Vehicles

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. Full article

This study presents the development of an open-source Driver-in-the-Loop simulation platform, specifically designed to test and analyze advanced automated driving functions. We emphasize the creation of a versatile system architecture that ensures seamless integration and interchangeability of components, supporting diverse research needs. Central to the simulator’s configuration is a hexapod motion platform with six degrees of freedom, chosen through a detailed benchmarking process to ensure dynamic accuracy and fidelity. The simulator employs a half-vehicle cabin, providing an immersive environment where drivers can interact with authentic human–machine interfaces such as pedals, steering, and gear shifters. By projecting complex driving scenarios onto a curved screen, drivers engage with critical maneuvers in a controlled virtual environment. Key innovations include the integration of a motion cueing algorithm and an adaptable, cost-effective open-source framework, facilitating collaboration among researchers and industry experts. The platform enables standardized testing and offers a robust solution for the iterative development and validation of automated driving technologies. Functionality and effectiveness were validated through testing with the ISO lane change maneuver, affirming the simulator’s capabilities. Full article

In the present investigation, carbon dioxide (CO₂), carbon monoxide (CO), hydrocarbons (HC), nitric oxides (NO_X), particulate matter (PM), and fuel consumption were measured in a compression ignition internal combustion engine on a road route cycle in Quito, Ecuador. We used premium diesel and a mixture of diesel and cerium oxide at a concentration of 250 ppm. This research aimed to investigate the impact of cerium oxide on fossil fuels in terms of CO₂, CO, HC, NOx, PM, and fuel consumption. Five repetitions were performed for each fuel, and the results obtained were statistically analyzed using control charts. The experimental results showed a 27.1% reduction in PM, a 24.9% increase in NOx, and a 24.2% increase in HC, along with a 1% decrease in fuel consumption compared to the premium diesel case. We observed that the reduction in PM was due to the catalytic action of CeO₂, which enhances carbon oxidation. On the other hand, the increase in NOx was related to the higher temperature in the combustion chamber resulting from the improved thermal efficiency of the engine. This study provides guidelines for controlling air pollutants originating from vehicle emissions in high-altitude (over 2000 masl) road operations using cerium oxide as an additive. Full article

This paper presents the design, development, and experimental validation of a low-cost, modular, and scalable Advanced Driver Assistance System (ADAS) platform intended for research and educational purposes. The system integrates embedded computer vision and electronic control using an FPGA for accelerated real-time image processing and an STM32 microcontroller for sensor data acquisition and actuator management. The YOLOv3-Tiny model is implemented to enable efficient pedestrian and vehicle detection under hardware constraints, while additional vision algorithms are used for lane line detection, ensuring a favorable trade-off between accuracy and processing speed. The platform is deployed on a 1:5 scale gasoline-powered vehicle, offering a safe and cost-effective testbed for validating ADAS functionalities, such as lane tracking, pedestrian and vehicle identification, and semi-autonomous navigation. The methodology includes the integration of a CMOS camera, an FPGA development board, and various sensors (LiDAR, ultrasonic, and Hall-effect), along with synchronized communication protocols to ensure real-time data exchange between vision and control modules. A wireless graphical user interface (GUI) enables remote monitoring and teleoperation. Experimental results show competitive detection accuracy—exceeding 94% in structured environments—and processing latencies below 70 ms per frame, demonstrating the platform’s effectiveness for rapid prototyping and applied training. Its modularity and affordability position it as a powerful tool for advancing ADAS research and education, with high potential for future expansion to full-scale autonomous vehicle applications. Full article

This research paper describes a new method to measure the groove wander effect on real concrete road surfaces using an Internal Drum Test Bench. Groove wander describes lateral forces resulting from interactions between a tire and the road surface texture. To create these lateral forces, the test bench induces a continuous lateral displacement of the tire on a textured road surface. It was found that the groove wander effect could be reproduced on a test bench. The presented method was shown to provide reproducible results that meet the expectations from previous studies. The overall findings of the measurements were that smaller tires and tires with longitudinally aligned profile show higher susceptibility to the groove wander effect. Full article

Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization methods from the field of operations research to improve the efficiency of service workshops for bus maintenance and repair. Based on an analysis of collected data using queueing theory, the authors assessed the current system performance and found that the queueing system still has spare capacity and could be downsized, which aligns with the company’s management goals. Specifically, the company plans to reduce the number of bus repair service stations (servers in a queueing system). The main question is whether the system will continue to function effectively after this reduction. Three specific downsizing solutions were proposed and evaluated using queueing theory methods: extending the daily operating hours of the workshops, reducing the number of arriving buses, and increasing the productivity of a service station (server). The results show that, under high system load, only those solutions that increase the productivity of individual service stations (servers) in the queueing system provide optimal outcomes. Other solutions merely result in longer queues and associated losses due to buses waiting for service, preventing them from performing their intended function and causing financial loss to the company. Full article

The automatic detection of traffic accidents plays an increasingly vital role in advancing intelligent traffic monitoring systems and improving road safety. Leveraging computer vision techniques offers a promising solution, enabling rapid, reliable, and automated identification of accidents, thereby significantly reducing emergency response times. This study proposes an enhanced version of the YOLO11 architecture, termed YOLO11-AMF. The proposed model integrates a Mamba-Like Linear Attention (MLLA) mechanism, an Asymptotic Feature Pyramid Network (AFPN), and a novel Focaler-IoU loss function to optimize traffic accident detection performance under complex and diverse conditions. The MLLA module introduces efficient linear attention to improve contextual representation, while the AFPN adopts an asymptotic feature fusion strategy to enhance the expressiveness of the detection head. The Focaler-IoU further refines bounding box regression for improved localization accuracy. To evaluate the proposed model, a custom dataset of traffic accident images was constructed. Experimental results demonstrate that the enhanced model achieves precision, recall, mAP50, and mAP50–95 scores of 96.5%, 82.9%, 90.0%, and 66.0%, respectively, surpassing the baseline YOLO11n by 6.5%, 6.0%, 6.3%, and 6.3% on these metrics. These findings demonstrate the effectiveness of the proposed enhancements and suggest the model’s potential for robust and accurate traffic accident detection within real-world conditions. Full article

To improve the stability and safety of electric vehicles during medium-to-high-speed cornering, this paper investigates torque differential control for dual rear-wheel hub motor drive systems, extending beyond traditional speed control based on the Ackermann steering model. A nonlinear three-degree-of-freedom vehicle dynamics model incorporating the Dugoff tire model was established. By introducing the maximum correntropy criterion, an unscented Kalman filter was developed to estimate longitudinal velocity, sideslip angle at the center of mass, and yaw rate. Building upon the speed differential control achieved through Ackermann steering model-based rear-wheel speed calculation, improvements were made to the conventional exponential reaching law, while a novel switching function was proposed to formulate a new sliding mode controller for computing an additional yaw moment to realize torque differential control. Finally, simulations conducted on the Carsim/Simulink platform demonstrated that the maximum correntropy criterion unscented Kalman filter effectively improves estimation accuracy, achieving at least a 22.00% reduction in RMSE metrics compared to conventional unscented Kalman filter. With torque control exhibiting higher vehicle stability than speed control, the RMSE values of yaw rate and sideslip angle at the center of mass are reduced by at least 20.00% and 4.55%, respectively, enabling stable operation during medium-to-high-speed cornering conditions. Full article

This study developed and validated an integrated theoretical framework combining the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to investigate how driving assistance technologies (DATs) influence risky driving behaviors among young urban Chinese drivers. Based on this framework, we proposed and tested several hypotheses regarding the effects of psychological and technological factors on risky driving intentions and behaviors. A survey was conducted with 495 young drivers in Shaoguan, Guangdong Province, examining psychological factors, technology acceptance, and their influence on risky driving behaviors. Structural equation modeling revealed that the integrated TPB-TAM explained 58.3% of the variance in behavioral intentions and 42.6% of the variance in actual risky driving behaviors, significantly outperforming single-theory models. Attitudes toward risky driving (β = 0.287) emerged as the strongest TPB predictor of behavioral intentions, while perceived usefulness (β = −0.172) and perceived ease of use (β = −0.113) of driving assistance technologies negatively influenced risky driving intentions. Multi-group analysis identified significant gender and driving experience differences. Logistic regression analyses demonstrated that model constructs significantly predicted actual traffic violations and accidents. These findings provide theoretical insights into risky driving determinants and practical guidance for developing targeted interventions and effective traffic safety policies for young drivers in urban China. Full article

