Vehicle Configuration Research Articles

A tilt-wing VTOL UAV configuration: Flight dynamics modelling and transition control simulation

AbstractThis paper aims to present a vertical take-off and landing unmanned aerial vehicle (VTOL UAV) configuration and numerically simulate its flight transition from hover to cruise and from cruise to hover. It can tilt the canard and wing along with two attached propellers. Additionally, two fixed front propellers are pointing upwards. Multi-body equations of motion are derived for this concept of aircraft, which are used to compute the flight transition trajectory from hover to cruise configuration. Furthermore, a transition control algorithm based on gain scheduling is described, which stabilises the aircraft while it accelerates from hover to cruise, gradually tilting the wing along with its propellers, sequentially switching between equilibrium states, as the stability cost functions thresholds are reached. The transition control algorithm of the conceptual aircraft model is numerically simulated.

State of the art and future trends in obstacle-surmounting unmanned ground vehicle configuration and dynamics

AbstractThis article presents a review of the platform configuration and dynamic of obstacle-surmounting unmanned ground vehicles (UGVs). For now, unmanned systems have emerged as a result of the rapid advancement of artificial intelligence and modern manufacturing techniques both domestically and internationally. The research on unmanned systems has been improved a lot. The UGV platform can execute transportation, recurring, and military tasks independently. For the high-level self-control, adaption, and maneuverability abilities, the UGV platform has been applied in the military, industry, and other special fields widely. The UGV platform usually performs tasks in an unstructured environment, so the all-terrain performance becomes a key factor restricting their operating efficiency and reliability. A brief literature review of the UGV platform is carried out in this article.

Machine learning-based electro-magnetic field guided localization technique for autonomous underwater vehicle homing

Precise guidance is the primary requirement for the reliable homing of autonomous underwater vehicles (AUV). The necessity for accurate short-range position tracking arises due to the limitations of the inertial sensors’ cumulative integral drift, unreliable acoustic positioning system in very short ranges, and non-propagation of high-frequency electromagnetic signals of the GPS system. In this article, a very low-frequency electro-magnetic (EM) field is generated for very short-range guidance for the homing operation of underwater vehicles. The design and modeling of the 3-axis low-frequency EM-based shallow water submerged Docking Station (DS) at 10 m depth is explained and the EM field is generated for a 20 m range in the 3-axis plane using two perpendicular coils operated in distinct frequencies. The stochastic magnetometer measurement model is used to estimate the relative kinematics of the underwater vehicle with respect to the EM docking coil. The EM field datasets generated from EM field maps around DS are used for training and validating with different Machine Learning (ML) algorithms. The designed ML aided EM-based tracking technique guidance algorithms are validated for underwater vehicle localization that requires a precise and reliable homing process to the DS even in turbid waters, bio-fouling environment, and reflective boundaries though it has limitations in presence of ambient ferromagnetic sources. Numerical simulations have been carried out with conventional methods and ML algorithms on the generated EM data. The results indicate that the position accuracy achieved is within 1% for ML-aided EM based guidance techniques when compared to the conventional algorithm which has a position accuracy of 3%. ML aided techniques are advantageous as they are independent of the dynamic ocean environment and the underwater vehicle configuration and the Time to Home (ToH) to the DS is simulated in different scenarios, is 160 s–200 s with a vehicle speed of 0.25 m/s. It is observed that the vehicle can choose the shortest path for homing operation with the measured EM field values and with aid of ML techniques.

An Extended Bridge Weigh-in-Motion System without Vehicular Axles and Speed Detectors Using Nonnegative LASSO Regularization

