Abstract

Various neurological disorders, like Parkinson's disease, Multiple Sclerosis, Huntington's disease and Stroke, affect millions of people worldwide. Tremor that is a result of these disorders affects the performance of many Activities of Daily Living such as dressing, bathing, self-care, and writing, which reduces the functional independence and self-rated quality of life. Standardized rating scales have been developed, however, these scales display some degree of variability due to their subjective/qualitative approach. Therefore, the accurate and objective measure of a patient's condition is crucial. Due to the lack of objectivity and accuracy from conventional procedures, there is a need to develop an objective evaluation system. In this work, a horizontal movement test is implemented in a Virtual Environment with the use of a Haptic Interface. The proposed test consists of a simple reaching task (more tasks are under development) for defining quantitative metrics. Wrist motion is accurately measured using the haptic interface and analyzed to calculate evaluation metrics based on the joint energy and spatial deviation from the ideal path. To improve the sensitivity of the metrics, a harmonic disturbance force is applied by the haptic interface to the user. The disturbance frequency is varied from 1 to 7 Hz and the duration of the movement is constraint to be constant. Fourteen healthy adults performed the experiments with 10 to 21 repetitions for each movement conditions. The results show that all users spend higher energy to complete the test at frequencies around 2.5 Hz. The statistical analysis indicates that energy is a reliable evaluation metric, with low variance, that can be used to quantify upper extremities. Copyright © 2017 ASME. 1

Abstract

Date of Conference: 11–13 October 2017 Conference Name: ASME 2017 Dynamic Systems and Control Conference, DSCC 2017 Model reference adaptive control (MRAC) can effectively handle various challenges of the real world control problems including exogenous disturbances, system uncertainties, and degraded modes of operations. In human-in-the-loop settings, MRAC may cause unstable system trajectories. Basing on our recent work on the stability of MRAC-human dynamics, here we follow an optimization based computations to design a linear filter and study whether or not this filter inserted between the human model and MRAC could help remove such instabilities, and potentially improve performance. To this end, we present a mathematical approach to study how the error dynamics of MRAC could favorably or detrimentally influence human operator's error dynamics in performing a certain task. An illustrative numerical example concludes the study.

Abstract

Date of Conference: 11–13 October 2017 Conference Name: ASME 2017 Dynamic Systems and Control Conference, DSCC 2017 The paper presents an approach to the Model Reference Adaptive Control (MRAC) design for nonlinear dynamical systems. A nonlinear reference system is considered such that its response is designed to be stable via Successive Approximation Approach (SAA). Having designed the stable reference model through the SAA, MRAC is then formulated for nonlinear plant dynamics with a new adaptation rule to guarantee the convergence of the nonlinear plant response to that of the response of the nonlinear reference model. The proposed design methodology is illustrated with examples for different case studies.

Abstract

This paper presents a microscopic traffic model for the validation of advanced driver assistance systems. This model describes single-lane traffic and is calibrated with data from a field operational test. To illustrate the use of the model, a Monte Carlo simulation of single-lane traffic scenarios is executed with application to cooperative adaptive cruise control system. The model is then validated by comparing the simulation results with data gathered from test drives. ©2007 IEEE.

Abstract

dvanced Driver Assistance Systems (ADAS) are becoming ubiquitous, and gradually take over the role of human drivers in the vision of the automotive sector. Humans are different from most systems: while in general humans exhibit a much higher error rate when performing specific functions, they are also unmatched in their adaptability, and their ability to recognize patterns and anticipate on these. In this paper we derive temporal requirements on future ADAS operation, needed to at least match human driving behavior in dense traffic. We examine Adaptive Cruise Control and Automated Emergency Braking systems at highway speeds, derive temporal requirements, and show that in dense traffic situations intent communication has a significant benefit to improve systems operation. The resulting requirements will challenge ADAS developments in the coming years. cop. 2015 IEEE.

Abstract

n increasing amount of vehicles are equipped with driver assistance systems; many of the vehicles currently on the market can be optionally equipped with adaptive cruise control and lane centering systems. Using both systems at the same time brings the vehicle to SAE level-2 automation. This means a driver does not need to perform longitudinal and lateral operational driving, although the driver should be ready to intervene at any time. While this can provide comfort, the interaction between vehicles operated by these systems might cause some undesired effects. This becomes particularly relevant with increasing market penetration rates. This paper describes an experiment with seven SAE level-2 vehicles driven as a platoon on public roads for a trip of almost 500 km. The paper discusses how the experiment was organized and the equipment of the vehicles. It also discusses the interaction of the platoon in traffic, as well as, in basic terms, the interaction between the automated vehicles. The experiences can be useful for other studies setting up field tests. The conclusion from this platoon test is: intentionally creating platoons on public roads is difficult in busy traffic conditions. Moreover, interactions between the vehicles in the platoon show that the current SAE level-2 systems are not suitable for driving as platoons of more than typically three to four vehicles, because of instabilities in the car-following behavior. © National Academy of Sciences: Transportation Research Board 2019.

Abstract

More and more Advanced Driver Assistance Systems (ADAS) are entering the market for improving both safety and comfort by assisting the driver with their driving task. An important aspect in developing future ADAS and Automated Driving Systems (ADS) is testing and validation. Validating the failure rate of an ADS requires so many operational hours that testing in real time is almost impossible. One way to reduce this test load is virtual testing or hardware in-The-loop testing. The major challenge is to create realistic test cases that closely resemble the situation on the road. We present a way to use data of naturalistic driving to generate test cases for Monte-Carlo simulations of ADS. Because real-life data is used, the assessment allows to draw conclusions on how the ADS would perform in real traffic. The method, developed in EU AdaptIVe, is demonstrated by testing an Adaptive Cruise Control (ACC) system in scenarios where the predecessor of the ego vehicle is braking. We show that the probability of the occurrence of unsafe situations with the ACC system can be accurately and efficiently determined.