Novel Approaches to Cognitive Load Estimation in Automated Driving Systems

Automation has become indispensable in all walks of everyday life. In driving environments, Automated Driving Systems (ADS) aid the driver by reducing the required workload and by improving road safety. However, the present-day ADS requires the human driver to remain vigilant at all times and be ready to take over whenever the driving task requires. Thus, continuous monitoring of the drivers is important for adopting ADS. Such monitoring can be done in ADS by measuring the cognitive load experienced by the drivers. Studies show various methods to estimate cognitive load, however, the state of the art in cognitive load estimation, particularly, the non-invasive ones suitable for ADS, still suffer from significant deficiencies. Thus, more research to improve the accuracy of cognitive load estimators is crucial for allowing the safe adoption of ADS. This thesis contains the analysis of non-invasive metrics that can be used as reliable indicators of cognitive load. Eye-tracking measures such as pupil size, eye-gaze, and eye-blinks from low-cost eye-trackers are analyzed. In addition to eye-tracking data, heart rate is also studied as an estimator of cognitive load. Furthermore, this thesis introduces a novel model-based approach to filter noisy physiological measurements for the real-time monitoring of cognitive load. The proposed measures will be beneficial to the development of more accurate metrics for cognitive load estimation, thereby contributing to the advancement of ADS. The thesis also contains a detailed description of two datasets collected at the HSLab.These datasets will be helpful to researchers interested in employing machine learning algorithms to develop predictive models of humans for applications in human-machine automation

Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-2: Data Collection Procedure

This paper is the second part of a series of papers about empirical approaches to open circuit voltage (OCV) modeling and its performance comparison in lithium-ion batteries. The first part of the series introduced various sources of uncertainties in the OCV models and established a theoretical relationship between uncertainties and the performance of a battery management system. In this paper, clearly defined approaches for low-rate OCV data collection are defined and described in detail. The data collection is designed with consideration to several parameters that affect the experimental time. Firstly, a more suitable method to fully charge the battery at different C-Rates is defined. Secondly, the OCV characterization following the full charge is described for various performance comparisons. Finally, optimal and efficient resistance estimation profiles are discussed. From the voltage, current and time data recorded using the procedure described in this paper, the OCV-SOC relationship is characterized and its uncertainties are modeled in the third part of this series of papers

On validating a generic camera-based blink detection system for cognitive load assessment

Detecting the human operator\u27s cognitive state is paramount in settings wherein maintaining optimal workload is necessary for task performance. Blink rate is an established metric of cognitive load, with a higher blink frequency being observed under conditions of greater workload. Measuring blink rate requires the use of eye-trackers which limits the adoption of this metric in the real-world. The authors aim to investigate the effectiveness of using a generic camera-based system as a way to assess the user\u27s cognitive load during a computer task. Participants completed a mental task while sitting in front of a computer. Blink rate was recorded via both the generic camera-based system and a scientific-grade eye-tracker for validation purposes. Cognitive load was also assessed through the performance in a single stimulus detection task. The blink rate recorded via the generic camera-based approach did not differ from the one obtained through the eye-tracker. No meaningful changes in blink rate were however observed with increasing cognitive load. Results show the generic-camera based system may represent a more affordable, ubiquitous means for assessing cognitive workload during computer task. Future work should further investigate ways to increase its accuracy during the completion of more realistic tasks

Response time and eye tracking datasets for activities demanding varying cognitive load

The dataset contains the following three measures that are widely used to determine cognitive load in humans: Detection Response Task - response time, pupil diameter, and eye gaze. These measures were recorded from 28 participants while they underwent tasks that are designed to permeate three different cognitive difficulty levels. The dataset will be useful to those researchers who seek to employ low cost, non-invasive sensors to detect cognitive load in humans and to develop algorithms for human-system automation. One such application is found in Advanced Driver Assistance Systems where eye-trackers are employed to monitor the alertness of the drivers. The dataset would also be helpful to researchers who are interested in employing machine learning algorithms to develop predictive models of humans for applications in human-machine system automation. The data is collected by the authors at the Department of Electrical & Computer Engineering in collaboration with the Faculty of Human Kinetics at the University of Windsor under the guidance of their Research Ethics Board

Novel Approaches to Cognitive Load Estimation in Automated Driving Systems

Automation has become indispensable in all walks of everyday life. In driving environments, Automated Driving Systems (ADS) aid the driver by reducing the required workload and by improving road safety. However, the present-day ADS requires the human driver to remain vigilant at all times and be ready to take over whenever the driving task requires. Thus, continuous monitoring of the drivers is important for adopting ADS. Such monitoring can be done in ADS by measuring the cognitive load experienced by the drivers. Studies show various methods to estimate cognitive load, however, the state of the art in cognitive load estimation, particularly, the non-invasive ones suitable for ADS, still suffer from significant deficiencies. Thus, more research to improve the accuracy of cognitive load estimators is crucial for allowing the safe adoption of ADS. This thesis contains the analysis of non-invasive metrics that can be used as reliable indicators of cognitive load. Eye-tracking measures such as pupil size, eye-gaze, and eye-blinks from low-cost eye-trackers are analyzed. In addition to eye-tracking data, heart rate is also studied as an estimator of cognitive load. Furthermore, this thesis introduces a novel model-based approach to filter noisy physiological measurements for the real-time monitoring of cognitive load. The proposed measures will be beneficial to the development of more accurate metrics for cognitive load estimation, thereby contributing to the advancement of ADS. The thesis also contains a detailed description of two datasets collected at the HSLab.These datasets will be helpful to researchers interested in employing machine learning algorithms to develop predictive models of humans for applications in human-machine automation

Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-3: Experimental Results

This paper is the third part of a series of papers about empirical approaches to open circuit voltage (OCV) modeling of lithium-ion batteries. The first part of the series proposed models to quantify various sources of uncertainties in the OCV models; and, the second part of the series presented systematic data collection approaches to compute the uncertainties in the OCV-SOC models. This paper uses data collected from 28 OCV characterization experiments, performed according to the data collection plan presented, to compute and analyze the following three different OCV uncertainty metrics: cell-to-cell variations, cycle-rate error, and curve fitting error. From the computed metrics, it was observed that a lower C-Rate showed smaller errors in the OCV-SOC model and vice versa. The results reported in this paper establish a relationship between the C-Rate and the uncertainty of the OCV-SOC model. This research can be thus useful to battery researchers for quantifying the tradeoff between the time taken to complete the OCV characterization test and the corresponding uncertainty in the OCV-SOC modeling. Further, quantified uncertainty model parameters can be used to accurately characterize the uncertainty in various battery management functionalities, such as state of charge and state of health estimation

Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-1: Performance Measures

The open circuit voltage to the state of charge (OCVSOC) characteristic is crucial for battery management systems. Using the OCV-SOC curve, the SOC and the battery capacity can be estimated in real-time. Accurate SOC and capacity information are important to carry out the majority of battery management functionalities that ensure a safe, efficient, and reliable battery pack power system. Numerous approaches have been reported in the literature for improved SOC estimation and battery capacity estimation. These approaches focus on various estimation and filtering techniques to reduce the effect of measurement noise and uncertainties due to hysteresis and relaxation effects. Even though all the existing approaches to SOC estimation rely on the OCV-SOC characterization, little attention was paid to investigating the possibility of errors in the OCV-SOC characterization and the effect of uncertainty in the OCV-SOC curve on SOC and capacity estimates. In this paper, which is the first part of a series of three papers, the effect of OCV-SOC modeling error in the overall battery management system is discussed. The different sources of uncertainties in the OCV-SOC curve include cell-to-cell variation, temperature variation, aging drift, cycle rate effect, curve-fitting error, and measurement/estimation error. The proposed uncertainty models can be incorporated into battery management systems to improve their safety, performance, and reliability

Optimizing Current Profiles for Efficient Online Estimation of Battery Equivalent Circuit Model Parameters Based on Cramer–Rao Lower Bound

Battery management systems (BMS) are important for ensuring the safety, efficiency and reliability of a battery pack. Estimating the internal equivalent circuit model (ECM) parameters of a battery, such as the internal open circuit voltage, battery resistance and relaxation parameters, is a crucial requirement in BMSs. Numerous approaches to estimating ECM parameters have been reported in the literature. However, existing approaches consider ECM identification as a joint estimation problem that estimates the state of charge together with the ECM parameters. In this paper, an approach is presented to decouple the problem into ECM identification alone. Using the proposed approach, the internal open circuit voltage and the ECM parameters can be estimated without requiring the knowledge of the state of charge of the battery. The proposed approach is applied to estimate the open circuit voltage and internal resistance of a battery

Open-Circuit Voltage Models for Battery Management Systems: A Review

A battery management system (BMS) plays a crucial role to ensure the safety, efficiency, and reliability of a rechargeable Li-ion battery pack. State of charge (SOC) estimation is an important operation within a BMS. Estimated SOC is required in several BMS operations, such as remaining power and mileage estimation, battery capacity estimation, charge termination, and cell balancing. The open-circuit voltage (OCV) look-up-based SOC estimation approach is widely used in battery management systems. For OCV lookup, the OCV–SOC characteristic is empirically measured and parameterized a priori. The literature shows numerous OCV–SOC models and approaches to characterize them and use them in SOC estimation. However, the selection of an OCV–SOC model must consider several factors: (i) Modeling errors due to approximations, age/temperature effects, and cell-to-cell variations; (ii) Likelihood and severity of errors when the OCV–SOC parameters are rounded; (iii) Computing system requirements to store and process OCV parameters; and (iv) The required computational complexity of real-time OCV lookup algorithms. This paper presents a review of existing OCV–SOC models and proposes a systematic approach to select a suitable OCV–SOC for implementation based on various constraints faced by a BMS designer in practical application

Open-Circuit Voltage Models for Battery Management Systems: A Review

A battery management system (BMS) plays a crucial role to ensure the safety, efficiency, and reliability of a rechargeable Li-ion battery pack. State of charge (SOC) estimation is an important operation within a BMS. Estimated SOC is required in several BMS operations, such as remaining power and mileage estimation, battery capacity estimation, charge termination, and cell balancing. The open-circuit voltage (OCV) look-up-based SOC estimation approach is widely used in battery management systems. For OCV lookup, the OCV–SOC characteristic is empirically measured and parameterized a priori. The literature shows numerous OCV–SOC models and approaches to characterize them and use them in SOC estimation. However, the selection of an OCV–SOC model must consider several factors: (i) Modeling errors due to approximations, age/temperature effects, and cell-to-cell variations; (ii) Likelihood and severity of errors when the OCV–SOC parameters are rounded; (iii) Computing system requirements to store and process OCV parameters; and (iv) The required computational complexity of real-time OCV lookup algorithms. This paper presents a review of existing OCV–SOC models and proposes a systematic approach to select a suitable OCV–SOC for implementation based on various constraints faced by a BMS designer in practical application