Types Of Maneuvers Research Articles

Cooperative, Connected and Automated Mobility Service Continuity in a Cross-Border Multi-Access Edge Computing Federation Scenario

The paradigm shift in transportation systems towards Cooperative, Connected and Automated Mobility (CCAM) aims to improve vehicle maneuverability, reduce pollution, and increase safety. CCAM is primarily responsible for ensuring the best mobility environment, making it one of the main trends in the automotive industry. However, taking advantage of CCAM in Cross-Border Corridors (CBCs) faces many challenges, which go beyond infrastructure deployment cost, and all of which are related to supporting Service Continuity (SC) for mobile users, especially in light of the diversity of territories, network coverage areas, international roaming agreements, and type of cooperative maneuvers. As a solution to these challenges, the paper proposes and implements a new architecture for CCAM SC in CBCs that combines a federation of Multi-access Edge Computing (MEC) concepts to maintain SC.

Determining the trajectory of the vehicle motion on the basis of the recorded physical quantities describing its dynamics during typical maneuvers in road traffic

The paper compares the results of the vehicle motion trajectory calculations determined on the basis of the recorded waveforms of the acceleration vector and angular velocity components during the double lane change maneuver, taking into account the errors of acceleration sensors reset (zeroing). The differences in the results of the calculations were determined and possible causes of these differences were indicated. The test results indicate that special attention should be paid to zeroing the acceleration sensors, in particular the ay component.

Comparison of the efficacy of two alveolar recruitment manoeuvres in improving the lung mechanics and the degree of atelectasis in anaesthetized healthy sheep

The aim of the present study was to evaluate whether the application of two types of alveolar recruitment manoeuvres (ARMs) followed by a positive end-expiratory pressure (PEEP) improved lung mechanics and the degree of atelectasis caused by general anaesthesia. Twenty-one female Merino sheep were divided into three groups: sustained inflation ARM (ARMsust), stepwise ARM (AMRstep), and control (without ARM). Sheep received detomidine-morphine for premedication, propofol for induction, and isoflurane during general anaesthesia in a volume-controlled mode with 100% oxygen during the first 15 min of anaesthesia and 40% the rest of the study. The right jugular vein and metacarpal artery were catheterised for mixed venous and arterial blood sample collection, respectively. The quasistatic compliance (Cqst), oxygenation parameters, and shunt fraction (Qs/Qt) were monitored before ARM application (TpreARM), and at 10 (T10) and 60 min (T60) after ARM application. A pulmonary histopathological study was conducted on five animals from each group. A significant increase in Cqst was observed in both ARM groups at T10 compared to TpreARM (ARMsust: P = 0.001; ARMstep: P = 0.002), although only the ARMsust group showed significant differences compared to the control group. The ARMstep group presented a significant improvement in oxygenation parameters and Qs/Qt fraction (T10: 4.84 (3.26–16.48)%, P = 0.048; T60: 4.40 (4.31–14.16)%, P = 0.004) compared with TpreARM (21.48 (20.61–28.32)%). The ARMstep group had the highest percentage of alveolar area and the most homogeneous values. In conclusion, the application of a stepwise ARM followed by PEEP improved atelectasis caused by isoflurane anaesthesia in healthy sheep.

Exploring Bicyclists’ Visual Attention during Conflicts with Truck Traffic

To determine how bicyclists allocate their visual attention, measurable eye-fixations of bicyclists during conflicts with a truck maneuvering at a commercial vehicle loading zone (CVLZ) were studied in a bicycling simulator environment. The potential influence of pavement markings in the conflict area (white lane markings, solid green, and dashed green), truck traffic (parked and exiting), and bicyclists’ gender (female and male) on bicyclists’ visual attention were investigated. Results of a mixed repeated-measures analysis of variance indicated that the type of truck maneuver in the CVLZ had the most significant effect on bicyclists’ visual attention. Dashed green bike lanes were more effective than other design practices to attract bicyclists’ visual attention. It was also found that men fixated more on pavement markings than women regardless of truck maneuver or of pavement marking type.

