Search Results (86)

Foamed concrete has been used to construct lightweight embankments as a substitute for conventional fills, aiming to promote its engineering application in soft-soil regions. However, the dynamic response and safety mechanism of foamed concrete embankments during vehicle collision are not yet fully understood. In this paper, the safety performance of lightweight foamed concrete embankments under vehicle–guardrail collision and the optimal design of the guardrail foundation are investigated from the perspectives of lateral displacement and stress distribution. Through static uniaxial compression tests, the stress–strain curves, compressive strength, elastic modulus, and statistical variability of foamed concrete with six different mix proportions were obtained. On this basis, a coupled finite element model of the vehicle–guardrail–lightweight embankment system was established (the guardrail and its foundation were modeled using a linear elastic constitutive model, the embankment using a crushable foam model, and the vehicle using a 1.5 t passenger car model validated by full-scale crash tests). According to the passenger car impact conditions specified in current Chinese regulations (velocity 100 km/h, angle 20°), the peak lateral displacement and peak principal stress of the lightweight embankment were analyzed for four foundation base slab lengths (L₀, 1.1 L₀, 1.2 L₀, 1.3 L₀). The results show that increasing the base slab length effectively reduces lateral displacement and stress concentration. Increasing the length by 10–20% reduces the peak lateral displacement by up to 68%, and the peak principal stress remains far below the material strength. From the perspectives of structural stability and cost-effectiveness, a 10–20% increase in the base slab length is recommended. The ratio of the peak principal stress to the material strength can serve as a criterion for evaluating the safety margin and assessing the rationality of the foundation design. This study provides quantitative evidence for optimizing the guardrail foundation base slab length to enhance the collision safety of lightweight foamed concrete embankments, and the proposed design range offers a cost-effective reference for practical engineering applications in soft-soil regions. Full article

Optimizing a high-fidelity GT3 race car setup is a serious dimensional, nonlinear problem in which small changes to mechanical parameters can affect lap time, handling balance, and vehicle stability. Existing motorsport AI studies largely emphasize racing line optimization, autonomous control, race strategy, or offline vehicle dynamics estimation, while the mechanical setup layer is often treated as fixed or tuned manually. This paper presents a staged simulator-based setup optimization framework augmented with setup-conditioned telemetry response modeling. Using the virtual BMW Z4 GT3 vehicle model implemented within the Assetto Corsa (v1.16.4) simulation environment as a controlled GT3 test platform, 134 setup configurations were evaluated at the Red Bull Ring under a fixed simulator AI driving policy. The staged search improved the best lap time from 91.430 s to 91.040 s, corresponding to a 0.390 s reduction. To move beyond a single aggregate lap-time claim, the full telemetry corpus was processed into 585 stable laps and 29,250 track-position segment samples. A setup-conditioned LightGBM model was trained to predict segment time and local vehicle response metrics from setup parameters and segment context, using five-fold GroupKFold validation by telemetry file to avoid random row leakage. The setup-conditioned segment model reconstructed held-out file-level lap time with 0.223 s mean absolute error and Spearman correlation of 0.961, outperforming a setup-only model at 0.288 s, a track-only segment model at 0.687 s, and a shuffled-setup placebo at 0.776 s. The same setup-conditioned model also improved the prediction of segment-level speed, slip angle, tire load spread, rake (defined here as rear-front ride height difference), tire temperature, yaw response, and lateral acceleration. These results show that high-frequency telemetry can support not only staged setup search, but also quantifiable learning of where and how setup changes alter vehicle behavior around the lap. Full article