The bridge weigh-in-motion (BWIM) technique uses the instrumented bridge on a large scale to identify the axle weight of a passing vehicle. Vehicle configurations, e.g., axle number and wheelbase, are crucial for the BWIM system, which require additional axle detectors. Free of axle (FAD) sensors are often used to obtain vehicle information, but they are only suitable for specific bridge types, such as slab-girder bridges. The concept of a virtual-axle-based algorithm, without requiring axle detectors, has been developed, and the validity of this algorithm has been verified numerically and experimentally. However, this algorithm assumes the vehicle speed as a known input, indicating that additional speed sensors/devices are still required in the BWIM system. Using this virtual-axle-based algorithm in a field test, it is found that the identification accuracy of the BWIM system is sensitive to the vehicle speed, and it shows poor recognition of vehicle configuration. To improve the recognition accuracy and remove vehicle speed detectors from the BWIM system, an extended BWIM system is proposed using the regularization technique and iterative approach. Both vehicular virtual axles and speeds are assumed in this approach. An error function based on the measured responses and theoretical ones is built to evaluate these assumed vehicle configurations and speeds. The effectiveness of the proposed approach is verified by the field tests. The results show that the proposed approach can obtain high recognition accuracy, which is close to Moses’s algorithm using FAD sensors. Compared with the previous virtual-axle-based algorithm, the recognition accuracy and robustness of the proposed approach are greatly improved. The proposed approach is still challenged by real-world traffic because this paper only considers the case when a single vehicle passes over the bridge. Nevertheless, the proposed extended BWIM system shows potential practical applications as it can further reduce costs and be applicable to more bridge types.

Application of Inverse Optimal Formation Control for Euler-Lagrange Systems

This paper studies the inverse optimal formation control problem for heterogeneous Euler-Lagrange systems. In order to reduce the impacts of surface vehicles configuration and surface vehicles type on transportation efficiency, the nonlinear terms in the system models are considered. Then, an inverse optimal formation controller is proposed by using leader-follower formation approach and the inverse optimal stability theory, which ensures all signals of considered system are semi-globally uniformly ultimately bounded (SGUUB). And the controller can also minimize the cost function. Finally, a simulation example is given to verify the effectiveness of the designed control method.

Vehicle-following behaviour in mixed traffic – role of lane position and adjacent vehicle

ABSTRACT In mixed traffic condition, varying vehicle dimensions and lack of lane discipline lead to parallel movement of vehicles in the same lane or between lanes. This results in a condition where the longitudinal response of vehicles gets affected by adjacent vehicles and their configurations. Correspondingly, the adjacent vehicle configurations are significantly influenced by the lane position of subject vehicle. To accommodate this scenario, the study formulates longitudinal acceleration models from trajectory data considering the influence of subject vehicle’s lane position along with adjacent vehicle characteristics. The response under different cases of subject vehicle’s lane position and adjacent vehicle configurations are evaluated. The statistical analysis indicates that the following behavior varies based on lane position of subject vehicle and adjacent vehicle attributes. It is also found that disregarding these attributes can produce significantly erroneous acceleration estimates. These features can improve existing following behavior models and can enhance the realism of the microscopic modeling scheme for mixed traffic conditions.

SCP model based on the new energy vehicle collaborative detection configuration method research

Abstract To address the problem of inaccurate positioning of new energy vehicles during cooperative detection, this paper investigates the positioning accuracy of different combinations of radar and infrared sensors. In order to meet the control requirements of the vehicle co-detection formation, a distributed control method considering the vehicle dynamics characteristics is adopted for this purpose. And considering the cost of sensors, the optimal parameters are calculated, and an optimal aircraft co-detection configuration design scheme based on the SCP model (structure-behavior-performance model) is proposed. At the working level of multi-craft cooperative detection guidance, a two-layer cooperative guidance structure is designed based on the basic framework. Research on vehicle configuration design, vehicle data transmission, processing technology, and cooperative detection and guidance is carried out. The basic principles of the particle swarm optimization algorithm and the algorithmic flow of the discrete particle swarm algorithm are applied to calculate the assignment of targets by the particle swarm optimization algorithm. The GDOP values are analyzed according to the simulation results, and it can be concluded that: the change rate of 2~5 sensors accuracy can reach 71.1%, 71.6%, and 71.5%, respectively, when the infrared sensor spacing is 1km~3km. 5~10 sensors accuracy change rate is only 53.3%, 52.2%, and 49.7%, respectively, which shows that the value is improved by 20%, indicating that the collaborative sensing accuracy of the aircraft has been effectively improved, which is of great help to the collaborative detection of new energy vehicles based on SCP model.

