Driver Input Research Articles

A Ring Diode–Capacitor Network for Current-Balancing Multiple LED Strings

A ring diode–capacitor network for current-balancing multiple LED strings is presented. The concept is based on utilizing the charge-balance property of capacitors to balance the currents of two adjacent LED strings in each switching cycle. Such current-balancing mechanism propagates over a ring network. The network has several merits, including 1) high current-balancing accuracy, 2) a simple structure, 3) modular and scalable, 4) current-balancing property being less dependent on the string voltages and the values of the capacitors, 5) inherent galvanic isolation between the driver and the LED strings, and 6) operation of the healthy LED(s) being unaffected by the failure LED(s). In addition, the capacitors in the network can be used as resonant tank components for the front-stage driver. An 80-W prototype for balancing the currents of ten LED strings has been built and evaluated. Each string has eight pieces of 1-W LED connected in series. The prototype is driven by a series-resonant converter. Results reveal that the string current variation is less than ±0.5%, irrespective to the variation of the string voltages and capacitor values. The total LED power can be dimmed to 10% of the rated output by a hybrid switching frequency and duty-cycle control scheme. The overall efficiency from the driver input to all LED strings is around 95% over the operating range. Modeling, design, and analysis of four possible configurations will be given.

An efficient design of CMOS comparator and folded cascode op-amp circuits using particle swarm optimization with an aging leader and challengers algorithm

Due to the compl ex growth in very large scale integration circuits, the task of optimal analog integrated circuit design by hand is very difficult. Optimization is a time consuming process having many conflicting criteria and a wide range of design parameters. Characterization of complex tradeoffs between nonlinear objectives while assuring required specifications makes analog circuit design a tedious process. The design and optimization processes have to be automated with high accuracy. Evolutionary technique may be a proficient implement for automatic design of analog integrated circuits that has been one of the most challenging topics in VLSI design process. This paper presents a competent approach for optimal designs of two analog circuits, namely, complementary metal oxide semiconductor two-stage comparator with P-type metal oxide semiconductor input driver and n-channel input, folded-cascode operational amplifier. The evolutionary technique used is particle swarm optimization (PSO) with an aging leader and challenger (ALC-PSO). The main aim is to optimize the MOS transistors’ sizes using ALC-PSO in order to reduce the areas occupied by the circuits and to get better performance parameters of the circuits. To exhibit the performance parameters of the circuits, simulation program with integrated circuit emphasis simulation has been carried out by using the optimal values of MOS transistor sizes and other design parameters. Simulation results demonstrate that design specifications are closely met and required functionalities are accommodated. The simulation results also show that the ALC-PSO is superior to the other algorithms in terms of MOS area, and performance parameters like gain, power dissipation, etc. for the examples considered.

Integrated wheel loader simulation model for improving performance and energy flow

Abstract This paper describes an integrated wheel loader simulation model for improving performance and energy flow. The proposed integrated wheel loader simulation model includes a driver model that is designed to perform the two objectives of working and driving. The driver model for working was designed according to eight conditions considered as events and environment information. The driver model for driving is composed of throttle, brake, and steering inputs which represent an actual driver's input characteristics. By analyzing experimental test data of V-pattern working, human driving characteristics have been derived and applied in the driver model by using linear quadratic regulator (LQR) and model predictive control (MPC). The wheel loader dynamic simulation model with the driver model used in this study consists of four parts: mechanical powertrain, hydraulic powertrain, vehicle dynamic model, and working dynamic model with a simplified load model. All simulation models have been constructed in the Matlab/Simulink environment, and the proposed driver model has been validated from experimental test data. Working performance with the optimized path, energy flow, and loss analysis during V-pattern working was predicted and evaluated with the developed human driver and dynamic simulation model of a wheel loader. The driver model can be utilized in the design stage for prediction and evaluation of a wheel loader's working performance. It is also expected that an investigation of the optimal working pattern and energy flow for various working cycles of wheel loaders will be possible with the driver-model-in-the-loop simulation.

Driver models for personalised driving assistance

We propose a learning-based driver modelling approach which can identify manoeuvres performed by drivers on the highway and predict the future driver inputs. We show how this approach can be applied to provide personalised driving assistance. In a first example, the driver model is used to predict unintentional lane departures and a model predictive controller is used to keep the car in the lane. In a second example, the driver model estimates the preferred acceleration of the driver during lane keeping, and a model predictive controller is implemented to provide a personalised adaptive cruise control. For both applications, we use a combination of real data and simulation to evaluate the proposed approaches.

