Improving the ride comfort of commercial vehicles is crucial for driver health and operational safety. This study focuses on optimizing the parameters of a cab suspension system to improve its vibration isolation performance. Initially, nonlinear fitting was applied to experimental data characterizing air spring stiffness and damping, which informed the development of a multi-body rigid-flexible coupled dynamic model of the suspension system; its dynamic characteristics were subsequently validated through modal analysis. Road excitation data, filtered through the chassis suspension, were collected during vehicle testing, and displacement excitations for ride comfort simulation were reconstructed using virtual iteration technology. Thereafter, an integrated ISIGHT platform, combining ADAMS and MATLAB, was employed to systematically optimize suspension parameters and key bushing stiffness via a multi-island genetic algorithm. The optimization results demonstrated significant performance improvements: on General roads, the overall weighted root-mean-square acceleration was markedly reduced with enhanced isolation efficiency; on Belgian pave roads, resonance in the cab’s X-axis direction was effectively suppressed; and on Cobblestone roads, the pitch angle was successfully constrained within the design limit. This research provides an effective parameter matching methodology for performance optimization of cab suspension systems.

Autonomous vehicles (AVs) are expected to operate safely and efficiently in complex urban environments characterized by dynamic and uncertain elements such as pedestrians, cyclists and adverse weather. Although current neural network-based decision-making algorithms, fuzzy logic and reinforcement learning have shown promise, they often struggle to handle ambiguous situations, such as partially hidden road signs or unpredictable human behavior. This paper proposes a new hybrid decision-making framework combining multi-agent reinforcement learning (MARL) and explainable artificial intelligence (XAI) to improve robustness, adaptability and transparency. Each agent of the MARL architecture is specialized in a specific sub-task (e.g., obstacle avoidance, trajectory planning, intention prediction), enabling modular and cooperative learning. XAI techniques are integrated to provide interpretable rationales for decisions, facilitating human understanding and regulatory compliance. The proposed system will be validated using CARLA simulator, combined with reference data, to demonstrate improved performance in safety-critical and ambiguous driving scenarios.

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.

This paper details the development of an embedded system for vehicle data acquisition using the On-Board Diagnostics version 2 (OBD2) protocol, with the objective of predicting power loss caused by exhaust gas backpressure (EBP). The system decodes and preprocesses vehicle data for subsequent analysis using predictive artificial intelligence algorithms. MATLAB’s 2023b Powertrain Blockset, along with the pre-built “Compression Ignition Dynamometer Reference Application (CIDynoRefApp)” model, was used to simulate engine behavior and its subsystems. This model facilitated the control of various engine subsystems and enabled simulation of dynamic environmental factors, including wind. Manipulation of the exhaust backpressure orifice revealed a consistent correlation between backpressure and power loss, consistent with theoretical expectations and prior research. For predictive analysis, two deep learning models—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—were applied to the generated sensor data. The models were evaluated based on their ability to predict engine states, focusing on prediction accuracy and performance. The results showed that GRU achieved lower Mean Absolute Error (MAE) and Mean Squared Error (MSE), making GRU the more effective model for power loss prediction in automotive applications. These findings highlight the potential of using synthetic data and deep learning techniques to improve predictive maintenance in the automotive industry.

This study addresses the joint optimization of full-length and short-turning operations for the Nanchang Metro Airport Line, aiming to balance operational efficiency and passenger service quality. A novel mathematical model is proposed, which integrates train schedule design, capacity allocation, and passenger flow assignment into a linear programming framework. The model features three key innovations: (1) precise calculation of passenger waiting times under strict capacity constraints by incorporating dynamic passenger flow distribution and train occupancy thresholds; (2) implicit treatment of train numbers as decision variables, enabling flexible adjustments to service frequency based on time-varying demand patterns; and (3) a linear formulation for direct optimal solution computation, avoiding the complexity of nonlinear constraints through variable substitution and constraint relaxation. The model is validated through a case study of the Nanchang Metro Line 1 (Airport Line), where passenger demand is derived from historical data and flight schedules. Numerical experiments demonstrate that the optimized strategy reduces the number of full-length trains by 53%, achieves a 22% power cost saving, and decreases the waiting time for all passengers by 3.4%. The relevant findings and recommendations can offer valuable guidance to metro companies in making operational decisions related to the full-length and short-turning service plans and schedules.

This study proposes a methodological approach to the redesign of a 1980s vehicle, the Lancia Beta HPE, integrating the TRIZ (Theory of Inventive Problem Solving) and the Industrial Design Structure (IDeS) frameworks within the design process. The redesign process focused on both the external morphology of the vehicle and its propulsion system, aligning the outcome with contemporary trends in market evolution, societal shifts, and environmental considerations. The objective of the project was to reinterpret stylistic elements that were typical of 1980s automotive design through a contemporary lens, while incorporating characteristics of the current aesthetic of electric vehicles (EVs). A pivotal element of the research involved a comparative stylistic analysis of past and present vehicle design languages. This facilitated the identification of design guidelines for adapting formal and stylistic details to the electric mobility paradigm, with emphasis on contemporary aesthetics and energy efficiency. The transition from internal combustion to electric propulsion necessitated a comprehensive re-evaluation of the vehicle’s key exterior features, encompassing the front end, body shape, and lighting systems, in order to reflect a novel ecological identity and convey technological advancement. In order to inform stylistic choices, an in-depth exploration of electric propulsion principles was conducted, leveraging AI-based tools such as GPT to support TRIZ-guided problem-solving.