Thoracic responses and injuries to male postmortem human subjects (PMHS) in rear-facing seat configurations in high-speed frontal impacts

Objective: One potential nonstandard seating configuration for vehicles with automated driving systems (ADS) is a reclined seat that is rear-facing when in a frontal collision. There are limited biomechanical response and injury data for this seating configuration during high-speed collisions. The main objective of this study was to investigate thoracic biomechanical responses and injuries to male postmortem human subjects (PMHS) in a rear-facing scenario with varying boundary conditions. Method: Fourteen rear-facing male PMHS tests (10 previously published and 4 newly tested) were conducted at two different recline angles (25-degree and 45-degree) in 56 km/h frontal impacts. PMHS were seated in two different seats; one used a Fixed D-Ring (FDR) seat belt assembly and one used an All Belts To Seat (ABTS) restraint. For thoracic instrumentation, strain gages were attached to ribs to quantify strain and fracture timing. A chestband was installed at the mid-sternum level to quantify anterior-posterior (AP) chest deflections. Data from the thorax instrumentation were analyzed to investigate injury mechanisms. Results: The PMHS sustained a greater number of rib fractures (NRF) in the 45-degree recline condition (12 ± 7 NRF for ABTS45 and 25 ± 18 NRF for FDR45) than the 25-degree condition (6 ± 4 NRF for ABTS25 and 12 ± 8 NRF for FDR25), despite AP chest compressions in the 45-degree condition (−23.7 ± 9.4 mm for ABTS45 and −39.6 ± 11.9 mm for FDR45) being smaller than the 25-degree condition (−38.9 ± 16.9 mm for ABTS25 and −55.0 ± 4.4 mm for FDR25). The rib fractures from the ABTS condition were not as symmetric as the FDR condition in the 25-degree recline angle due to a belt retractor structure located at one side of the seatback frame. Average peak AP chest compression occurred at 45.7 ± 3.4 ms for ABTS45, 45.6 ± 3.1 ms for FDR45, 46.7 ± 1.9 ms for ABTS25, and 46.9 ± 2.3 ms for FDR25. Average peak seatback resultant force occurred at 43.9 ± 0.9 ms for ABTS45, 44.6 ± 0.8 ms for FDR45, 42.5 ± 0.2 ms for ABTS25, and 41.5 ± 0.5 ms for FDR25. The majority of rib fractures occurred after peak AP chest compression and peak seatback resultant force likely due to the ramping motion of the PMHS, which might create a combined loading (e.g., AP deflection and upward deflection) to the thorax. Although NRF in the 45-degree reclined condition was greater than the 25-degree recline condition, similar magnitudes of rib strains were observed regardless of seat and restraint types, while strain modes varied. Conclusions: The majority of rib fractures occurred after peak AP chest compression and peak seatback force, especially in FDR25, ABTS45, and FDR45, while the PMHS ramped up along the seatback. AP chest compression, seatback load, and strain measured along the rib could not explain the greater NRF in the 45-degree recline conditions. A complex combination of AP chest deflection with upward deflection was discovered as a possible mechanism for rib fractures in PMHS subjected to rear-facing frontal impacts in this study.

Empirical Equations of Changes in Aerodynamic Drag Based on Direct On-Track Road Load Measurements for Multi-Vehicle Platoons

<div class="section abstract"><div class="htmlview paragraph">Considerable effort is currently being focused on emerging vehicle automation technologies. Engineers are making great strides in improving safety and reliability, but they are also exploring how these new technologies can enhance energy efficiency. This study focuses on the changes in aerodynamic drag associated with coordinated driving scenarios, also known as “platooning.” To draw sound conclusions in simulation or experimental studies where vehicle speed and gaps are controlled and coordinated, it is necessary to have a robust quantitative understanding of the road load changes associated with each vehicle in the platoon. Many variables affect the drag of each vehicle, such as each gap length, vehicle type/size, vehicle order and number of vehicles in the platoon. The effect is generally understood, but there are limited supporting data in the literature from actual test vehicles driving in formation. This study uses a practical approach to quantify road load changes in each vehicle using axle torque sensors to directly measure the load required to maintain steady-state speeds while in different platoon configurations (compared to driving solo). Three test vehicles were used, which varied in size and type: a passenger car, a mid-size SUV, and a full-size pickup truck. A control computer in each vehicle sent commands to the vehicle accelerator input to control the speed and gap based on radar feedback. The final product of the test program is a set of empirical equations that estimate drag changes for two- and three-vehicle platoons at highway speeds (88-112 km/hr) with varying vehicle and platoon configuration parameters.</div></div>