Adaptive In-Car External Applications using Nomadic Smartphones and Cloudlets

Computerized in-vehicle systems are used to generate data about vehicle operations such as vehicle on-board diagnostics and vehicle information obtained from devices mounted on road infrastructures. Detectors located along the roadside are pivotal to Intelligent Transport Systems services, including real time management of driven information. In this paper, we propose a model that affords in-car external applications based on nomadic smartphone, virtual machine-based cloudlets, and car manufacturer’s private cloud computing that on one side delivers remote diagnostic and vehicle performance information software as a service and can also be viewed as a doorway to deliver cloud based applications on other side. Virtual machine-based Cloudlet is used to improve response time extended by diverse communication latencies. We designed a remote vehicle assignation cloud computing platform service responsible for real time analysis of vehicle information and providing internet applications to the car user by using a vehicle-to-smart phone applications interface approach, which helps the smartphones to act as a remote user which passes driver inputs and delivers output from external applications.

Real-Time Estimation of Transmitted Torque on Each Clutch for Ground Vehicles With Dual Clutch Transmission

This paper mainly focuses on the robust separate estimation of transmitted torque on each clutch of the dual clutch transmission-the task that has not yet been accomplished by previously published efforts. In order to estimate the torque on individual clutches, the observer system is composed of a shaft model-based observer, unknown input observers, and a model reference PI observer. The estimations obtained by each subcomponents are processed together to provide online torque estimation of high accuracy. The proposed estimator requires the following information: speed measurements of engine, input shafts, and wheels, and nominal engine torque information obtained as a function of driver inputs and engine speed. These are already available in the current production cars; hence, no further modification or addition to the vehicle hardware is necessary. To validate the effectiveness of the proposed estimator, experiments using an actual vehicle are conducted with various scenarios. With detailed analysis of the results obtained by the experiments, the proposed dual clutch transmission torque estimator is shown to be apt for real car application.

Manoeuvre-based Validation of Hybrid Powertrains

For the implementation of driving manoeuvres, the process shown in Figure 4 has been established. The starting point is always marked by a validation task requiring customer-relevant driving situations. Those can be derived from common or extreme customer use cases [8]. Afterwards, a specific definition is put down in a manoeuvre catalogue. As indicated in Figure 2, a driving manoeuvre is completely defined if the driver inputs, the vehicle and the environmental conditions are declared. The validation target determines the objective analysis and measurement criteria as well as the subjective and therefore customer-relevant assessment criteria. In the following, the manoeuvre sequence is described in a sequence chart and implemented in a Matlab/Simulink model. The model’s functionality can be evaluated virtually in VE 1 as well as at the test bench in VE 3 (verification). A comparison between the results of the test bench experiments and the road tests finally confirms the quality of the analysis. Open image in new window FIGURE 4 Process for definition and verification of driving manoeuvres [5, 6, 7]

A clipped-optimal control algorithm for semi-active vehicle suspensions: Theory and experimental evaluation

This paper addresses the problem of optimal control for semi-active vehicle suspensions. A specific goal is to develop an algorithm which is capable of optimising ride and handling behaviour simultaneously in an experimental situation. A time-domain optimal control approach is adopted in which ride and handling are modelled as exogenous disturbances acting on the vehicle: road disturbances (modelled stochastically), and driver inputs (treated as deterministic quasi-static disturbances). A control algorithm is derived from a solution of the stochastic Hamilton–Jacobi–Bellman equation for the finite horizon case. The advantages of the approach are demonstrated experimentally on a test vehicle performing a steering manoeuvre on a bumpy roundabout.

Cloud Model-Based Energy Management Strategy for Parallel Hybrid Vehicles

Using the uncertain conversion capacity between the expressions of quantitative and qualitative concept in the cloud model, an energy management strategy based on cloud model is developed for parallel hybrid vehicles (PHVs). By the driver input and the state of charge (SOC) of the energy storage, a set of rules are developed to effectively determine the torque split between the internal combustion engine (ICE) and the electric motor. An analysis of the simulation results is conducted using ADVISOR in order to verify the effectiveness of the proposed control strategy. It is confirmed that the control scheme can be used to improve fuel economy and emission of the hybrid vehicles.

Development of an optimal driver command interpreter for vehicle dynamics control

This paper introduces a new driver command interpreter (DCI) for vehicle dynamics control, providing optimal calculation of desired CG forces and moments based on driver inputs and road conditions. The proposed DCI is established based on principles of optimal linear quadratic regulator (LQR) theory and vehicle dynamics. The optimal feed-forward and feedback gains of proposed DCI can be updated in real time by online matrix calculation. Analytical simulations and experimental test results under various driving conditions are presented to evaluate the proposed DCI and vehicle dynamics control system. The simulation and experimental results indicate that the vehicle dynamics control system using the proposed DCI can effectively stabilise the vehicle motion and improve the vehicle handling under critical driving conditions.

