Traffic Cop is a game where the player become a traffic police officer trainee and direct the traffic to reduce the car accident rate in MA. The game is a top-down view traffic simulation and the player need to respond to the violations correctly. The game is aimed at new drivers ages from 16-24 and the purpose is to change the drivers' attitude about safe driving. Player will learn what they should stay focused on while driving and what the consequences may be when they choose to violate the traffic signal.

Wide spread consumer adoption of self-driving cars (SDC) is predicated on a level of trust between humans and the autonomous vehicle. Despite advances being made in the technical abilities of SDCs, recent studies indicate that people are negatively predisposed toward utilizing autonomous vehicles. To bridge the gap between consumer skepticism and adoption of SDCs, research is needed to better understand the evolution of trust between humans and growing autonomous technologies. The question of mainstream acceptance and requisite trust is explored through integration of virtual reality SDC simulator, an electroencephalographic (EEG) recorder, and a new approach for real-time trust measurement between passengers and SDCs. An experiment on fifty human subjects was conducted where participants were exposed to scenarios designed to induce positive and negative trust responses. Emotional state was quantified by the EEG beta wave to alpha wave power ratio, and participants self-reported their levels of trust in the SDC after each segment. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

For a little over the past decade since the DARPA Grand Challenge in 2004 and the more successful Urban Challenge in 2007 autonomous vehicles have seen a surge in popularity with car manufacturers, and companies such as Google and Uber. Light Detection And Ranging (LiDAR) has been one of the major sensors in use to sense for acting on the surrounding environment instead of the classic radar which has a much narrower field of vision. However the cost of the higher end 3D LiDAR systems which started seeing use during the DARPA challenges still have the high cost of $70,000 a piece which is an issue when trying to design a consumer friendly system on a family car. This work aims to investigate alternate 2D LiDAR systems to the costly systems currently in use in many prototypes to find a cost efficient alternative that can detect and track obstacles in front of a vehicle. The introduction begins by summarizing some related prior works, particularly papers from after the Grand Challenge as well as some about the competition itself. Detection and tracking methods for point clouds generated by the LiDAR are explored including ways to search through the data in an efficient manner to meet real-time constraints. Some of the trade-offs in going from a 3D system to a 2D system and examined along with how some of the drawbacks can be mitigated.