An improved nonlinear path following method with on-line transition trajectory generation for fixed-wing unmanned aerial vehicles

To enhance the path following ability for fixed-wing unmanned aerial vehicles, and solve the stability and high-accuracy tracking problems due to inappropriate route switchover time during intense maneuvers, an improved nonlinear path following method with on-line transition trajectory generation was proposed in the article. Firstly, the influence of the guidance distance to the nonlinear path following method was verified through a flight experiment, and the importance of the critical time for route switchover was deduced. The on-line transition trajectory generation was expected to realize the automation of the switchover process, including the computation of the critical switchover time and desired paths. Secondly, to generate the on-line transition trajectory, the computation method was derived for typical intense maneuvers, such as the turning maneuver for square trajectory, or the converging maneuver to the expected trajectory under initial numerous errors (such as modifying the waypoint during a flight mission). Finally, to solve the situations in which the transition trajectory does not exist, an adaptive guidance distance algorithm was proposed to improve the flight stability and accuracy. From the simulation and flight experiment results, stability and high accuracy can be guaranteed for different situations with the proposed methods. The path following error is smaller than 1.0 m when it is converged (in downwind or upwind situations), which is important for the method to be used widely.

Influence of Integration Schemes and Maneuvers on the Initial Alignment and Calibration of AUVs: Observability and Degree of Observability Analyses.

The use of autonomous underwater vehicles (AUV) has increased in a wide range of sectors, including the oil and gas industry, military, and marine research. The AUV capabilities to operate without a direct human operator and untethered to a support vessel are features that have aroused interest in the marine environment. The localization of AUV is significantly affected by the initial alignment and the calibration of the navigation sensors. In this sense, this paper proposes a thorough observability analysis applied to the latter problem. The observability analysis is carried out considering three types of sensor fusion integration and a set of maneuvers, and the results are validated through numerical simulations. As main contribution of this paper, it is shown how the addition of position errors in the observation vector can decouple some gyro and accelerometer biases from the latitude and altitude errors, particularly in the stationary observability analysis. The influence of oscillations in the diving plane and typical AUV maneuvers are analyzed, showing their relative impacts on the degree of observability of the inertial measurement unit (IMU)/Doppler velocity log (DVL) misalignment and DVL scale factor error. Finally, the state’s estimation accuracy is also analyzed, showing the limitation of the degree of observability as an assessment tool for the estimability of the states.

Impact of time pressure on acceleration behavior and crossing decision at the onset of yellow signal

This study investigates acceleration behavior and crossing decision of the drivers under increasing time pressure driving conditions. A typical urban route was designed in a fixed-base driving simulator consisting of four signalized intersections with varying time to stop line (4 s and 6 s) and maneuver type (right-turn and go-through). 97 participants’ data were obtained under No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) driving conditions. The acceleration behavior was examined at the onset of yellow signal in four ways: continuous deceleration, acceleration-deceleration, deceleration-acceleration, and continuous acceleration. A random forest model was used to build an acceleration behavior prediction model for identifying the significant explanatory variables based on variable importance ranking. Further, a Mixed Effects Multinomial Logit (MEML) model was developed using the explanatory variables obtained from a random forest model. Additionally, a generalized linear mixed model was incorporated for estimating the likelihood of crossing an intersection by considering all the explanatory variables. A MEML model result revealed that the odds of adopting acceleration-deceleration, deceleration-acceleration, and continuous acceleration instead of continuous deceleration increased by 63 %, 123 %, and 77 %, respectively under HTP driving conditions. Moreover, the likelihood of crossing a signalized intersection increased by 2.73 times and 4.26 times when the drivers were under LTP and HTP driving conditions, respectively as compared to NTP driving condition. Apart from this, time to stop line (reference: 6 s) and age showed negative association with crossing probability. Overall, the findings from this study revealed that drivers altered their acceleration behavior for executing risky driving decisions under increasing time pressure driving conditions.

Multisensor-Based Predictive Control for Autonomous Parking

This article formalizes, under a single common multisensor-based predictive control framework, five different types of parking maneuvers: perpendicular, diagonal for both forward and backward motions, and parallel for backward motions. Since, from a practical point of view, forward parallel parking is usually not advisable, it is not addressed in this work. By moving the effort from motion planning to control, the parking tasks can be completely defined solely from the detected empty parking spots. Additionally, the classical compromise between completeness and computational efficiency when compared to exploration-based path planning techniques is eliminated. The results of a few individual cases are presented and compared against a state-of-the-art path planning approach to illustrate the behavior and performance of the proposed framework as well as results from exhaustive simulations to assess its convergence. As shown in the convergence analyses, the presented approach allows us to park from virtually any sensible initial pose. Finally, real experimentation using a robotized Renault ZOE shows the validity and robustness in the convergence domain of the presented approach.