To mitigate driving risks from brake failure on long and steep downhill sections, this study designs three deployment schemes for radar–video fusion devices: a baseline scenario with no coverage, a scenario with partial coverage in high-risk areas, and a scenario with full coverage. Corresponding information service strategies are delivered via Human–Machine Interfaces (HMIs), forming an integrated active prevention and control framework from risk perception to preventive action. Driving simulation experiments focusing on the car-following process were conducted to collect vehicle operational data and extract characteristic indicators based on the Wiedemann model. A Generalized Linear Mixed Model was employed to comprehensively examine the effects of HMIs on car-following behavior to identify the optimal active prevention strategy. Results show that drivers exhibit greater caution under the partial coverage scheme, with time headway increasing by 47.63% compared to the scheme with no radar–video fusion devices to ensure safety. Under full coverage conditions, drivers can obtain real-time information about the leading vehicle’s status and the distance between the two vehicles in key risk sections. Drivers choose to follow the leading vehicle, balancing both safety in car-following and efficiency on long and steep downhill sections. As the level of accompanying services improves, drivers engage in self-regulation to avoid rear-end collisions. Particularly under the scheme with full coverage of radar–video fusion devices, the standing distance significantly increases by 219.37% compared to the partial coverage condition. Drivers demonstrate optimal vehicle control capabilities. Furthermore, there is an interaction effect between the accompanying service strategy and drivers’ attributes on car-following behaviors. Under different schemes, more experienced drivers exhibit a certain degree of aggressiveness, providing a basis for the targeted design of information services for different types of drivers. The findings support the deployment and application of risk perception and prevention devices on long and steep downhill sections, which can effectively enhance the comprehensive safety of such special roads in the connected vehicle environment. Full article

Fine-grained vehicle orientation estimation is widely reported with strong in-domain accuracy, yet performance degrades substantially when models are applied across datasets; the relative contributions of visual domain shift and annotation label incompatibility to this degradation remain poorly understood. A controlled cross-dataset benchmark was conducted using two publicly available datasets—Car Full View (CFV) and Freiburg Static Cars 52 v1.1 (UnsupCar)—under a fixed ConvNeXt-Small predictor with a varied training source, test target, and image preprocessing strategy. All conditions were evaluated with five-fold cross-validation at the vehicle-instance level. Annotation label incompatibility was identified as the dominant source of transfer error: correcting the angular convention mismatch in UnsupCar orientation labels reduced cross-dataset circular mean absolute error (CMAE) by approximately $3.5$–$4.5^{\circ }$. Crop protocol was a similarly large factor—train/test crop mismatch raised CMAE into the 9–${12}^{\circ }$ range. Square cropping with mirrored boundary padding provided the most robust preprocessing across both in-domain and cross-dataset conditions. After label harmonization, a residual transfer gap of approximately $2^{\circ }$ remained, with a consistent directional asymmetry favoring the UnsupCar-to-CFV transfer direction. Joint training on both harmonized datasets achieved the best-balanced performance ($3.{77}^{\circ }$ on CFV; $5.{38}^{\circ }$ on UnsupCar). These results demonstrate that instance-level splitting, explicit label harmonization, and consistent crop definition are necessary preconditions for credible cross-dataset vehicle orientation evaluation. Full article

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation. Full article

The flying car, academically known as electric vertical takeoff and landing (eVTOL) aircraft, is one of the core vehicles for low-altitude transportation. The split-type tilt-rotor cargo flying vehicle that is composed of tilt rotors, a fixed wing, and a detachable cargo pod exhibits characteristics of rotor–wing coupling and significant changes in weight and center of gravity (CG). Therefore, empirical design rules for conventional aircraft are not directly applicable. This paper presents the stability analysis of two configurations, i.e., the aerial vehicle module (AVM) and the aerial cargo configuration (ACC). The dynamic model of the proposed cargo flying vehicle is developed. Based on test data from the tilt-rotor experimental bench, the CFD models of the rotor subsystems and the full vehicle were validated and subsequently used to simulate the aerodynamic performance and stability of the flying vehicle under various operating conditions. The results indicate that vertical takeoff and landing (VTOL) stability is highly sensitive to the rotor–CG lever arm. Under cruise conditions, the CG positions were tested within a range of 1.4–1.7 $c_A$ (mean aerodynamic chord) from the wing leading edge with the most favorable static stability observed at 1.62 $c_A$. Among the three proposed tilt-rotor strategies, initiating the secondary tilt rotors first while keeping the main tilt rotors vertical results in the weakest rotor–surface aerodynamic coupling, the lowest pitching-moment peaks, and favorable longitudinal static stability. These findings inform CG management, aerodynamic layout, and tilt-schedule design for split-type tilt-rotor cargo vehicles in low-altitude transportation. Full article