Dans un contexte où la consommation de carburant est un poste de coût de plus en plus important, la consommation et la vitesse moyenne d'un poids-lourds est l'une des caractéristiques clés estimées durant le développement des nouveaux véhicules. Ainsi, nous désirons créer différents modèles de conducteurs en termes de consommation et de vitesse moyenne, c'est à dire en termes de conduite rationnelle. Nous proposons une méthode en deux étapes: premièrement la reconnaissance des évènements de conduite grâce à des attributs de conduite. Puis la quantification de trois types de conduite différents sur ces évènements. Suite à ces deux étapes, nous pouvons implémenter nos résultats dans un outil qui crée différents modèles de conducteurs pour la simulation et le banc à rouleaux. Les écarts entre conducteurs se mesurent en termes de consommation de carburant et de vitesse moyenne. Le taux de classification des évènements s'étend de 74% à 91% selon le type d'évènements. Ces résultats sont dus à la nature même des données et aux similarités entre les classes, mais nous estimons que ces taux sont suffisants pour notre application. Nous obtenons également des corrélations prometteuses entre les attributs de conduite sélectionnés et l'indicateur de conduite rationnelle. Nous avons notamment porté notre étude sur les évènements classiques: les ronds-points, les péages et les arrêts. Les résultats de l'outil que nous avons développé sont pertinents. Nous pouvons désormais simuler différents types de chauffeurs. Sur nos essais en simulation, l'adaptation de seulement 10% des évènements d'un cycle découle sur un gain en consommation de 1.5% et une vitesse moyenne 3% plus élevée pour un conducteur efficace. Ces résultats sont encourageants, surtout que le travail à venir visera à augmenter la diversité des évènements couverts. / Realistic driver behavior modeling for energy efficiency estimation during heavy-trucks vehicles development Abstract: In the context where fuel consumption is a growing cost center, fuel consumption of a truck coupled with its average speed is one of the key vehicle characteristics that needs to be optimized and accurately estimated during the truck design process. Consequently, we aim to create different driver behavior models for testing trucks regarding fuel consumption and average speed issues, i.e., rational driving. We propose a two-step method to model more accurately driving behavior: first, the identification of driving events through driving features. Second, the quantification of three different driving behaviors on the recognized driving events. Then we implement our results in a tool that creates these different driving behaviors. The output of this tool is a cycle adapted to a driver type in terms of fuel consumption and average speed, and that can be used in simulation and on chassis-dynamometer. The classification of driving events reaches classification rates between 74% and 91% depending on the events. We believe that they are sufficient for our application due to the raw nature of driving events and the similarities between the different classes. We also obtain promising results concerning the correlation between driving features and rational driving index. We focus especially on typical events, namely roundabout on extra-urban roads, toll on highways and stop on urban roads. The results of the developed tool prove to be efficient since we can now simulate different driving behaviors. On our test run in simulation, adapting only 10% of the events of a cycle produces fuel savings of 1.5% and an average speed which is 3% faster for an efficient driver than a non-efficient driver. These results are promising and we need to implement other events in the future.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Driving is a complex visuomotor task, and the study of eye movements can provide interesting and detailed insights into driving behaviour. The aim of this thesis was to understand (a) what methods are useful to assess driving behaviour, (b) the reasons we observe differences in eye movements when driving, and (c) offer a possible visual training method. The first experiment compared drivers' eye movements and hazard perception performance in an active simulated driving task and a passive video driving task. A number of differences were found, including an extended horizontal and vertical visual search and faster response to the hazards in the video task. It was concluded that when measuring driving behaviour in an active task, vision, attention and action interact in a complex manner that is reflected in a specific pattern of eye movements that is different to when driving behaviour is measured using typical video paradigms. The second experiment investigated how cognitive functioning may influence eye movement behaviour when driving. It was found that those with better cognitive functioning exhibited more efficient eye movement behaviour than those with poorer cognitive functioning. The third experiment compared the eye movement and driving behaviour of an older adult population and a younger adult population. There were no differences in the eye movement behaviour. However, the older adults drove significantly slower, suggesting attentional compensation. The final experiment investigated the efficacy of using eye movement videos as a visual training tool for novice drivers. It was found that novice drivers improved their visual search strategy when driving after viewing videos of an expert driver's eye movements. The results of this thesis helps to provide insights into how the visual system is used for a complex behaviour such as driving. It also furthers the understanding of what may contribute to, and what may prevent, road accidents.

Chan, Kai Chi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 91-98). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.3 --- Literature Review --- p.7 / Chapter 1.3.1 --- Headlamp Preference Investigation --- p.7 / Chapter 1.3.2 --- Vehicle Dynamic System --- p.10 / Chapter 1.3.3 --- Inertial Navigation Systems --- p.11 / Chapter 1.4 --- Objective --- p.12 / Chapter 1.5 --- A Sensor Based Method --- p.14 / Chapter 1.5.1 --- Accelerometer --- p.14 / Chapter 1.5.2 --- Lighting --- p.16 / Chapter 1.5.3 --- System Design --- p.19 / Chapter 1.6 --- Thesis Organization --- p.20 / Chapter 1.7 --- Achievement and Contributions --- p.21 / Chapter 2 --- Methodology --- p.23 / Chapter 2.1 --- Kinematics of a Turning Car --- p.24 / Chapter 2.2 --- Headlamp Direction Prediction --- p.27 / Chapter 2.2.1 --- Steering Wheel Angle Measurement --- p.28 / Chapter 2.2.2 --- Steering Wheel Angle Stabilization --- p.31 / Chapter 2.2.3 --- Auxiliary Headlamps Control --- p.36 / Chapter 3 --- Implementation --- p.48 / Chapter 3.1 --- Hardware Configuration --- p.49 / Chapter 3.2 --- Design Framework --- p.51 / Chapter 3.3 --- Night Drive Simulator --- p.55 / Chapter 3.3.1 --- Simulator Configuration --- p.56 / Chapter 3.3.2 --- Turning Path Prediction --- p.61 / Chapter 3.3.3 --- Auxiliary Headlamps Control --- p.63 / Chapter 4 --- Experiments --- p.65 / Chapter 4.1 --- Steering Wheel Angle Measurement --- p.66 / Chapter 4.1.1 --- Experiment Setup --- p.66 / Chapter 4.1.2 --- Evaluation Results --- p.68 / Chapter 4.2 --- Auxiliary Headlamps Prediction --- p.71 / Chapter 4.2.1 --- Simulation --- p.72 / Chapter 4.2.2 --- Test Drive --- p.76 / Chapter 5 --- Conclusions --- p.86 / Chapter 5.1 --- Summary --- p.86 / Chapter 5.2 --- Limitations and Future Works --- p.87 / Chapter 5.2.1 --- Headlamp of LEDs --- p.87 / Chapter 5.2.2 --- Simple Car Model --- p.88 / Chapter 5.2.3 --- Response Time of Filtering --- p.88 / Chapter 5.2.4 --- Test Drive --- p.89 / Chapter 6 --- Publications --- p.90 / Bibliography --- p.91