Wpływ dynamiki samolotu i jego stanu lotu na bezpieczeństwo wykonania manewru ominięcia ruchomych przeszkód

The exact identification of essential factors affecting the course of evasive manoeuvre, that has been computed to avoid a collision with moving obstacles, is necessary to ensure a safe passing by a moving obstacle. At the same time, during the evasive manoeuvre the pre-defined separation between the airplane and obstacles is required. The matter of presented work is defined as the analysis of influence of factors on execution of anti collision manoeuvre taking into account deformation of flight trajectory and changes of time histories of distance from the airplane to detected obstacles. Attention has been focused on internal interactions, resulting from the essential changes of the airplane’s behaviour. Discussion has been illustrated by selected results of computer simulations, executed for typical manoeuvres performed to avoid obstacles, while affected by adverse impacts of selected factors. The appropriate guidelines have been proposed, that should counteract these adverse effects on realisation of computed manoeuvre.

Design of an Estimator Using the Artificial Neural Network Technique to Characterise the Braking of a Motor Vehicle.

Automatic systems are increasingly being applied in the automotive industry to improve driving safety and passenger comfort, reduce traffic and increase energy efficiency. The objective of this work is focused on improving the automatic brake assistance systems of motor vehicles trying to imitate human behaviour but correcting possible human errors such as distractions, lack of visibility or time reaction. The proposed system can optimise the intensity of the braking according to the available distance to carry out the manoeuvre and the vehicle speed to be as less aggressive as possible, thus giving priority to the comfort of the driver. A series of tests are carried out in this work with a vehicle instrumented with sensors that provide real-time information about the braking system. The data obtained experimentally during the dynamic tests are used to design an estimator using the Artificial Neural Network (ANN) technique. This information makes it possible to characterise all braking situations based on the pressure of the brake circuit, the type of manoeuvre and the test speed. Thanks to this ANN, it is possible to estimate the requirements of the braking system in real driving situations and carry out the manoeuvres automatically. Experiments and simulations verified the proposed method for the estimation of braking pressure in real deceleration scenarios.

Soil degradation after the traffic of a military combat vehicle leopard 1a5br

Heavy vehicle traffic, especially large military combat vehicles, causes soil compaction, which reduces their physical quality and increases their susceptibility to soil erosion. A large contingent of the Brazilian Army conducts combat vehicle training at the Santa Maria Instruction Field (CISM), which has been caused degradation of the ecosystem. The aim of this study was to evaluate the effect of combat vehicle Leopard 1A5BR traffic on soil physical properties in an Abruptic Alisol with military vehicle traffic history. Two types of maneuvers were evaluated: (i) straight traffic with 0, 1 and 3 passes, and (ii) pivoting maneuver with 0, 1 and 2 pivots. Soil morphology, particle size distribution and organic carbon content were analyzed in the 0.00-0.10 and 0.20-0.30 m layers. Bulk density, total porosity, macroporosity, microporosity, saturated soil hydraulic conductivity, penetration resistance and preconsolidation pressure were evaluated in the 0.00-0.04, 0.10-0.14 and 0.20-0.24 m layers. The preconsolidation pressure of the surface layer indicated that the soil surface layer is susceptible to traffic compaction of Leopard 1A5BR. Only one pass (straight traffic) and one pivoting maneuver were sufficient to increase soil penetration resistance and bulk density and reduce the total porosity and macroporosity in the surface layer (0.00-0.04 m).

Current Therapies in Patients With Posterior Semicircular Canal BPPV, a Systematic Review and Network Meta-analysis.

