Search Results (317)

To address the oversteering risk during aircraft ground towing with a towbarless towing vehicle, this study proposes a dual-LiDAR point-cloud detection and pose estimation method for aircraft rear-wheel targets. First, a complementary dual-LiDAR acquisition strategy is adopted to reduce rear-wheel point-cloud occlusion caused by the aircraft nose landing gear and towing mechanism. Second, considering the small size and distinctive local geometry of rear-wheel targets, vertical density enhanced encoding and a lightweight CNN-Transformer BEV backbone are introduced into the PointPillars framework. The vertical density enhanced encoding explicitly describes the normalized height-wise distribution of valid points within each pillar, thereby improving the representation of cylindrical wheel structures. The CNN-Transformer BEV backbone incorporates a window-based self-attention Transformer module into deep features to strengthen local contextual modeling in the BEV space. Based on the detected coordinates of the left and right rear wheels, the aircraft fuselage pose is then estimated in combination with the TLTV coordinate system. In three-seed experiments on the fixed validation split, the Full model achieves an mAP@0.5 of $0.8788\pm 0.0161$, which is 8.50 percentage points higher than the original PointPillars baseline. The model contains 4.1069 M parameters and runs at 38.0732 FPS. The towing-angle estimation error remains within the allowable engineering range. These results show that the task-specific adaptations improve rear-wheel detection while retaining a compact model and real-time processing capability. Full article

Bridging the real-to-sim gap in low-cost autonomous mobile robotics requires careful cross-domain alignment of kinematic geometry, actuator behavior, and sensor characteristics. This paper presents a systematic Real-to-Sim parameter calibration and multi-stage experimental validation framework for a low-cost differential-drive unmanned ground vehicle (Jackson UGV) operating within NVIDIA Isaac Sim. The calibration process distinguishes initial product/design references, directly measured physical geometry, empirically adjusted ROS 2 runtime parameters, and simulation-specific PhysX parameters. By tuning virtual wheel geometry, inertial sensor profiles, and PhysX joint-drive damping, the proposed framework enables controlled comparison between physical execution and digital-twin behavior. Benchmark evaluations across three experimental stages—square waypoint-tracking trajectories, continuous figure-eight maneuvers, and dynamic obstacle avoidance in a mapped maze course—quantify rotational repeatability, temporal alignment, estimator consistency, and cross-domain trajectory deviation. The square and figure-eight trials reveal a proprioceptive “estimator optimism gap” in which onboard EKF estimates remain internally repeatable while underestimating terminal displacement relative to external floor measurements or simulator-provided reference poses. In Stage 3, 2D LiDAR-based localization and Nav2/DWB local planning reduce dependence on purely proprioceptive dead reckoning, achieving $100\%$ goal completion without observed collision events across both physical and virtual deployments. The results support the calibrated digital twin as a controlled simulation baseline for studying cross-domain navigation behavior and for future sim-to-real evaluation of autonomous mobile robot navigation algorithms. Full article

The increasing popularity of electric vehicles is inevitable due to their lower dependence on conventional fuel and reduced air pollution. Among various drivetrain architectures, four-wheel independently driven electric vehicles (4WID-EVs) have gained significant attention owing to their superior load-carrying and dynamic performance. However, the distributed four-motor architecture makes them vulnerable to unpredictable motor failures, necessitating an effective fault-tolerant control strategy. This work proposes a common sliding mode controller (CSMC)-integrated quantum complete graph neural network (QCGNN) for adaptive tuning under one-, two-, and three-motor failure conditions at reference speeds of 20 and 40 m/s, ensuring stable operation through continuous state feedback. Simulation results demonstrate fault recovery within 3 s, a rise time of 1.2–1.3 s, a settling time below 5.5 s, a peak overshoot below 8%, and a steady-state error below 0.2%. Compared with the QCGNN-Optimal LQR, the proposed QCGNN-CSMC reduces the Mean Absolute Error (MAE) from 34 to 18, Root Mean Square Error (RMSE) from 41 to 23, and the steady-state error from 1.07 to 0.56, while maintaining R² values above 0.90. Rapid controller prototyping further validates the robustness, reliability, and real-time applicability of the proposed fault-tolerant control framework for 4WID-EVs. Full article