Research on Adaptive Distribution Control Strategy of Braking Force for Pure Electric Vehicles

The actual driving conditions of electric vehicles (EVs) are complex and changeable. Limited by road adhesion conditions, it is necessary to give priority to ensuring safety, taking into account the energy recovery ratio of the vehicle during braking to obtain better braking quality. In this work, an electric vehicle with an EHB (electro-hydraulic braking) system whose braking force adaptive distribution control strategy is studied. Firstly, the vehicle dynamics model, including seven degrees of freedom, tire, drive motor, main reducer, battery pack, and braking system, was constructed, which is attributed to the vehicle configuration and braking system scheme. Second, based on curve I and ECE regulations, the adaptive braking force distribution control strategy was formulated by taking the maximum regenerative braking torque as the inflection point, the synchronous adhesion coefficient as the desired point, and the battery SOC, road adhesion coefficient, and braking strength as the threshold. Finally, the vehicle dynamics simulation model was built on the Matlab/Simulink platform, and the simulation results verified the feasibility of the proposed braking force adaptive allocation control strategy. The research shows that the adaptive distribution control strategy can better adapt to the complex and variable driving conditions of the vehicle by combining the inflection point and the desired point. The braking energy recovery ratios of the vehicle under the NEDC and NYCC cycle conditions on a high adhesion road are 52.62% and 47.45%. The braking force distribution curve is close to curve I under the low adhesion extreme road.

Evaluation of plug-in hybrid vehicles in real-world conditions by simulation

Assessing the real-world energy performance and emissions of Plug-in Hybrid Vehicles (PHEVs) is complex: it depends on their usage (trip distance, recharging behavior), and results in different combined uses of their thermal and electric propulsion.In this study, vehicle simulators were calibrated using experimental data (in-lab and on-road), allowing a comprehensive range of uses spanning vehicle configurations, battery capacity, outside temperature and driving profiles. These results were synthetized through a method weighting each simulated use-case according to their probability, based on statistics of daily distance travelled and temperature. The assessment was made for a wide range of battery capacity and recharging frequency, and provided the real-world share of electric drive, CO2 emissions, fuel and electricity consumptions of PHEVs according to these two key parameters. Finally, in a very likely battery-constrained environment, PHEVs should be fostered to minimize GHG emissions providing that they are recharged at least every-five driving days.

URANS Simulations of Vehicle Exhaust Plumes with Insight on Remote Emission Sensing

Remote Emission Sensing (RES) is a measurement method based on absorption spectroscopy for the determination of pollutant concentrations. The absorption of the exhaust plume of a vehicle is measured from the roadside without intervention by means of a light/laser barrier during a short measurement (∼0.5 s) and concentration ratios of pollutants (e.g., NOx to CO2) are estimated. Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations of exhaust plumes in vehicle wakes are performed using the k-ω SST turbulence model with focus on pollutant dispersion. The simulation setup has been validated by a comparison with experimentally obtained drag coefficients. The resulting concentration fields represent the pollutants available for measurements by a RES device. The influence of the characteristics of the RES device on the measurement is assessed. In addition, investigations involve several environmental and vehicle related parameters. The results demonstrate that due to strong turbulence, mixing is enhanced and the exhaust plumes rapidly disperse in the near vehicle wakes. Results show that emission characteristics of a vehicle are contained downstream for approximately half the vehicle length, regardless of different vehicle configurations, driving and ambient parameters. Further downstream dispersion of pollutants results in concentrations that are less than 1/100 of the pollutant concentration in the vehicle’s exhaust tailpipe implying that RES devices have to measure at a high sampling frequency. Therefore, reliable determination of the concentration ratios of pollutant at high vehicle velocities requires the RES device to operate in the order of 1000 Hz sampling frequency. Ultimately, the numerical simulations not only help to understand exhaust plume dispersion, but provide a very useful tool to minimize RES uncertainties.