Design of an Integrated Vehicle Chassis Control System with Driver Behavior Identification

An integrated vehicle chassis control strategy with driver behavior identification is introduced in this paper. In order to identify the different types of driver behavior characteristics, a driver behavior signals acquisition system was established using the dSPACE real-time simulation platform, and the driver inputs of 30 test drivers were collected under the double lane change test condition. Then, driver behavior characteristics were analyzed and identified based on the preview optimal curvature model through genetic algorithm and neural network method. Using it as a base, an integrated chassis control strategy with active front steering (AFS) and direct yaw moment control (DYC) considering driver characteristics was established by model predictive control (MPC) method. Finally, simulations were carried out to verify the control strategy by CarSim and MATLAB/Simulink. The results show that the proposed method enables the control system to adjust its parameters according to the driver behavior identification results and the vehicle handling and stability performance are significantly improved.

Modelling and Validation of the Vehicle Longitudinal Model

This paper presents the detailed derivation and validation of a full vehicle model tostudy the behaviour of vehicle dynamics in the longitudinal direction. The modelconsists of handling and tyre subsystems, an engine model subsystem, an automatictransmission subsystem and a brake model subsystem. The full vehicle model was thenvalidated using an instrumented experimental vehicle based on the driver input frombrake and throttle pedals. Vehicle transient handling dynamic tests known as suddenbraking tests were performed for the purpose of validation. Several behaviours of thevehicle dynamics were observed during braking and throttling manoeuvres, such asbody longitudinal velocity, wheel linear velocity and tyre longitudinal slip, at eachquarter of the vehicle. Comparisons of the experimental results and model responseswith sudden braking and throttling imposed motions were made in this study. It isconcluded that the trends between simulation results and experimental data were found to be almost similar with an acceptable level of error for the application at hand

Semiautonomous Vehicular Control Using Driver Modeling

Threat assessment during semiautonomous driving is used to determine when correcting a driver's input is required. Since current semiautonomous systems perform threat assessment by predicting a vehicle's future state while treating the driver's input as a disturbance, autonomous controller intervention is limited to a restricted regime. Improving vehicle safety demands threat assessment that occurs over longer prediction horizons wherein a driver cannot be treated as a malicious agent. In this paper, we describe a real-time semiautonomous system that utilizes empirical observations of a driver's pose to inform an autonomous controller that corrects a driver's input when possible in a safe manner. We measure the performance of our system using several metrics that evaluate the informativeness of the prediction and the utility of the intervention procedure. A multisubject driving experiment illustrates the usefulness, with respect to these metrics, of incorporating the driver's pose while designing a semiautonomous system.

A Novel Algorithm for Crash Detection Under General Road Scenes Using Crash Probabilities and an Interactive Multiple Model Particle Filter

Driver inattention causes the majority of vehicular crashes, and these accidents produce extensive economic and social costs, as well as injuries and fatalities. Thus, the development of imminent crash detection systems is one of the most important issues in automotive safety. Various crash detection algorithms have been proposed, but the coverage of these algorithms has been limited to one or two crash scenarios. To widen the coverage of crash detection systems to include various crash modes, driver behaviors that are dependent on road scenes and vehicle dynamics should be considered. This paper proposed an algorithm for detecting an imminent collision in general road scenes. The proposed algorithm consists of crash probability data generated from Monte Carlo simulations that consider driver behavior and vehicle dynamics, a tracking algorithm that uses an interactive multiple-model particle filter, and a threat assessment algorithm that estimates crash probabilities. To reduce nuisance and false-positive alarms, the algorithm discriminated between normal and dangerous road scenes, and a point of no return was detected using three driver models that addressed different levels of driver input. The performance of the proposed algorithm was evaluated under three scenarios, and it successfully discriminated between collision and near-miss cases, and it adjusted warning times depending on the road scenes. It is expected that the proposed algorithm would have good driver acceptability based on the results of the near-miss cases. The proposed algorithm can be used as an integrated crash detection algorithm for crash warning, avoidance, and mitigation purposes while incorporating tracking information from multiple sources.

Modeling the Speed Choice Behaviors of Drivers on Mountainous Roads with Complicated Shapes

Roadway geometric features and pavement conditions can significantly affect driver behavior, particularly with regard to vehicle speed. This paper presents the development of an algorithm for speed selection for use in automated passenger car travel (without driver input) on mountainous roads with complicated shapes. The relationship between favorable driving speed and the geometric features of horizontal curves was established on the basis of driving experiments and spot speed observation data, and speed control models were established for driving on curves, curve approaches/departures, and tangents. The models developed can be used to calculate a driver's desired speed on any roadway with a defined geometry. The model considers the driver's behavior type and the vehicle's dynamic properties. This paper presents the results of simulation experiments on roads with small curve radii and narrow widths. The algorithms developed may be used for assisted and automated driving. Under automated driving conditions, speed control and speed change based on the algorithms developed make drivers feel natural as if they drive the car themselves.

