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This paper presents a real-time driver characterisation algorithm that models the driver based on 10 parameters governing their control of vehicle longitudinal speed and acceleration. It utilises an open-loop longitudinal model in conjunction with an Unscented Kalman Filter (UKF). The algorithm can operate in real-time using velocity and acceleration measurements, in addition to a-priori knowledge of the path curvature and road features such as speed limit locations. The UKF also enables a system identification process to estimate average response of each driver. These identified parameters are subsequently fitted to independent measures of fuel economy and safety to demonstrate possible uses for the characterisations. In addition to the comprehensive characterisation of the drivers’ behaviour the work is also novel in its unique approach to identifying time-based parameters within a time-varying dynamic system (UKF). Possible applications include insurance black box assessments of driver aggressiveness and safety, and in autonomous driving algorithms by providing a model to closely replicate the specific driving styles of various users.

School nurses are pivotal to the safety of school-aged children, particularly those who receive medications in the school setting. The purpose of this study was to explore factors associated with medication administration errors in North Carolina school districts between 2012/2013 and 2017/2018. A longitudinal study using repeated measures analysis of school health services data collected in the North Carolina Annual School Health Services and Programs Survey was conducted. Over time, the number of medication errors ( p = .001) and number of medication corrective action plans ( p

School nurses intervene with students, parents, and school staff to advance the health and academic success of students. We conducted an integrative literature review of published research to describe the types of school nurse interventions and health and education outcome measures and to examine how school nurse interventions were linked to student outcomes. Sixty-five studies met the inclusion criteria. We used the National Association of School Nurses’ Framework for 21st Century School Nursing Practice to categorize school nurse interventions and health and education outcome measures. The majority of interventions were categorized under the care coordination principle, most commonly, motivational interviewing and counseling. In 17 studies, school nurse interventions were linked to improved student outcomes. Most studies (80%) were descriptive. To advance school nursing science, researchers can build on this foundation with more rigorous research methods to evaluate the impact of school nurse interventions and activities on student health and education outcomes.

A new tool for black-box non-linear system identification of multi-input multi-output systems is presented in this paper. The new structure extends the conventional linear state-space model into a non-linear framework, where each parameter is a non-linear function of the inputs or the states. The method works iteratively in the time domain using an extended Kalman filter. The model retains a state-space structure in modal canonical form, which ensures that a minimal number of parameters need to be identified and also produces additional information in terms of the system eigenvalues and the dominant modes. This structure is a completely black-box system, which requires no physical understanding of the process for successful identification, and it is possible to expand easily the order and the complexity of non-linearities, while ensuring good parameter conditioning. A simple non-linear example illustrates the method, and identification of a highly non-linear brake model is also presented. These examples show that the method can be applied as a mechanism for model order reduction; it is equally very suitable as a tool for non-linear plant system identification. In both capacities this new method is valuable, particularly as the generation of simplified models for the whole vehicle and its subsystems is an increasingly important aspect of modern vehicle design.

In this paper, we present a real-time optimal controller, Predictive Steering Control (PSC), to perform high-speed runway exit manoeuvres. PSC is developed based on a time-varying LQR with look-ahead. The aircraft’s ground dynamics are described by a high-fidelity nonlinear model. The proposed controller is compared with an Expert Pilot Model (EPM), which represents a pilot, in several different speed runway exit manoeuvres. With an improved road preview mechanism and optimal feedback gain, the predictive steering controller outperforms the expert pilot’s manual operations by executing the runway exit manoeuvre with a lower track error. To investigate the optimality of PSC, its solution is further optimised using a numerical optimal controller Generalized Optimal Control (GOC). PSC is shown to be close to the final optimal solution. To study robustness, PSC is tested with various aircraft configurations, road conditions and disturbances. The simulation results show that PSC is robust to disturbances within a normal range of operational parameters.

A step towards fully autonomous vehicles can be achieved by autonomous collision avoidance. This paper presents the development and implementation of a rear-end collision avoidance system which consists of novel linear threat assessment, projected escape path planning with non-zero initial condition, reference generation and linear path control by an electric steering actuator using dynamic inversion. The linear threat assessment continuously calculates threat metrics associated with collision avoidance by either steering or braking. The escape path planner continuously estimates a path with constraints on the peak lateral acceleration and the required lateral deviation. The reference generator then estimates the states for avoidance according to the available friction. Linear state feedback control is applied for path following to avoid a collision in both straight-road scenarios and curved-road scenarios. The proposed approach is evaluated in simulations, and in practice on a C-segment Ford vehicle. A user-related assessment about the usefulness and satisfaction is included to conclude the paper.

This paper describes a new motion cueing algorithm for motion-based driving simulators. The algorithm uses the simulated vehicle’s body sideslip angle as the demand for the motion platform’s yaw degree of freedom. The current state of the art for motion cueing algorithms involves some form of filter or controller that limits the bandwidth of the vehicle motion before using this as the motion platform demand; the algorithm is tuned such that the platform does not exceed its limits. However, this means that information about the vehicle state that is contained within the motion is removed indiscriminately. Since the body sideslip angle will fit within the platform yaw limit under normal conditions, it does not need to be filtered beforehand, and thus no information must be removed. The implementation of the body-sideslip-based algorithm is described, as is a set of tests using human participants wherein the body sideslip algorithm was compared against the three most popular existing algorithms (namely the classical, adaptive and linear quadratic regulator algorithms) for normal road driving. The results of these tests indicate that the body sideslip algorithm performs as well as, or marginally better than, the other algorithms; future work will test the algorithm under limit handling conditions, to see whether the approach of preserving vehicle state information improves the simulator driver’s perception.

