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

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; the second part of the series presented systematic data collection approaches to compute the uncertainties in the OCV to state of charge (SOC) models. This paper uses data collected from 28 OCV characterization experiments, performed according to the data collection plan presented in the second part, to compute and analyze three OCV uncertainty metrics: cell-to-cell variations, C-Rate error, and curve fitting error. The computed metrics showed that a lower C-Rate resulted in 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. Further, it was observed that the magnitude of cell-to-cell variations varied with the battery SOC and that it was not significantly affected by the C-Rate at which the experimental data was collected. The analysis in this paper also found that widely used polynomial modeling approaches for OCV-SOC curve modeling, incur significant errors. The approaches and results presented in this paper can be 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 accurately characterize the uncertainty in various BMS functionalities, such as SOC and state of health estimation. The insights presented in this paper can be useful to collect more accurate data for training machine learning models in SOC estimation

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

Approach for Rigorous Evaluation of a Battery Fuel Gauge

A battery management system (BMS) is crucial for the safe and reliable operation of a battery pack. During use, it is important to monitor the remaining charge in the battery, known as the state of charge (SOC), to preserve battery health and lifetime. However, the SOC of a battery cannot be directly measured and it is approximated by the battery fuel gauge (BFG) using several empirical approaches. The accuracy of the SOC calculated by the BFG is affected by (i) temperature (ii) charging/usage history (iii) hysteresis and relaxation effects. Evaluating the SOC values reported by a BFG remains a challenging problem due to the fact that it is not possible to know the true SOC value. Consequently, indirect measures were developed to evaluate the SOC estimates reported by a BFG. In this paper, three BFG evaluation metrics: the Coulomb counting (CC) metric, the open circuit voltage (OCV) metric and the time-to-voltage (TTV) metric are demonstrated. The present paper is focused on demonstrating the implementation details of the above three BFG evaluation metrics. The proposed metrics are modified versions of previously reported ones to make the BFG evaluation more robust. Voltage and current data generated from a battery simulator and a BFG based on the extended Kalman filter algorithms were employed to demonstrate the proposed evaluation scheme. The battery in the simulator is set to an Rint approximation of the equivalent circuit model (ECM) and the BFG is set to assume the knowledge of the ECM model parameters. Voltage and current measurements were simulated based on a noisy model with zero mean and known standard deviation. Under these assumptions, the BFG under evaluation produced less than 1% error in SOC and less than 15 minutes in TTV error. These values, produced under the known model assumption, can be taken as a benchmark for the same voltage and current measurement noise statistics

Performance Comparison of Open-Circuit Voltage Modelling of Li-ion Batteries at Different C-Rates

The characterization of a battery to estimate its capacity is a crucial step in open-circuit voltage modelling. The battery capacity estimation is essential to determine diagnostic details on the battery and in determining several other battery parameters. Past research has shown that a normalized open-circuit voltage characterization independent of temperature is also dependent on accurate capacity estimation. In most works, the normalized OCV characterization approaches were done at C/30 rates where the entire data collection took approximately 60 hours. The undesirably long data collection process motivated the need to determine the expected accuracy at lower C-rates in realistic conditions. However, little attention was paid in the literature to investigate capacity estimation error at various C-rates. Thus, in this paper, the battery capacity estimation is repeated at seven C-rates: C/2, C/4, C/8, C/16, C/32, C/64 and C/128, to compare their accuracy using data collected from a laboratory-based battery cycler. It was found that with a lower current rate, a maximum of 0.3 Ah error is observed in the charge capacity. An error of 0.16 Ah was observed for the discharge capacity at the lowest C-rate of C/2 A

A Fast OCV Characterization Approach for Battery Reuse Applications

This paper considers the problem of open circuit voltage (OCV) to state of charge(SOC) characterization in rechargeable batteries for battery reuse applications. The traditional approach to OCV-SOC characterization is done by collecting voltage and current data through a slow discharge and charge process; this process usually takes about 60 hours. Such OCV-SOC characterization is performed on a few sample batteries because the OCV-SOC characterization is the same for new batteries coming out of the same manufacturing process. However, the characteristics of a battery may change as it is used for years in different environmental and usage conditions. Hence, they may need to be re-characterized before secondary use. Unlike primary characterization, secondary characterization may have to be done faster in order to save time and cost. This paper presents a new and faster approach for OCV-SOC characterization. The proposed approach in this paper consists of constant current profiles that halve in magnitude after a specified time. Such reducing current magnitude allows for fully depleting the battery; similarly, the battery is charged back with a reducing current profile in order to make sure the battery is fully charged back. The resulting current profile reduces the total characterization time by 1/5. It was hypothesized that the changing current magnitude may result in hysteresis voltage bias. For this, a new OCV modelling approach consisting of separate resistance estimation at each pulse was developed. The proposed approach was tested using data collected from four cylindrical Li-ion batteries. Compared to the traditional OCV modelling approach, the proposed approach results in 3% of SOC error and takes 20% of the time