To compare the efficacy of different treatments for posterior semicircular canal benign paroxysmal positional vertigo (PC-BPPV) by using direct and indirect evidence from existing randomized data. Randomized case-control studies that compared the efficacy of various nonsurgical treatments in PC-BPPV patients at 1 week and 1 month of follow-up were comprehensively screened. Bayesian network meta-analysis was performed to evaluate direct and indirect treatment comparisons. We further conducted subgroup pairwise meta-analysis to explore the inconsistency between comparisons of the Epley versus a sham maneuver and the Epley versus the Semont maneuver. A total of 41 parallel, randomized controlled studies were included. The Epley with vestibular rehabilitation (EVR), Epley, Semont and Hybrid maneuvers were effective in eliminating nystagmus during a Dix-Hallpike test at 1 week of follow-up (odds ratios [ORs]: 11.41-23.8, 95% credible interval [CrI]: excluding null), among which EVR showed the best efficacy (the surface area under the cumulative ranking curves [SUCRA] = 77.5%). However, at 1 month of follow-up, only the Semont (rank first, SUCRA = 76.1%) and Epley maneuvers (rank second, SUCRA = 65.3%) were effective in eliminating nystagmus during a Dix-Hallpike test. In the pairwise subgroup meta-analysis, for patients younger than 55 years of age, the efficacy of the Epley maneuver was comparable to that of the Semont maneuver [rate ratio (RR): 0.99, 95% confidence interval (CI): 0.93-1.05]; for patients with a longer duration before treatment, the effect of the Epley maneuver was equivalent to that of a sham maneuver (RR: 1.07, 95% CI: 0.90-1.29). Among the 12 types of PC-BPPV treatments, the Epley, Semont, EVR, and Hybrid maneuvers were effective in eliminating nystagmus during a Dix-Hallpike test for PC-BPPV at 1 week of follow-up, whereas only the Epley and Semont maneuvers were effective at 1 month of follow-up. The duration before treatments and the age of patients might contribute to the efficacy of treatments.

Learning Embedding Features Based on Multisense-Scaled Attention Architecture to Improve the Predictive Performance of Air Combat Intention Recognition

In modern air combat, acquiring the opponent’s air combat intention is one of essential prerequisites to evaluate the air combat situation effectively and master the battlefield initiative. On account of multi-dimensional and temporal characteristics of the target state, a recognition model is proposed to identify tactical intention of aerial target based on a multi-sense-scaled attention architecture. First of all, the multi-dimensional feature information, including target state attributes, battlefield environment, target attributes and so on, is constructed as target feature. Secondly, the non-numerical information, such as battlefield environment characteristics, enemy and friend attributes of targets, radar status, maneuver type, etc, is transformed into numerical data. For the purpose of subsequent data processing, the flight speed, altitude, RCS, etc, in the target status information are normalized into the same dimension. Furthermore, a target intention recognition model with multiple sense-scaled attention mechanism is designed to depict the target state, attributes and the information of the battlefield environment from multiple dimensions, which is convenient to be close to the actual combat. The BiLSTM neural network is used to learn the deep-seated information in the air combat intention feature vector, and the attention mechanism is used to adaptively allocate the network weights. The air combat feature information with different weights is introduced into the softmax function layer for intention recognition. Compared with the traditional air tactical target intention recognition model, the proposed model effectively improves the efficiency of air target tactical intention recognition as well as affords important theoretical significance and reference value for the auxiliary combat system.

On-Line Learning for Planning and Control of Underactuated Robots With Uncertain Dynamics

We present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the perturbations induced by the model uncertainty on the active and passive degrees of freedom. The generic iteration of the algorithm makes use of the learned data in both the planning phase, which is based on optimization, and the control phase, where partial feedback linearization of the active dofs is performed on the model updated on-line. The performance of the proposed approach is shown by comparative simulations and experiments on a Pendubot executing various types of swing-up maneuvers. Very few iterations are typically needed to generate dynamically feasible trajectories and the tracking control that guarantees their accurate execution, even in the presence of large model uncertainties.

Older Drivers' Performance at Intersections: An Experimental Study in Italy

Longer life expectancy is leading to an increase in the average age of the European population. In 2018, 20% of the population was over 65 years old. In 2070, 30.3% of the population will be over 65. Older people prefer to walk in their neighborhoods but they rely on cars as their primary mode of transportation, which allows them to maintain their autonomy over long distances. However, age-related physical impairments can have a significant impact on driving skills, including the ability to properly visualize driving scenarios. Numerous studies have shown that older drivers have significant safety problems at standard intersections rather than roundabouts (40% of fatal crashes occur at standard intersections among drivers 70 years and older). In this work, it was decided to study a specific parameter representative of the behavior of all road users when preparing for the typical maneuvers at stop-controlled intersections (crossing, right-turn and left-turn). This parameter is the gap accepted by elderly road users at different stop-controlled intersections and under homogeneous traffic conditions. For this purpose, a research campaign was organized with 37 elderly drivers performing all possible maneuvers at 9 intersections in Italy (Eastern Sicily). The experimentally determined values of the critical gap for left-turn and crossing maneuvers were found to be compatible with those of the main internationally used reference manuals for road design. The value of the critical 50^th percentile gap (G₅₀) for the right-turn maneuver was similar to that for the left-turn maneuver. This is an indication of a very similar level of difficulty for older drivers in performing both turning maneuvers. The results of this study have highlighted the need to take into account the different operational difficulties associated with the possible maneuvers at intersections. The theoretical approaches of the Italian legislation are carried out, based on a criterion that does not distinguish according to the way in which the three maneuvers, which proves to be totally inadequate. This suggests a complete revision of the criteria for checking sight distances at stop-controlled intersections.