ML-based vehicle downtime reduction: A case of air compressor failure detection

This paper addresses the critical issue of reducing the downtime of vehicles by using machine learning (ML) techniques and full life cycle data. The study tests the failure prediction of one major automotive system, air compressors in long-distance trucks. To validate the learning capabilities of ML models, three different algorithms such as C5.0, C5.0 with boosting and classification and regression tree (CART) are used. The findings suggest that the C5.0 model with boosting provides a better prediction of air compressor failure than other decision trees such as CART and C5.0. The diagnostic trouble codes (DTCs) and the vehicle operational variables are important indicators of the air compressor failure whereas the vehicle configuration data are not meaningful. A hybrid model which includes both DTCs and vehicle operational data is able to generate superior prediction results.

Experimentally flight performances comparison of octocopter, decacopter and dodecacopter using universal UAV

This study presents a comparative analysis of different unmanned aerial vehicle (UAV) configurations using a proposed universal UAV. The octocopter, decacopter, and dodecacopter were experimentally evaluated through point tracking flight performances and vibration level analyses. To the best of our knowledge, this is the first study to conduct a comparative analysis of these configurations on the same platform. Our findings indicate that the dodecacopter exhibited the best overall performance, whereas the decacopter showed the lowest roll angle error. Notably, the implemented decacopter exhibited the highest pitch angle error and north position error due to its asymmetric rotor placement in the pitch axis. We observed that the octocopter demonstrated the best vibrationless flight performance, while increasing vibration magnitude was observed with an increase in the number of rotors.

Numerical and Experimental Determination of the Wind Speed Value Causing Catastrophe of the Scissor Lift

The current work is devoted to the numerical determination of the wind speed value, which can cause the overturning of the mobile elevating work platform of the scissor lift type. In the first step of the analysis, the scaled model of the real vehicle is prepared. In the second step, the model is used in the aerodynamic tunnel to determine the aerodynamic force values and moment, which act on the vehicle. The three different configurations of the work platform are considered, namely: (a) The work platform raised to the maximum height with an additional bridge extended, (b) the work platform raised to the maximum height, and (c) the work platform half raised. In each position, the direction of the wind is changed from the range from 0° to 180° with an increment equal to 15°. In the next step of the analysis, the CFD simulations are carried out. The ANSYS Fluent R22 software is used. As a model of turbulent airflow, the standard k-ε with standard wall function is adopted. The obtained experimental results are used to verify the numerical model. A very good agreement between the results of the experiment and the results of numerical simulations is obtained. As the main result of the numerical study, the values of the tipping moment and corresponding wind speed that cause the overturning of the analyzed real scissor lift are determined. It occurred that the lowest value of the wind speed is obtained for the first variant of the vehicle configuration V1crt = 22.315 m/s for the angle of the wind speed direction β = 30° and the highest one for the third variant V3crt = 34.534 m/s and β = 15°, without any persons on the work platform. The presence of human beings on the work platform is also considered.

The Impact of Gamifications and Serious Games on Driving under Unfamiliar Traffic Regulations

Drivers face many challenges when driving under unfamiliar traffic regulations, which may lead to a reduction in road safety. The need to adjust to different traffic rules could be a major factor toward a safer drive. Gamification is a promising way to enhance the user engagement in non-game tasks. In this paper, we hypothesize that gamification can improve driving performance and minimize the number of driving errors when driving under unfamiliar traffic regulations and thus enhance road safety. A game was designed to provide gamification elements in a simulated driving environment with unfamiliar traffic regulations where the players were motivated to reach the target with no errors. In the experiments, 14 participants who were not familiar with the designed traffic regulations were asked to drive a car simulator in two scenarios. The first scenario had no gamification elements, whereas the second one included gamification elements. The results indicated that gamification significantly helped the participants to drive in the correct traffic flow with the proper use of vehicle configuration. Our findings show that gamified simulation is a reasonable method to adjust the required driving performance and behavior to safely drive under unfamiliar traffic regulations.

X-CAR: An Experimental Vehicle Platform for Connected Autonomy Research

Autonomous vehicles promise a future with a safer, cleaner, more efficient, and more reliable transportation system. However, the current approach to autonomy has focused on building small, disparate intelligences that are closed off to the rest of the world. Vehicle connectivity has been proposed as a solution, relying on a vision of the future where a mix of connected autonomous and human-driven vehicles populate the road. Developed by the U.S. Department of Transportation Federal Highway Administration as a reusable, extensible platform for controlling connected autonomous vehicles, the CARMA Platform is one of the technologies enabling this connected future. Nevertheless, the adoption of the CARMA Platform has been slow, with a contributing factor being the limited, expensive, and relatively old vehicle configurations that are officially supported. To alleviate this problem, we propose X-CAR (eXperimental vehicle platform for Connected Autonomy Research). By implementing the CARMA Platform on more affordable, high quality hardware, X-CAR aims to increase the versatility of the CARMA Platform and facilitate its adoption for research and development of connected driving automation.