Accurate and reliable multi-scene positioning remains a critical challenge in autonomous driving systems, as conventional fixed-noise fusion strategies struggle to handle the dynamic error characteristics of heterogeneous sensors in complex operational environments. This paper proposes a novel noise-adaptive fusion framework integrating Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) measurements. Our key innovation lies in developing a dual noise estimation model that synergizes priori weighting with posterior variance compensation. Specifically, we establish an a priori weighting model for satellite pseudorange errors based on elevation angles and signal-to-noise ratios (SNRs), complemented by a Helmert variance component estimation for posterior refinement. For INS error modeling, we derive a bias instability noise accumulation model through Allan variance analysis. These adaptive noise estimates dynamically update both process and observation noise covariance matrices in our Error-State Kalman Filter (ESKF) implementation, enabling real-time calibration of GNSS and INS contributions. Comprehensive field experiments demonstrate two key advantages: (1) The proposed noise estimation model achieves 37.7% higher accuracy in quantifying GNSS single-point positioning uncertainties compared to conventional elevation-based weighting; (2) in unstructured environments with intermittent signal outages, the fusion system maintains an average absolute trajectory error (ATE) of less than 0.6 m, outperforming state-of-the-art fixed-weight fusion methods by 36.71% in positioning consistency. These results validate the framework’s capability to autonomously balance sensor reliability under dynamic environmental conditions, significantly enhancing positioning robustness for autonomous vehicles. Full article

To inform the development of effective prevention strategies for reducing pedestrian fatalities in an ageing society, a retrospective analysis was conducted on fatal pedestrian–vehicle collisions in Japan. All pedestrian fatalities caused by motor vehicle collisions between 2013 and 2022 in Shiga Prefecture were reviewed. Among the 164 pedestrian fatalities (involving 92 males and 72 females), the most common scenario involved a pedestrian crossing the road (57.3%). In 61 cases (64.9%), pedestrians crossed from the oncoming traffic lane side to the vehicle’s lane side (i.e., crossing from right to left from the driver’s perspective, as vehicles drive on the left in Japan). In 33 cases (35.1%), pedestrians crossed from the vehicle’s lane side to the oncoming traffic lane side. Among cases of pedestrians crossing from the vehicle’s lane side, 54.5% were struck by the near side of the vehicle’s front, whereas 39.7% of those crossing from the oncoming traffic lane side were hit by the far side of the vehicle’s front (p = 0.02). Therefore, for both crossing directions, collisions frequently involved the front left of the vehicle. When pedestrians were struck by the front centre or front right of the vehicle, the collision speeds were higher when pedestrians crossed from the oncoming traffic lane side to the vehicle’s lane side rather than crossing from the vehicle’s lane side to the oncoming traffic lane side. A significant difference in collision speed was observed for impacts with the vehicle’s front centre (p = 0.048). The findings suggest that increasing awareness that older pedestrians may cross roads from the oncoming traffic lane side may help drivers anticipate and avoid potential collisions. Full article

The presented research is focused on the development of the bus rapid transit (BRT) system, combining the high capacity of rail transport with the flexibility of bus routes. Classic BRT systems have certain limitations, particularly concerning a single rolling stock capacity. The main motivation of the work is to find efficient and cost-effective solutions to increase passenger traffic in the BRT system while optimizing fuel consumption. The main contribution of this study is the comprehensive analysis and optimization of various configurations of trailer bus trains, which represent a flexible and cost-effective alternative to traditional single or articulated buses. Based on two schemes, four possible options for using trailer bus trains are offered, which differ in the number of sections and working engines. Among the suggested schemes of trailer bus trains, the two-section and three-section schemes with all engines running and the three-section scheme with one engine turned off are appropriate for use due to improved fuel efficiency indicators with better or acceptable traction and speed properties. Calculations carried out on a mathematical model show that, for example, a two-section bus train can provide a reduction of specific fuel consumption per passenger by 6.3% compared to a single bus at full load, while a three-section train can provide even greater savings of up to 8.4%. Selective shutdown of one of the engines in a multi-section train can lead to an additional improvement in fuel efficiency by 5–10%, without leading to a critical reduction in the required traction characteristics. Full article