The majority of vehicle dynamics control systems currently in production utilize some form of brake or throttle intervention to generate a yaw moment and control wheel slip. Such control systems can be both intrusive and inefficient. The use of active driveline technology is therefore an attractive alternative and recent advances in controlled differential technology have served to make it a potentially viable one. Using simulation results, this paper will demonstrate the power of these devices to influence vehicle dynamics by first proposing a suitable control strategy. This is then used to illustrate how, with perfect actuation, a vehicle's handling characteristics may be modified. The actuator limitations imposed by the two main classes of contemporary controlled differentials are then discussed and imposed on the simulation model. Using the ideal results as a benchmark, the relative merits of each type are then assessed.

This article considers a novel method for estimating parameters in a vehicle-handling dynamic model using a recursive filter. The well-known extended Kalman filter - which is widely used for real-time state estimation of vehicle dynamics - is used here in an unorthodox fashion; a model is prescribed for the sensors alone, and the state vector is replaced by a set of unknown model parameters. With the aid of two simple tuning parameters, the system self-regulates its estimates of parameter and sensor errors, and hence smoothly identifies optimal parameter choices. The method makes one contentious assumption that vehicle lateral velocity (or body sideslip angle) is available as a measurement, along with the more conventionally available yaw velocity state. However, the article demonstrates that by using the new generation of combined GPS/inertial body motion measurement systems, a suitable lateral velocity signal is indeed measurable. The system identification is thus demonstrated in simulation, and also proved by successful parametrization of a model, using test vehicle data. The identifying extended Kalman filter has applications in model validation - for example, acting as a reference between vehicle behaviour and higher-order multi-body models - and it could also be operated in a real-time capacity to adapt parameters in model-based vehicle control applications.

The paper presents a method for designing a non-linear (i.e. extended) Kalman filter that is also parameter adaptive and hence capable of online identification of its model. The filter model is deliberately simple in structure and low order, yet includes non-linear, load-varying tyre force calculations to ensure accuracy over a range of test conditions. Shape parameters within the (Pacejka) tyre model are adapted rapidly in real time, to maintain excellent state reconstruction accuracy, and provide valuable real-time lateral and vertical tyre force information. The filter is tested in both simulated and test vehicle environments and provides good results. The paper also provides an illustration of the importance of good Kalman filter design practice in terms of selection and tuning of the noise matrices, particularly in terms of the influence of model/sensor error cross-correlations.

Two actuation mechanisms are considered for the comparison of performance capabilities in improving the yaw-sideslip handling characteristics of a road vehicle. Yaw moments are generated either by the use of single-wheel braking or via driveline torque distribution using an overdriven active rear differential. For consistency, a fixed reference vehicle system is used, and the two controllers are synthesized via a single design methodology. Performance measures relate to both open-loop and closed-loop driving demands, and include both on-centre and limit handling manoeuvres.

With the advent of production electronic throttle control there is scope for increased customer satisfaction through the optimization of the throttle pedal demand map to individual drivers. The aim of this study is to develop algorithms to identify, from variables measured in real time on a test vehicle, the requirement for and the direction of adaptation of throttle pedal progression. An on-line appraisal procedure has been developed to identify the individual ‘ideal’ progression (IIP) for any driver. During the appraisal the subject is exposed to a series of pedal progressions, and their verbal response to each change is used to converge to their optimal setting. Vehicle data acquired on these appraisal drives have been regressed against IIP in a full factorial study, and the most statistically significant driver model established. A preliminary implementation of the model is used to demonstrate that throttle progression adapts appropriately towards IIP, thereby matching vehicle performance feel to driver expectations.

This paper describes a new general framework for the action of an automated driver (or driver model) to provide the control of longitudinal and lateral dynamics of a road vehicle. The context of the problem is assumed to be in high-speed competitive driving, as in motor racing, where the requirement is for maximum possible speed along a track, making use of a reference path (racing line) but with the capacity for obstacle avoidance and recovery from large excursions. While not necessarily representative of a human driver, the analysis provides worthwhile insight into the nature of the driving task and offers a new approach for vehicle lateral and longitudinal control; it also has applications in less demanding applications such as Advanced Cruise Control systems. As is common in the literature, the driving task is broken down into two distinct subtasks: path planning and local feedback control. In the first of these tasks, an essentially geometric approach is taken here, which makes use of a vector field analysis. At each location x the automated driver is to prescribe a vector w for the desired vehicle mass centre velocity; the spatial distribution and global properties of w( x) provide essential information for stability analysis, as well as control reference. The resulting vector field is considered in the context of limited friction and limited mass centre accelerations, leading to constraints on ∇ w. Provided such constraints are satisfied, and using suitable adaptation of w( x) when required, it is shown that feedback control can be applied to guarantee stable asymptotic tracking of a reference path, even under limit handling conditions. A specific implementation of the method is included, using dual non-linear SISO (single-input single-output) controllers.

This paper proposes a new approach to the identification of reduced order models for complex mechanical vibration systems. Parametric identification is commonly conducted by the regression of time-series data, but when this includes significant unmodelled modes, the error process has a high variance and autocorrelation. In such cases, optimization using least-squares methods can lead to excessive parameter bias. The proposed method takes advantage of the inherent boundedness of mechanical vibrations to design a new regression set with dramatically reduced error variance. The principle is first demonstrated using a simple two-mass simulation model, and from this a practicable approach is derived. Extensive investigation of the new randomized integral error criterion method is then conducted using the example of identification of a quarter-car suspension system. Simulation results are contrasted with those from comparable direct least-squares identifications. Several forms of the identification equations and error sources are used, and in all cases the new method has clear advantages, both in accuracy and consistency of the resulting identification model.