Convolutional Neural Network-Based Intelligent Decision-Making for Automated Vehicles

The decision-making module provides proper driving maneuvers for Automated vehicles (AVs), which is essential to the deployment of AVs. Current research works attempted to tackle this issue by optimizing some specific significant objectives or modeling separate typical maneuvers. However, highly dynamic and complicated traffic flows bring huge challenges to this module. This paper develops a convolutional neural network-based (CNN) deep learning framework to learn proper driving maneuvers from bulks of the collected traffic data. The proposed method firstly covers the dynamic traffic information through constructing virtual traffic images, namely dynamic motion images. Those images contain important motion relationships between the ego car and its neighbors. Then, the proposed CNN learning model is trained to capture inherent features from those motion images and to learn to make proper driving behaviors. We employ the learning framework to learn typical highway driving scenarios. Training and test results validate the effectiveness of our proposed CNN-based learning decision-making approach and suggest that deep-learning-based methods have dramatic potential to promote the intelligent driving capability of AVs and thus accelerate the launch of AVs.

Study of the problem and the idea of operation of four-wheel-steering cars

The article takes the form of a review paper and focuses on the control problems of four wheel steering vehicles. The main issues related to the control of four wheel steering (4WS) vehicles, the historical development, the current state and the approach to the construction of 4WS vehicles have been discussed. The whole of theoretical issues related to the control of the car direction (including elements related to mathematical modelling and simulation studies of the vehicle motion, issues of synthesis and analysis of the 4WS control algorithms) were presented systemically. The presented collection of literature is based on a rather extensive review relating to 4WS cars and on considerations shaping the concept of development of the topic of control algorithms in 4WS cars. A preliminary concept of control in autonomous cars is also presented and will be explain what type of road manoeuvres it is going to analyse. The authors have also shown an example of the computational apparatus used.

RECURSIVE NEURAL NETWORK MODEL OF CATAMARAN MANOEUVRING

A neural network model to simulate catamaran manoeuvres is proposed as an alternative to the traditional methodology of developing manoeuvring mathematical models. Data obtained in full-scale trials with a real ship are used to train the model. By recording full-scale trials of catamaran manoeuvres it is possible to generate a neural network model which will allow the prediction of the catamaran manoeuvring performance under different conditions.
 A Recursive Neural Network (RNN) manoeuvring simulation model is proposed and applied to a catamaran in this specific case. Inputs to the simulation are the orders of rudder angle and ship’s speed and also the recursive outputs velocities of sway and yaw. Two types of manoeuvres are simulated: tactical circles and zigzags. The results between the full-scale data and the simulations are compared in order to analyze and determine the accuracy of the RNN. The study is performed for a catamaran operating in the Tagus estuary for passenger transport to and from Lisbon.

Torque Vectoring Control of RWID Electric Vehicle for Reducing Driving-Wheel Slippage Energy Dissipation in Cornering

The anxiety of driving range and inconvenience of battery recharging has placed high requirements on the energy efficiency of electric vehicles. To reduce driving-wheel slip energy consumption while cornering, a torque vectoring control strategy for a rear-wheel independent-drive (RWID) electric vehicle is proposed. First, the longitudinal linear stiffness of each driving wheel is estimated by using the approach of recursive least squares. Then, an initial differential torque is calculated for reducing their overall tire slippage energy dissipation. However, before the differential torque is applied to the two side of driving wheels, an acceleration slip regulation (ASR) is introduced into the overall control strategy to avoid entering into the tire adhesion saturation region resulting in excessive slip. Finally, the simulations of typical manoeuvring conditions are performed to verify the veracity of the estimated tire longitudinal linear stiffness and effectiveness of the torque vectoring control strategy. As a result, the proposed torque vectoring control leads to the largest reduction of around 17% slip power consumption for the situations carried out above.

Simulation Technology in Anesthesiology.

Simulation Technology in Anesthesiology.