As driving becomes more automated, the driver–car interaction is changing. The steering wheel, traditionally the main interface between the driver and the vehicle, is reduced to being a part of the environment in autonomous driving. Its influence on kinematic reactions to decelerations without a prompt to take over control of the vehicle is still unknown. Therefore, low-speed sled tests with seven female volunteers matching 5th or 50th percentile anthropometrics were performed, including three upright trials and two reclined trials at backrest angles of 23° and 45° during a standardized braking pulse. A steering wheel was placed in front of the volunteers to limit available space when moving forward. Kinematic analysis focused on the forward head movement. The study showed that no targeted hand grasping towards the steering wheel occurred in either backrest configuration. Head forward excursion was slightly higher in the first upright trial for some volunteers. Less variation and lower absolute forward excursion of the head were found in the reclined trials. Without the need for a required vehicle takeover, no grasping of the steering wheel was observed among the participants studied. Full article

During frequent starting and braking of metro vehicles, wheel–rail contact may change from pure rolling to full slip, and the frictional heat generated at the interface can induce localised rail damage such as rail burns. Because the motion state and the thermal input vary together in service, their separate contributions to the contact response are difficult to identify. To separate them, a three-dimensional elastic–plastic finite element model of a Type B metro wheel and rail was established and four cases were computed, forming a 2 × 2 factorial combination of two motion states (pure rolling and full slip) and two thermal states. The temperature field was imposed as a prescribed railhead boundary rising from 22 °C to 50 °C, applied identically under both motion states as a control variable, rather than solved from frictional heating. Under an 80 kN wheel load, the maximum rail equivalent stress of the four cases is 541.9, 596.3, 623.1 and 679.5 MPa, all exceeding the 457 MPa yield strength of U71Mn rail steel and indicating shallow localised plasticity in the contact patch. Full slip changes the peak contact pressure by less than 2% but raises the maximum rail equivalent stress by 14–15%, because the interface passes into full sliding and the high-stress zone moves towards the rail surface; the 28 °C temperature rise concentrates the contact and raises the stress by a further 9–10%. The factorial interaction term is +2.0 MPa, below 4% of either main effect. An analytical estimate shows the imposed thermal load to be a conservative lower bound for continuous sliding. Full article

Predicting the Remaining Useful Life (RUL) of railway wheels is challenging because wheel–rail degradation is cumulative, stochastic, and influenced by operating conditions. This study evaluates a multi-indicator prognostic framework using real in-service measurements acquired with a CALIPRI C42 optical profilometer (NextSense GmbH, Graz, Austria). The database comprises 80 wheels from ten vehicles of the same rolling-stock type, monitored during five monthly measurement campaigns, and includes flange width (Fw), flange height (Fh), and the flange-gradient dimension (qR). The Gamma process and first-passage formulation are established tools; the contribution of this work is their common application to all three indicators on the same in-service fleet and the benchmarking of long-horizon probabilistic results against an AR(1) short-term predictor embedded in the First-Passage Auto-Regressive (FP-AR) framework using the same dataset. Median Gamma-based RUL values were 21.2–22.0 months for Fw, 13.7–17.6 months for Fh, and 5.7–9.5 months for qR, with qR showing the largest relative percentile dispersion. For one-step prediction, the FP-AR benchmark achieved global MAE/RMSE values of approximately 0.368/0.502 mm for Fw and 0.0187/0.0216 mm for Fh; qR was more difficult to predict, with global MAE/RMSE values of approximately 0.575/0.991 mm. Under the adopted intervention thresholds, these results identify qR as the most variable and operationally constraining indicator under the studied Fès–Marrakech service conditions. The proposed dual-model analysis therefore provides a position-specific, uncertainty-aware basis for comparing wheel-profile degradation indicators, while its maintenance implications remain fleet- and route-specific pending validation in additional operating contexts. Full article