Enhancing traffic flow stability is a critical approach for achieving energy conservation and emission reduction in road transportation. While existing cooperative car-following strategies for connected and automated vehicles (CAVs) are effective, their heavy reliance on reliable Vehicle-to-Everything (V2X) communication limits practical deployment. This study proposes an energy-optimal car-following model for CAVs, introducing a regulation term based on the predicted optimal speed difference. Rather than directly using predicted kinematic variables, this mechanism adjusts acceleration based on the difference in optimal velocity between predicted and current headways. This leverages the inherent filtering of the optimal velocity function to ensure smooth control. Linear and nonlinear stability analysis confirm the model’s effectiveness in suppressing traffic disturbances and suppression of stop-and-go wave propagation, thereby laying the theoretical foundation for smoother traffic flow and the resulting reductions in energy consumption and emissions. Simulations validate the theoretical findings. Compared to the classical Full Velocity Difference (FVD) model, the proposed model achieves significant reductions in energy consumption (38.82%), CO₂ emissions (39.41%), and NO_x emissions (83.46%). The model also reduces rear-end collision risks, ensuring higher safety. These findings indicate that the proposed ego-vehicle predictive framework provides a communication-independent and practically viable approach for improving the energy efficiency and stability of CAV traffic flow. Full article

This paper presents a method to design a static output feedback active suspension controller with a quarter-car model for motion sickness mitigation. To mitigate motion sickness in a vehicle, it has been known that the vertical acceleration and pitch rate of a sprung mass should be reduced over the frequency range from 0.8 to 8 Hz. For this purpose, a half-car model has been used with linear quadratic optimal control for controller design because it can describe the pitch motion of a sprung mass. However, a controller design procedure with the half-car model is relatively more complex than the quarter-car one. To cope with this problem, a quarter-car model is used for controller design in this paper. The half-car model consists of two quarter-car models. Based on this fact, a controller designed with a quarter-car model can be applied to the front and rear suspensions in the half-car one. To avoid the full-state feedback in a real vehicle, a static output feedback structure is selected. To find the gains of the controllers for the quarter-car models in the front and rear suspensions, linear quadratic optimal control and a simulation-based optimization method are applied. To validate the proposed method, the controllers designed with the quarter-car and half-car models are simulated on a vehicle simulation package. From the simulation results, it is shown that the static output feedback active suspension controller designed with the quarter-car model is quite effective for motion sickness mitigation. Full article

The parameter identification of Autonomous Underwater Vehicles (AUVs) serves as a fundamental basis for achieving high-precision motion control, state monitoring, and system development. Currently, AUV parameter identification typically relies on the complete motion information obtained from onboard sensors. However, in practical applications, it is often challenging to accurately measure key state variables such as velocity and angular velocity, resulting in incomplete measurement data that compromises identification accuracy and model reliability. This issue is particularly pronounced in vertical motion tasks involving low-speed, large pitch angles, and highly maneuverable conditions, where the strong coupling and nonlinear characteristics of underwater vehicles become more significant. Traditional hydrodynamic models based on full-state measurements often suffer from limited descriptive capability and difficulties in parameter estimation under such conditions. To address these challenges, this study investigates a parameter identification method for AUVs operating under vertical, large-amplitude maneuvers with constrained measurement information. A control autoregressive (CAR) model-based identification approach is derived, which requires only pitch angle, vertical velocity, and vertical position data, thereby reducing the dependence on complete state observations. To overcome the limitations of the conventional Recursive Least Squares (RLS) algorithm—namely, its slow convergence and low accuracy under rapidly changing conditions—a Multi-Innovation Least Squares (MILS) algorithm is proposed to enable the efficient estimation of nonlinear hydrodynamic characteristics in complex dynamic environments. The simulation and experimental results validate the effectiveness of the proposed method, demonstrating high identification accuracy and robustness in scenarios involving large pitch angles and rapid maneuvering. The results confirm that the combined use of the CAR model and MILS algorithm significantly enhances model adaptability and accuracy, providing a solid data foundation and theoretical support for the design of AUV control systems in complex operational environments. Full article

