposed method replaces the explicit manipulation of constraint differentials with algebraic test coefficients derived from the Lagrangian formulation. Its application to a transport depot location problem demonstrates its potential as a transparent and computationally efficient decision-support tool for transport and logistics infrastructure planning.

3. Future Directions

The contributions presented in this Special Issue address several challenges central to the development of intelligent and sustainable mobility. Collectively, the published papers highlight the growing importance of data-driven methodologies, advanced simulation environments, intelligent control systems, and sustainable propulsion technologies in the development of next-generation vehicles.

Future research is likely to focus increasingly on software-defined vehicles, artificial intelligence and machine learning applications, cybersecurity and functional safety, digital twins and virtual validation platforms, connected and cooperative mobility, and advanced electrified and hydrogen-based propulsion systems.

Equally important will be the development and adoption of holistic approaches that integrate technological innovation with sustainability, economic viability, regulatory compliance, and societal acceptance.

The articles gathered in this Special Issue provide a useful basis for further research and collaboration aimed at developing safer, smarter, and more sustainable mobility solutions.

The launch of “Intelligent Mobility and Sustainable Automotive Technologies, 2nd Edition” continues this thematic focus and provides a further venue for research in intelligent and sustainable mobility.