This paper presents a novel method for locally estimating vehicle density on highways based on vehicle-to-vehicle (V2V) communication, a communication mode within intelligent transport systems (ITSs), enabled via IEEE 802.11p and 3GPP C-V2X technologies. Awareness messages (AMs), such as basic safety messages (BSMs, SAE J2735) and cooperative awareness messages (CAMs, ETSI EN 302 637-2), are periodically broadcast by vehicles and can be leveraged to sense the presence of nearby vehicles. Unlike existing approaches that directly combine the number of sensed vehicles with measured packet reception ratio (PRR) of the AM, our method accounts for the deviations in PRR caused by imperfect channel conditions. To address this, we estimate the actual packet reception probability (PRP)–distance curve by exploiting its inherent downward trend along with multiple measured PRR points. From this curve, two metrics are introduced: node awareness probability (NAP) and average awareness ratio (AAR), the latter representing the ratio of sensed vehicles to the total number of vehicles. The real density is then estimated using the number of sensed vehicles and AAR, mitigating the underestimation issues common in V2V-based methods. Simulation results across densities ranging from 0.02 vehs/m to 0.28 vehs/m demonstrate that our method improves estimation accuracy by up to 37% at an actual density of 0.28 vehs/m, compared with methods relying solely on received AMs, without introducing additional communication overhead. Additionally, we demonstrate a practical application where the basic safety message (BSM) transmission rate is dynamically adjusted based on the estimated density, thereby improving traffic management efficiency.

Advanced rider assistance systems (ARAS) play a crucial role in enhancing motorcycle safety through features such as collision avoidance, blind-spot detection, and adaptive cruise control, which rely heavily on sensors like radar, cameras, and LiDAR. However, their performance is often compromised under adverse weather conditions, leading to sensor interference, reduced visibility, and inconsistent reliability. This study evaluates the effectiveness and limitations of ARAS technologies in rain, fog, and snow, focusing on how sensor performance, algorithms, techniques, and dataset suitability influence system reliability. A thematic analysis was conducted, selecting studies focused on ARAS in adverse weather conditions based on specific selection criteria. The analysis shows that while ARAS offers substantial safety benefits, its accuracy declines in challenging environments. Existing datasets, algorithms, and techniques were reviewed to identify the most effective options for ARAS applications. However, more comprehensive weather-resilient datasets and adaptive multi-sensor fusion approaches are still needed. Advancing in these areas will be critical to improving the robustness of ARAS and ensuring safer riding experiences across diverse environmental conditions.

A quasi-static FEM framework for wheel–rail contact is presented, aimed at large parametric analyses including independently rotating wheel (IRW) configurations. Unlike half-space formulations such as CONTACT, the FEM approach resolves global deformations and strongly non-Hertzian geometries while remaining computationally tractable through three key features: (i) a tailored mesh transition around the contact patch, (ii) solver settings optimized for frictional contact convergence, and (iii) an integrated post-processing pipeline for creep forces, micro-slip, and wear. The model is verified against CONTACT, an established surface-discretization reference based on the Boundary Element Method (BEM), demonstrating close agreement in contact pressure, shear stress, and stick–slip patterns across the Manchester Contact Benchmark cases. Accuracy is quantified using error metrics (MAE, RMSE), with discrepancies analyzed in high-yaw, near-flange conditions. Compared with prior FEM-based contact models, the main contributions are: (i) a rigid–flexible domain partition, which reduces 3D computational cost without compromising local contact accuracy; (ii) a frictionless preconditioning step followed by friction restoration, eliminating artificial shear-induced deformation at first contact and accelerating convergence; (iii) an automated selection of the elastic slip tolerance (slto) based on frictional-energy consistency, ensuring numerical robustness; and (iv) an IRW-oriented parametrization of toe angle, camber, and wheel spacing. The proposed framework provides a robust basis for large-scale studies and can be extended to transient or elastoplastic analyses relevant to dynamic loading, curved tracks, and wheel defects.

This study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating affective constructs (innovativeness, optimism, and hedonic motivation) to examine user adoption of autonomous bus (AB) in China, where government-supported deployment creates unique adoption dynamics. Analyzing 313 responses, collected via stratified sampling using SmartPLS 4.0, we identified innovativeness as the dominant driver (total effect, β = 0.347), directly influencing behavioral intention (β = 0.164*) and indirectly shaping optimism (β = 0.692*), effort expectancy (β = 0.347*), and hedonic motivation (β = 0.681*). Our findings highlight contextual influences in public service systems. Performance expectancy (β = 0.153*) exerts a stronger effect than hedonic or social factors (H6/H3 rejected), while optimism demonstrates a dual scaffolding effect (OPT→EE, β = 0.189*; OPT→PE, β = 0.401*), reflecting a “calculative optimism” pattern where users balance technological interest with pragmatic utility evaluation in policy-supported deployment contexts. From a practical perspective, these findings suggest targeting high-innovativeness users through incentive programs, emphasizing system reliability over ease of use, and implementing adapted designs. This study contributes to the literature both theoretically, by validating the hierarchical role of innovativeness in UTAUT, and practically, by offering actionable strategies for China’s ongoing AB deployment initiative, including ISO-standardized UX and policy tools such as municipal Innovator Badges.