This study proposes a new method to improve the accuracy of car-following models in predicting the mobility of mixed traffic flow involving trucks and automated vehicles (AVs). A classification is developed to categorize car-following behaviors into eight distinct modes based on vehicle type (passenger car/truck) and autonomy level (human-driven vehicle [HDV]/AV) for parameter calibration and simulation. The car-following model parameters are calibrated based on the HighD dataset, and the models are selected through minimizing statistical error. A cellular-automaton-based simulation platform is implemented in MATLAB (R2023b), and a decision framework is developed for the simulation. Key findings demonstrate that mode-specific parameter calibration improves model accuracy, achieving an average error reduction of 80% compared to empirical methods. The simulation results reveal a positive correlation between the AV penetration rate and traffic flow stability, which consequently enhances capacity. Specifically, a full transition from 0% to 100% AV penetration increases traffic capacity by 50%. Conversely, elevated truck penetration rates degrade traffic flow stability, reducing the average speed by 75.37% under full truck penetration scenarios. Additionally, higher AV penetration helps stabilize traffic flow, leading to reduced speed fluctuations and lower emissions, while higher truck proportions contribute to higher emissions due to increased traffic instability. Full article

Oilseed rape yield critically reflects varietal superiority. Rapid field-scale estimation enables efficient high-throughput breeding. This study evaluates unmanned aerial vehicle (UAV) hyperspectral imagery’s potential for yield prediction at the pod stage by utilizing wavelength selection and vegetation indices. Meanwhile, optimized feature selection algorithms identified effective wavelengths (EWs) and vegetation indices (VIs) for yield estimation. The optimal yield estimation models based on EWs and VIs were established, respectively, by using multiple linear regression (MLR), partial least squares regression (PLSR), extreme learning machine (ELM), and a least squares support vector machine (LS-SVM). The main results were as follows: (i) The yield prediction of oilseed rape using EWs showed better prediction and robustness compared to the full-spectral model. In particular, the competitive adaptive reweighted sampling–extreme learning machine (CARS-ELM) model (R_pre = 0.8122, RMSE_P = 170.4 kg/hm²) achieved the best prediction performance. (ii) The ELM model (R_pre = 0.7674 and RMSE_P = 187.6 kg/hm²), using 14 combined VIs, showed excellent performance. These results indicate that the remote sensing image data obtained from the UAV hyperspectral remote sensing system can be used to enable the high-throughput acquisition of oilseed rape yield information in the field. This study provides technical guidance for the crop yield estimation and high-throughput detection of breeding information. Full article

Real-time estimation of the road surface friction coefficient is crucial for vehicle dynamics control. Under large steering angles, the accuracy of existing road surface friction coefficient estimation methods is unsatisfactory due to the nonlinear characteristics of the tire. This paper proposes a segmented estimation method for the road adhesion coefficient, which considers different steering angle ranges and utilizes multimodal vehicle dynamics fusion. The method is designed to accurately estimate the road adhesion coefficient across the full steering angle range of the steer-by-wire system. When the front wheel angle is small (less than 2.8°), an improved Unscented Kalman Filter (AUKF) algorithm is used to estimate the road surface friction coefficient. When the front wheel angle is large (greater than 3.2°), a rack force expansion state observer is constructed using the dynamics model of the steer-by-wire actuator to estimate the rack force. Based on the principle that the rack force varies with different road surface friction coefficients for the same steering angle, the rack force is used to distinguish the road surface friction coefficient. When the front wheel angle is between the two ranges, the average value of both methods is taken as the final estimate. The method is verified through Matlab/Simulink and CarSim co-simulation, as well as hardware-in-the-loop experiments of the steer-by-wire system. Simulation results show that the relative error of road surface friction coefficient estimation is less than 10% under different steering angles. The segmented combination estimation strategy proposed in this paper reduces the impact of tire nonlinearities on the estimation result and achieves high-precision road surface friction coefficient estimation over the entire steering angle range of the steer-by-wire system, which is of significant importance for vehicle dynamics control. Full article