Morphology and connectivity of parabrachial and cortical inputs to gustatory thalamus in rats.

The ventroposterior medialis parvocellularis (VPMpc) nucleus of the thalamus, the thalamic relay nucleus for gustatory sensation, receives primary input from the parabrachial nucleus, and projects to the insular cortex. To reveal the unique properties of the gustatory thalamus in comparison with archetypical sensory relay nuclei, this study examines the morphology of synaptic circuitry in the VPMpc, focusing on parabrachiothalamic driver input and corticothalamic feedback. Anterogradely visualized parabrachiothalamic fibers in the VPMpc bear large swellings. At electron microscope resolution, parabrachiothalamic axons are myelinated and make large boutons, forming multiple asymmetric, adherent, and perforated synapses onto large-caliber dendrites and dendrite initial segments. Labeled boutons contain dense-core vesicles, and they resemble a population of terminals within the VPMpc containing calcitonin gene-related peptide. As is typical of primary inputs to other thalamic nuclei, parabrachiothalamic terminals are over five times larger than other inputs, while constituting only 2% of all synapses. Glomeruli and triadic arrangements, characteristic features of other sensory thalamic nuclei, are not encountered. As revealed by anterograde tracer injections into the insular cortex, corticothalamic projections in the VPMpc form a dense network of fine fibers bearing small boutons. Corticothalamic terminals within the VPMpc were also observed to synapse on cells that were retrogradely filled from the same injections. The results constitute an initial survey describing unique anatomical properties of the rodent gustatory thalamus.

Modeling and Validation of Quarter Vehicle Traction Model

This manuscript provides modeling and validation of a quarter car vehicle model to study the wheel dynamics behavior in longitudinal direction. The model is consists of a longitudinal slip model subsystem, a quarter body dynamic and tire subsystems. The quarter vehicle model was then validated using an instrumented experimental vehicle based on the driver input from brake and throttle pedals. Vehicle transient handling dynamic tests known as sudden braking test was performed for the purpose of validation. Several behaviors of the vehicle dynamics were observed during braking maneuvers such as body longitudinal velocity, wheel linear velocity and tire longitudinal slip at a quarter of the vehicle. Comparisons of the experimental results and model responses with sudden braking imposed motions were made. Consequently, the trends between simulation results and experimental data were found almost similar with an acceptable level of error for the application at hand.

Driveline modeling and estimation of individual clutch torque during gear shifts for dual clutch transmission

The purpose of the proposed work centers on the unprecedented task of accurately estimating the torque transmitted through each clutch of the dual clutch transmission. This is to possibly improve the clutch control performance during vehicle launch and gearshifts and to elongate the clutch life. Such goal is attained by only using the measurements and data that are already available in current production vehicles. The suggested estimator requires the speed measurements of engine, input shafts, and wheels, and nominal engine torque information obtained as a function of driver input and engine speed. By synthesizing the estimations obtained by shaft model-based observer, unknown input observers, and adaptive output torque observer, the novel algorithm to estimate the torque of each clutch separately is developed. Stability of the entire combined observer system is analyzed as well. In the process of developing the transfer shaft model-based observer, an original approach for the driveline modeling is proposed. The driveline modeling procedures include compliance model of each transfer shafts with the convenient methods to express the clutch torque without having to use complex clutch friction models, and the developed model is compared with the actual experiment data to validate its accuracy. The effectiveness of the individual clutch torque estimator is demonstrated both through simulations using SimDriveline, and tests on an actual vehicle equipped with a dual clutch transmission.

Linear Quadratic Optimal and Risk-Sensitive Control for Vehicle Active Suspensions

This paper is concerned with time-domain optimal control of active suspensions. The optimal control problem formulation is generalized by incorporating both road disturbances (ride quality) and a representation of driver inputs (handling quality) into the optimal control formulation. A regular optimal control problem as well as a risk-sensitive exponential optimal control performance index is considered. Emphasis is placed on practical considerations including the issue of state estimation in the presence of load disturbances (driver inputs).

X-ray laser driven gold targets

The femtosecond population dynamics of gold irradiated by a coherent high-intensity (>1017 W/cm2) x-ray laser pulse is investigated theoretically. There are two aspects to the assembled model. One is the construction of a detailed model of platinum-like gold inclusive of all inner-shell states that are created by photoionization of atomic gold and decay either by radiative or Auger processes. Second is the computation of the population dynamics that ensues when an x-ray pulse is absorbed in gold. The hole state generation depends on the intensity and wavelength of the driving x-ray pulse. The excited state populations reached during a few femtosecond timescales are high enough to generate population inversions, whose gain coefficients are calculated. These amplified lines in the emitted x-ray spectrum provide important diagnostics of the radiation dynamics and also suggest a nonlinear way to increase the frequency of the coherent output x-ray pulses relative to the frequency of the driver input x-ray pulse.