Distributed-drive electric vehicles (DDEVs) improve braking stability through independent four-wheel braking torque control, yet their complex multi-brake systems are prone to braking faults that cause loss of longitudinal and yaw stability. This study investigates DDEV braking performance under normal and faulty conditions and proposes a three-module braking stability control strategy consisting of demand torque calculation, fault constraint reconstruction, and fault-tolerant control. The fault constraint reconstruction module establishes braking capacity boundaries based on real-time fault and vehicle state data and constrains four-wheel braking torque via target projection. A composite fault-tolerant control scheme combining active front steering (AFS) and braking torque distribution is developed to suppress stability degradation. The AFS adopts a sliding-mode algorithm for accurate front wheel steering regulation, while a quadratic programming algorithm optimizes four-wheel braking torque allocation. Hardware-in-the-loop simulations are conducted for straight line and double lane change braking under single wheel regenerative, mechanical, and complete braking failure conditions. The results reveal that the proposed strategy limits peak yaw rates to 1.1 deg/s, 2.8 deg/s, and 3.5 deg/s in faulty regenerative, mechanical, and complete straight line braking, respectively, and achieves excellent yaw rate and trajectory tracking during faulty double lane change braking. This work provides an effective solution for DDEV braking stability optimization and fault-tolerant control. Full article

Lane-Keeping Assist Systems (LKAS) are widely used in modern passenger vehicles to support lateral vehicle control and reduce the risk of unintended lane departure. Although LKAS performance is commonly evaluated in relation to perception, control, and sensor fusion, the observable vehicle response may also depend on the mechanical condition of the chassis. This study presents a qualitative, measurement-based evaluation of the influence of intentionally introduced front-wheel toe misalignment on the observable response of a production LKAS under controlled proving-ground conditions. Experimental tests were conducted on the highway module of the ZalaZONE proving ground using a Lexus RX 450h equipped with a factory-installed LKAS function. Three front-wheel toe configurations were investigated: factory-specified alignment, single-wheel toe misalignment, and severe toe misalignment affecting both front wheels. Measurements were performed at 70, 90, and 110 km/h on straight and curved road sections. Vehicle speed, steering angle, lateral acceleration, and GNSS-based position data were recorded using CAN- and GNSS/IMU-based data acquisition. The qualitative comparison of the measured signal profiles indicated that the misaligned configurations were associated with a shifted steering-angle operating range and less uniform steering and lateral-acceleration responses. The most pronounced visible differences occurred under the severe toe-misalignment condition, particularly at higher speeds and in the curved section. As the analysis did not include quantitative effect measures or statistical comparisons, these observations are interpreted as exploratory tendencies rather than statistically validated changes in LKAS performance. The findings suggest that front-wheel toe condition should be considered in the measurement-based assessment, maintenance, and calibration of ADAS-equipped vehicles. Full article

The American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) live-load distribution-factor (DF) equations were calibrated on straight bridges, while their use for horizontally curved I-girder bridges is bounded by the L_as/R < 0.06 rad criterion in Article 4.6.1.2.4b of the AASHTO LRFD Bridge Design Specifications, 10th ed. (2024). This study quantifies their accuracy beyond that limit using the finite element method (FEM) in 35 three-dimensional CSiBridge models subjected to numerical consistency checks: three composite plate-girder arrangements (4–6 girders, 9.0 m deck) at central angles of 0–15°, with near-limit, span-transfer, sensitivity, and out-of-range extensions to 25°, under the AASHTO LRFD vehicular design live-load model (HL-93) and the Iraqi Class 100 wheeled military vehicle (HB115; 1150 kN). At the limit, curvature amplification is only 1.8–2.6%. Beyond it, the exterior-moment equations become unconservative almost immediately; FEM demand exceeds AASHTO by 21–29% at 15°, whereas the interior-shear equations remain conservative. A two-part correction factor (CF) of the form CF = R₀[1 + (a + a₁S/L)(L/R)] is proposed (R² ≈ 0.97) and predicts the withheld out-of-range cases within 3.3%. Within the tested envelope, exterior-girder amplification depends primarily on L/R; for HB115, its rate is about half that of HL-93. Direct CSiBridge reconstruction of two published 1/10-scale laboratory specimens shows good agreement in global deflection and moderate agreement in strain-based transverse distribution. Because full-scale measurements for the exact 38 m reference configuration were unavailable, this evidence is treated as external experimental benchmarking of the modeling methodology rather than complete validation of the full parametric matrix. Full article