This paper presents a comparative study on active suspension controllers for ride comfort. Two types of active suspension controllers are designed and compared in terms of ride comfort: static output feedback (SOF) and parameter adaptive ones, which have identical controller structure. A quarter-car model is selected as a vehicle model. To date, LQR has been used as an active suspension controller. LQR is hard to implement in real vehicles due to the full-state measurement requirement. To avoid the full-state measurement of LQR, SOF control is selected as a controller structure in this paper. Suspension stroke and its rate are selected as sensor outputs for SOF and parameter active controllers. Two types of SOF controllers are designed. The first is the LQ SOF controller, designed with the state-space model and LQ cost function. The second is SOF controllers, designed by simulation-based optimization (SBOM) for the quarter-car model with nonlinear spring and damper. A parameter adaptive controller is designed with the recursive lease square (RLS) algorithm and its equivalent extended Kalman filter (EKF). For comparison, LQR is designed and used as a baseline. From simulation results, it is shown that the static output feedback and parameter adaptive controllers are equivalent to each other in terms of controller structure and ride comfort and which conditions are needed for better control performance on those controllers. Full article

Potato is a major food crop in China. Its development and nutritional state can be inferred by the content of chlorophyll in its canopy. However, the existing study on applying feature extraction and optimization algorithms to determine the canopy SPAD (Soil–Plant Analytical Development) values of potatoes at various fertility stages is inadequate and not very reliable. Using the Pearson feature selection algorithm and the Competitive Adaptive Reweighted Sampling (CARS) method, the Vegetation Index (VI) with the highest correlation was selected as a training feature depended on multispectral orthophoto images from unmanned aerial vehicle (UAV) and measured SPAD values. At various potato fertility stages, Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) inversion models were constructed. The models’ parameters were then optimized using the Grey Wolf Optimizer (GWO) and Sparrow Search Algorithm (SSA). The findings demonstrated a higher correlation between the feature selected VI and SPAD values; additionally, the optimization algorithm enhanced the models’ prediction accuracy; finally, the addition of the fertility stage feature considerably increased the accuracy of the full fertility stage in comparison to the single fertility stage. The models with the highest inversion accuracy were the CARS-SSA-RF, CARS-SSA-XGBoost, and Pearson-SSA-XGBoost models. For the single-fertility and full-fertility phases, respectively, the optimal coefficients of determination (R²s) were 0.60, 0.66, and 0.87, the root-mean-square errors (RMSEs) were 2.63, 3.23, and 2.39, and the mean absolute errors (MAEs) were 2.00, 2.75, and 1.99. Full article

Taking into account the requirements to reduce the release of harmful emissions into the environment, the EU’s environmental standards when transitioning to the Euro 7 standard in 2025 will actually lead vehicles having to operate without producing emissions in all driving situations. Carmakers believe that the new, much stricter regulations will mark the end of the internal combustion engine era. For example, in 2030, the manufacturer SEAT will cease its activities, leaving behind the Cupra brand, which will be exclusively electric in the future. This trend will apply not only to private vehicles (passenger cars), but also to utility vehicles, which is the subject of our research, namely the spatial tubular frame in the Bogdan ERCV27 garbage truck, presented in the form of a solid model. The peculiarity of the studied model is the installation of a battery block behind the driver’s cabin, causing an additional load to be placed on the spatial frame of the garbage truck, which in terms of its architecture is more like the body of a bus. During the conditions involving various modes of operation of a full-scale Bogdan ERCV27 garbage truck sample, questions about the strength and uniformity of its load-bearing spatial frame inevitably arise, which are decisive, even at the stage of designing and preparing the technical documentation. The main static load mode, which, despite its name, also covers dynamic conditions, was modeled using the appropriate coefficient k_d = 2.0. The maximum stresses on the model during the “bending” mode were 381.13 MPa before structure optimization and 270.5 MPa as a result of the improvement measures. The spatial frame mass was reduced by 4.13%. During the “torsion” mode, the maximum deformation values were 12.1–14.5 mm, which guarantees the normal operation of the aggregates and units of the truck. Full article