Structural damage detection of the bridge under moving loads with the quasi-static displacement influence line from one sensor

As the bridges continue to age, it is imperative to identify structurally deficient bridges at an early stage to prevent unexpected maintenance needs. A quasi-static method is developed to identify structural damage based on the influence line (IL) from one sensor and an empirical Bayesian threshold estimator. Initially, the empirical Bayesian threshold estimator is utilized to preprocess the measured bridge displacement response to eliminate vehicle-induced dynamic effects. Subsequently, a regularization technique using Elastic Net and displacement response is used for IL estimation, and the Elastic Net that integrates with both L1 and L2 norms is adopted to establish the objective function of IL estimation to deal with the ill-posedness of the inverse problem for specific vehicle configurations. Finally, according to the mechanical relationship between the moving load and the displacement IL, an IL area-based difference index is established to identify the presence of the damage rapidly, raising a flag that leads to a more in-depth investigation to gather information related to structural integrity. The feasibility of the proposed method is verified with the numerical model and laboratory tests. This study introduces innovative contributions in the following three aspects: (1) Elastic Net that integrates with both L1 and L2 norms is adopted to establish the objective function for IL estimation, (2) A single sensor provides a cost-effective and less computationally expensive solution than multi-sensor methods, (3) Compared to other wavelet-based methods, the comparative studies demonstrate that empirical Bayesian threshold estimator can identify damage more clearly.

A Computational Investigation of the Hover Mechanism of an Innovated Disc-Shaped VTOL UAV

Inventive approaches are constantly being revealed in the field of vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) configuration concepts and designs. To date, a body-associated configuration of UAVs for augmented lift remains unclear among other approached designs. The current paper investigates the mechanism of a high-lift ducted fan mounted in the central body for VTOL UAV designs. We report an unresolved design of a disc-shaped UAV with a single rotor that aims to enhance the cost-effectiveness of fuel consumption with a substantial contribution of body lift to hover thrust. The convex upper surface curvature was applied to generate a significant lift contribution from the body during hover. The computational fluid dynamics (CFD) approach based on unstructured discretization followed by three-dimensional steady Reynolds-averaged Navier–Stokes (RANS) flow was applied in ANSYS CFX to mechanistically investigate the underlying design considerations. The disc-shaped UAV uses the lip curvature on the duct inlet to generate a vertical force that demonstrates a significant contribution of 95% of the rotor thrust during hovering. The UAV’s upper surface generates prolonged flow entrainment free from momentum losses in swirling flows. This phenomenon is followed by reduced power consumption in hovering and vertical flight, making the UAV aerodynamically stable and environmentally safe.

Identification and Reduction of Interactional Noise of a Quadcopter in Hover and Forward Flight Conditions

Advanced Air Mobility is a vision for a safe, accessible, and sustainable aviation system to transport people and packages between places not served by traditional aviation. With this emerging transportation industry, there is motivation to characterize the noise of vehicles to determine their potential impacts on the community. An experimental testing campaign was conducted on a small unmanned aerial vehicle in the NASA Langley Low Speed Aeroacoustic Wind Tunnel as a continuation of a previous testing campaign. The goals of the current test are to identify sources of interactional noise as well as to test custom designed rotors and noise reduction devices. The tested noise reduction methods involve increasing the vertical distances between the rotors and the vehicle airframe as well as between the forward and aft rotor disk planes. These methods are intended to reduce rotor-airframe interaction noise in hover and fore-aft rotor wake ingestion noise in forward flight. A phased microphone array is also utilized to identify the locations of prominent noise generation for the different vehicle configurations in forward flight. Elevation of the rotors from the vehicle airframe yielded up to an 8 dBA noise reduction in forward flight, while yielding only modest noise reductions in hover.