Analysis of the Effects of Adaptive Cruise Control (ACC) on Driver Behavior and Awareness Using a Driving Simulator

The thesis was aimed at determining the effects of adaptive cruise control (ACC) on driver behavior and awareness using a fixed-base driving simulator. ACC provides enhanced assistance by automatically adjusting vehicle speed according to the headway preference selected by the driver. The first step was to define the qualitative and quantitative measures of driver behavior and awareness. A review of existing literature was carried out to determine similar studies. The literature revealed information on modeling the ACC in driving simulators and the effects of the ACC on driver behavior. Based on this, a methodology was developed consisting of six main tasks. First, participants were recruited and screened using a questionnaire. The questionnaire provided a quick way to select participants from a particular demographic and screen them for any medical conditions. The simulator was then prepared for the study by configuring the ACC, setting up the detection response task (DRT) device, configuring the distraction application, and designing events targeted to capture changes in driver behavior and awareness with and without the ACC. After configuring events, data were collected during the drive of the participants. Data were then reduced and prepared for a statistical analysis consisting of hypothesis testing and analysis of variance (ANOVA). The statistical analysis resulted in a few significant differences between the variables collected. Participants were observed to maintain longer headways, reach lower peak velocities, and react slower in some critical events when driving with the ACC. The data from the DRT showed a significantly lower cognitive load when participants were engaged in a secondary task and driving with the ACC when compared to driving without the ACC

Predicting Sleepiness from Driving Behaviour

This research investigates the use of objective EEG analysis to determine multiple levels of sleepiness in drivers. In the literature, current methods propose a binary (awake or sleep) or ternary (awake, drowsy or sleep) classification of sleepiness. Having few classification of sleepiness increases the risk of the driver reaching dangerous levels of sleepiness before a safety system can prevent it. Also, these methods are based on subjective analysis of physiological variables, which leads to lack of reproducibility and loss of data, when a lack of consensus is reached amongst the EEG experts. Therefore, the doctoral challenge was to determine whether multiple levels of sleepiness could be defined with high accuracy, using an objective analysis of EEG, a reliable indicator of sleepiness. The study identified awake, post-awake, pre-sleep and sleep as the multiple levels of sleepiness through the objective analysis of EEG. The research used Neural Networks, a type of Machine Learning algorithm, to determine the accuracy of the proposed multiple levels of sleepiness. The Neural Networks were trained using driving and physiological behaviour. The EEG data and the driving and physiological variables were obtained through a series of experiments aimed to induce sleepiness, conducted in the driving simulator at the University of Leeds. As the Neural Network obtained high accuracy when differentiating between awake and sleep and between post-awake and pre-sleep, it led to the conclusion that the proposed objective classification based on objective EEG analysis was suitable. However, this study did not reach the highest levels of accuracy when the 4 levels of sleepiness are combined, nevertheless the solutions proposed by the researcher to be carried in future work can contribute towards increasing the accuracy of the proposed method

A Human-Centric Approach to Software Vulnerability Discovery

Software security bugs | referred to as vulnerabilities | persist as an important and costly challenge. Significant effort has been exerted toward automatic vulnerability discovery, but human intelligence generally remains required and will remain necessary for the foreseeable future. Therefore, many companies have turned to internal and external (e.g., penetration testing, bug bounties) security experts to manually analyze their code for vulnerabilities. Unfortunately, there are a limited number of qualified experts. Therefore, to improve software security, we must understand how experts search for vulnerabilities and how their processes could be made more efficient, by improving tool usability and targeting the most common vulnerabilities. Additionally, we seek to understand how to improve training to increase the number of experts. To answer these questions, I begin with an in-depth qualitative analysis of secure development competition submissions to identify common vulnerabilities developers introduce. I found developers struggle to understand and implement complex security concepts, not recognizing how nuanced development decisions could lead to vulnerabilities. Next, using a cognitive task analysis to investigate experts' and non-experts' vulnerability discovery processes, I observed they use the same process, but dier in the variety of security experiences which inform their searches. Together, these results suggest exposure to an in-depth understanding of potential vulnerabilities as essential for vulnerability discovery. As a first step to leverage both experts and non-experts, I pursued two lines of work: education to support experience development and vulnerability discovery automation interaction improvements. To improve vulnerability discovery tool interaction, I conducted observational interviews of experts' reverse engineering process, an essential and time-consuming component of vulnerability discovery. From this, I provide guidelines for more usable interaction design. For security education, I began with a pedagogical review of security exercises to identify their current strengths and weaknesses. I also developed a psychometric measure for secure software development self-efficacy to support comparisons between educational interventions