Passenger safety remains a primary goal in vehicle engineering, requiring the development of advanced passive safety systems to reduce injuries during collisions. Impact attenuators (particularly for race cars) are a crucial component for the safety of the driver. The impact of the impact attenuator (IA) is demonstrated by the behavior of a seat-belted dummy in a frontal collision with a rigid wall. The aim of this paper is to confirm the qualities of water as a damping agent in the manufacturing of the IA. To reach a conclusion, a theoretical model is used and experimental tests are performed. Once the loads operating on the dummy have been identified, it is confirmed that they fall within the range that the existing requirements recommend. The car is viewed as a structure with a seat-belt-fastened dummy and an impact attenuator. Research is being conducted on a new water-based impact attenuator technology. A frontal collision of the car–dummy assembly was taken into consideration when analyzing the dummy’s behavior in accordance with the criteria. A simulation program was used to calculate the accelerations at various points on the mannequin’s body as well as the force that manifested on the seat belts. So, the good qualities of IAs using water are revealed and support designers in their efforts to obtain better shock behavior. In the simulation, the variation of internal energy accumulated by the vehicle, displacements and velocities of various points on the chassis, as well as the accelerations of the vehicle and the occupant were determined. In the experiment, the vehicle velocities for both test cases were established and used in the simulation, and the accelerations of the vehicle and dummy were measured. The assessment was carried out by comparing experimental and simulation data, focusing on acceleration values recorded on both the dummy and the vehicle. Evaluation criteria such as HIC and ThAC were applied to determine the severity of the impact and the effectiveness of the proposed water-based attenuator. Full article

Connected and Autonomous Vehicles (CAVs) remain vulnerable to cyberattacks due to inherent security gaps in the Controller Area Network (CAN) protocol. We present a structured Python (3.11.13) framework that repairs structural inconsistencies in a public CAV dataset to improve the reliability of machine learning-based intrusion detection. We assess the effect of training data volume and compare Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers across four attack types: DoS, Fuzzy, RPM spoofing, and GEAR spoofing. XGBoost outperforms RF, achieving 99.2 % accuracy on the DoS dataset and 100 % accuracy on the Fuzzy, RPM, and GEAR datasets. The Synthetic Minority Oversampling Technique (SMOTE) further enhances minority-class detection without compromising overall performance. This methodology provides a generalizable framework for anomaly detection in other connected systems, including smart grids, autonomous defense platforms, and industrial control networks. Full article

Traffic congestion has increased significantly in today’s rapidly urbanizing world, influencing people’s daily lives. Traffic signal control systems (TSCSs) play an important role in alleviating congestion by optimizing traffic light timings and improving road efficiency. Yet traditional TSCSs neglected pedestrians, cyclists, and other non-monitored road users, degrading traffic signal optimization (TSO). Therefore, this framework proposes a multi-object-based traffic flow analysis and intensity estimation model for efficient TSO using Upper Confidence Bound Multi-agent Reinforcement Learning Cubic Spline Fuzzy Logic (UCB-MRL-CSFL). Initially, the real-time traffic videos undergo frame conversion and redundant frame removal, followed by preprocessing. Then, the lanes are detected; further, the objects are detected using Temporal Context You Only Look Once (TC-YOLO). Now, the object counting in each lane is carried out using the Cumulative Vehicle Motion Kalman Filter (CVMKF), followed by queue detection using Vehicle Density Mapping (VDM). Next, the traffic flow is analyzed by Feature Variant Optical Flow (FVOF), followed by traffic intensity estimation. Now, based on the siren flashlight colors, emergency vehicles are separated. Lastly, UCB-MRL-CSFL optimizes the Traffic Signals (TSs) based on the separated emergency vehicle, pedestrian information, and traffic intensity. Therefore, the proposed framework outperforms the other conventional methodologies for TSO by considering pedestrians, cyclists, and so on, with higher computational efficiency (94.45%). Full article