As vehicle intelligence and automotive electrification advance, the extensive deployment of distributed sensing nodes for comprehensive monitoring has grown rapidly. This poses severe challenges, such as rising onboard power consumption and the inability of conventional centralized power supply systems to sustain these sensors. Triboelectric nanogenerators (TENGs), an emerging technology for energy harvesting and self-powered sensing, exhibit great potential to address the above challenges. This review systematically summarizes research on TENGs for vehicle energy harvesting and intelligent sensing, covering their fundamental working principles and applications in diverse vehicle scenarios. First, the basic principle and working modes of TENGs are described, and their suitability for complex and variable vehicle environments is evaluated. Subsequently, existing applications are categorized into three domains: vehicle vibration systems, wheel–road systems, and intelligent vehicle systems. Studies on various topics are reviewed, including vibration energy harvesting and sensing, vehicle collision monitoring, tire energy harvesting, road sensing, smart cockpits, human–machine interaction, and vehicle fluid monitoring. Emphasis is placed on their technical approaches and application prospects. Finally, the state-of-the-art research and prevailing technical bottlenecks are summarized, and potential solutions and future research perspectives are discussed. This review aims to support the reliable practical deployment of TENG technology in vehicle engineering and to provide a technical basis for energy-saving strategies and in situ sensing technologies for future intelligent vehicles. Full article

This paper presents the development of a multi-domain simulation for the Porsche Taycan’s all-wheel-drive (AWD) electric propulsion system to investigate the impact of the rear drive unit’s two-speed transmission on performance and drive quality during maximum acceleration. This study was undertaken independent of the vehicle and propulsion system OEM. A lumped-parameter model of the front and rear electric drive units (EDU) and the high-voltage battery was developed and calibrated against the published data for key benchmarks, including 0–100 kph acceleration times and peak longitudinal acceleration. The mechanical shifting mechanism was reverse-engineered to simulate high-performance shift trajectories. To manage the transition, a clutch control scheme integrates a reduced-order clutch-to-clutch model featuring a feedforward (FF) torque estimator and a closed-loop feedback (FB) controller to achieve target input shaft speeds and shift durations. The study concludes with a comprehensive analysis of the propulsion system’s behavior at a battery state of charge of 96% and 25% and three electric motor speeds at which the upshift is commanded. The simulation results demonstrate that executing an early upshift at 10,700 rpm with 96% of SOC yields a 0.100-s inertia phase shift time, restricts the clutch thermal dissipation to 21 kJ, and achieves an 8-s velocity of 203.4 kph, outperforming the upshift at 15,300 rpm (0.210 s, 34 kJ, and 202.8 kph). Furthermore, the transient regenerative braking on the rear axle during the inertia phase reduces the peak current draw from 675 A to 87 A, recovering the DC bus voltage to enable cross-axle torque boosting on the front axle. Full article

Passenger Car Equivalent (PCE) factors are widely used to convert heterogeneous traffic streams into equivalent homogeneous flow rates for the design and analysis of roads and intersections. In developing countries, mixed traffic conditions differ substantially from those in developed contexts due to variations in vehicle composition, operating characteristics, roadway parameters, and environmental conditions. Consequently, PCE values are highly context-specific and require periodic updates to accurately represent prevailing traffic conditions. However, such updates are often infrequent because conventional PCE estimation relies on extensive field data collection through time-consuming and costly traffic surveys, as well as the availability of experienced experts to conduct and validate the analyses. In Sri Lanka, the currently adopted PCE factors are more than two decades old and no longer reflect existing traffic conditions. Although several recent studies have estimated PCE values for mid-block roadway sections of various facility types (e.g., four-lane roads, two-lane roads, and freeways), no study has comprehensively addressed intersections, which are critical for signal timing and geometric design. This study aims to develop a systematic methodology for estimating intersection-specific PCE factors using drone-based video data. Traffic data were collected at selected intersections using an unmanned aerial vehicle to obtain an accurate bird’s-eye view of vehicle movements. The methodology compares the area occupancy of different vehicle categories under varying traffic compositions with that of a passenger-car-only traffic stream operating at the same average speed. Using the extracted traffic parameters, the basic headway method was applied to establish a framework for calculating PCE factors. PCE values were estimated for ten vehicle categories, and the results reveal significant deviations, particularly for three-wheelers, motorcycles, and commercial vehicles, when compared with values currently in use. A high-level comparison with studies from other developing countries in the South Asian region indicates notable differences in vehicle impacts at signalized intersections in Sri Lanka. Furthermore, the proposed methodology provides a practical, economical, and less labour-intensive approach for estimating PCE factors, enabling more frequent updates without requiring extensive field surveys or specialized expertise. Because it relies on a straightforward headway-based framework and drone-derived traffic data, the methodology can be readily adapted to different roadway facilities, including highways, rural roads, and intersections, making it suitable for application across diverse geographical regions. Full article