Active suspension can improve vehicle vibrations caused by road excitation. For trucks, the vehicle mass change is usually large, and changes in vehicle mass will affect the control performance of the active suspension. In order to solve the problem of active suspension control performance decreasing due to large changes in vehicle mass, this paper proposes an active suspension control method integrating online mass estimation. This control method is designed based on the mass estimation algorithm of the recursive least squares method with a forgetting factor (FFRLS) and the Linear Quadratic Regulator (LQR) algorithm. A set of feedback control matrices $K$ is obtained according to different vehicle masses. Then, the mass estimation algorithm can estimate the actual vehicle mass in real-time during the vehicle acceleration process. According to the mass estimation value, a corresponding feedback control matrix $K$ is selected from the control matrix set, and $K$ is used as the actual control gain matrix of the current active suspension. With specific simulation cases, the vehicle vibration response is studied by the numerical simulation method. The results of the simulation process have shown that when the vehicle mass changes largely, the suspension dynamic deflection and tire dynamic deformation are significantly reduced while keeping a good vehicle body attitude control effect by using an active suspension controller integrated with online mass estimation. In the random road simulation, suspension dynamic deflection is reduced by 3.26%, and tire dynamic deformation is reduced by 5.91% compared with the original active suspension controller. In the road bump simulation, suspension dynamic deflection and tire dynamic deformation are also significantly reduced. As a consequence, the stability and comfort of the vehicle have been greatly enhanced. Full article

Differential planetary roller lead screw (DPRS) serves as a quintessential actuating mechanism within the electromechanical braking (EMB) systems of vehicles, where its operational reliability is paramount to ensuring braking safety. Considering different braking intensities, how assembly errors affect the contact stress in DPRS was analyzed via the finite element method. Firstly, the braking force of the EMB system that employed DPRS was verified by the braking performance of legal provisions. Secondly, a rigid body dynamics model of DPRS was established to analyze the response time, braking clamping force, and axial contact force of DPRS under varied braking intensities. Finally, a finite element model of DPRS was constructed. The impact of assembly errors in the lead screw and rollers on the contact stress were investigated within the DPRS mechanism based on this model. The results indicate that as braking intensity increases, the deviation of the lead screw exerts a greater influence on the contact stress generated by the engagement between the lead screw and rollers compared to that between the nut and rollers. The skewness of the rollers also affects the contact stress generated by the engagement of both the lead screw with rollers and the nut with rollers. When assembly errors reach a certain threshold, the equivalent plastic strain is induced to exceed the critical value. This situation significantly impairing the normal operation of DPRS. This study provides guidance for setting the threshold of assembly errors in DPRS mechanisms. It also holds significant implications for the operational reliability of EMB systems. Full article

Aiming at the problems of complex control processes, strong model dependence, and difficult engineering application when the existing active suspension control strategy is applied to multi-axle vehicles, an active suspension control strategy based on the inverse position solution of a parallel mechanism is proposed. First, the active suspension of the three-axle emergency rescue vehicle is grouped and interconnected within the group, and it is equivalently constructed into a 3-DOF parallel mechanism. Then, the displacement of each equivalent suspension actuating hydraulic cylinder is calculated by using the method of the inverse position solution of a parallel mechanism, and then the equivalent actuating hydraulic cylinder is reversely driven according to the displacement, thereby realizing the effective control of the attitude of the vehicle body. To verify the effectiveness of the proposed control strategy, a three-axis vehicle experimental platform integrating active suspension and hydro-pneumatic suspension was built, and a pulse road experiment and gravel pavement experiment were carried out and compared with hydro-pneumatic suspension. Both types of road experimental results show that compared to hydro-pneumatic suspension, the active suspension control strategy based on the inverse position solution of a parallel mechanism proposed in this paper exhibits different degrees of advantages in reducing the peak values of the vehicle vertical displacement, pitch angle, and roll angle changes, as well as suppressing various vibration accelerations, significantly improving the vehicle’s driving smoothness and handling stability. Full article