This paper presents the research, development, and functional validation of an original rear suspension system designed for hybrid reverse trike vehicles (two guided wheels on the front axle and a twin-tire-driven assembly at the rear). Conventional configurations featuring a single rear wheel exhibit severe limitations regarding lateral stability under critical dynamic regimes and induce roll-induced torsional loading in flexible chain drives. The proposed solution utilizes a twin-tire rear assembly integrated into an articulated suspension mechanism with two degrees of freedom (2 DoF), which reconfigures the geometric stability polygon from a triangle into an isosceles trapezoid. A mathematical model based on tire dynamics and tire slip phenomena demonstrates that introducing a controlled roll stiffness on the rear axle stabilizes the slip angles, ensuring a neutral and predictable steering behavior. Structural validation via finite element analysis (FEA) performed in SOLIDWORKS Simulation on the entire assembly under a conservative combined load scenario (2400 N vertical force shared by the two wheel bearings, 2400 N lateral force, and 1200 N tractive force) indicated a minimum factor of safety of 1.26 on S275N structural steel, confirmed by an eleven-run mesh independence study. Finally, the system’s functionality was experimentally confirmed through the manufacturing and road testing of a full-scale (1:1) demonstrator vehicle powered by an 1129 cc Boxer engine, highlighting a measurable increase in rollover resistance and trouble-free operation of the two-stage chain drive throughout the test program. A numerical evaluation shows that for rear-biased vehicles of the category the proposed axle raises the rollover-related lateral acceleration threshold by up to 54% and replaces the strongly oversteering balance of the single-wheel layout with a near-neutral, tunable one. Full article

This study quantifies whether vehicle electrification reduces greenhouse gas emissions on one of the world’s most fossil-intensive electricity grids. A transparent, ISO 14040/14044-conformant cradle-to-grave life cycle assessment compares a mid-size battery electric vehicle (BEV, 60 kWh) with a comparable gasoline car over 225,000 km, using a fully source-traceable process-sum inventory and life cycle (well-to-wheel) emission factors for both energy carriers. On the 2024 Saudi grid (692 g CO₂e/kWh, 99.8% fossil) the BEV emits 37.8 t CO₂e (168 g CO₂e/km) against the gasoline car’s 50.6 t (225 g CO₂e/km)—a 25% reduction, with the BEV’s 1.9 times higher production emissions repaid at 76,000 km, approximately three years of typical Saudi driving. The advantage rises to 44% on the world-average grid, 53% under Saudi Arabia’s 50% renewable-electricity target for 2030, and 66–80% on the EU and French grids; grid parity would require 991 g CO₂e/kWh, above any national grid. The result is robust to hot climate energy consumption (+15%, advantage 25%), Gulf-sourced materials (break-even shortens to 68,000 km), battery capacity (40–80 kWh), and 10,000-run Monte Carlo uncertainty propagation (BEV superior in 99.6% of draws). Electrification is therefore a sound climate strategy even in fossil-grid economies, and its benefit roughly doubles with the announced power-sector transition. Full article

Expansion joint anchorage zones are prone to premature cracking and fatigue deterioration under repeated wheel impact and interfacial stress concentration. Engineered cementitious composites (ECCs) are promising anchorage materials because of their tensile strain-hardening behavior, multiple fine cracking, and high deformation capacity. However, how ECC strength–ductility characteristics affect vehicle-induced stress redistribution and fatigue damage accumulation remains unclear. This study develops a material–structure–fatigue framework for ECC anchorage zones. Three PVA-ECC mixtures were tested, and their measured constitutive relationships were incorporated into a three-dimensional vehicle–expansion joint coupled finite element model validated using reported field strain data from a C50 concrete anchorage zone. Critical tensile stress histories were extracted for rainflow counting and Miner-based fatigue assessment. Results show that ECC reduced tensile stress concentration and increased tensile safety margins compared with C50 concrete. Under the defined loading scenario, the estimated fatigue life increased from 9.93 years for C50 concrete to 83.15 years for the best-performing ECC scheme. Ten-year comparative field observations supported the predicted durability trend. By linking ECC strength–ductility characteristics with vehicle-induced stress redistribution and cumulative fatigue damage, the proposed framework provides a quantitative basis for fatigue-resistant material selection and durability-oriented design of expansion joint anchorage zones. Full article