The purpose of this study was to examine the bidirectional effects of simulated driving and conversations using different speaking modalities. Participants included 30 males and 30 females with no history of speech, language or hearing disorders. The participants were divided into three age groups: 20s, 40s, and 60s. They completed a driving simulation task in isolation and also while speaking on a hand-held or hands-free cell phone or with a passenger in the car. Speech measures included speaking time ratio, mean, and standard deviation of intensity, as well as mean and standard deviation of fundamental frequency in semitones. Driving measures included standard deviation of lane position, mean, and standard deviation of speed, standard deviation of steering wheel position, and the average number of steering wheel turns. There were significant effects of speaking while driving on mean intensity, speaking time ratio, standard deviation of steering wheel position, and the number of steering wheel turns. There were significant gender effects for speaking time ratio, standard deviation of intensity, and mean intensity, with the females having higher speaking time ratios, and the males having a higher standard deviation and mean of intensity. There was a significant age effect for mean fundamental frequency, standard deviation of lane position, and the standard deviation of steering wheel position. For mean fundamental frequency, the 60s group were lower than the 20s group. The 60s group had a higher standard deviation of lane position and standard deviation of steering wheel position. These findings reveal effects on both speaking and driving performance when speaking and driving concurrently. This has potential clinical implications for planning therapy activities that will help individuals generalize their learned skills from quiet, distraction-free clinic rooms to more realistic situations with distractions and background noise.

Automotive manufacturers are competing to be the first to introduce customer-ready autonomous vehicles. Some manufacturers are claiming to launch their first self-driving cars as early as 2020. Which all sounds very good and futuristic; however, the question arises, are customers even ready to adopt this new technological advancement? Therefore, this pilot study is aimed at finding out the answer to this question in the Austrian market. This study discovers the standpoint of Austrian consumers concerning the acceptance of self-driving cars for daily usage and gives an overview of the current point of view regarding autonomous vehicles (AVs). The data for this study was collected using an online, user-friendly, Likert scale survey. The collected data were processed and analyzed for empirical significance in SPSS using Spearman's rank correlation and the Mann-Whitney U test supported by descriptive analysis. The results of the study indicate that Austrian consumers are well aware of autonomous vehicles and their technology. However, they have specific concerns about reliability, cybersecurity, and futuristic car-sharing models. Therefore, these concerns about AVs should be addressed by auto manufactures in order to gain consumers' trust and sell them a new form of mobility.

Licensed drivers arrested for driving under the influence (DUI) of alcohol have increased rates of vehicle crashes, moving violations, traffic tickets, and contribute to an estimated 120 million occurrences of impaired driving per year (Evans, 2004; Jewett et al., 2015). Survey research on DUI offenders indicates traits of impulsivity (e.g., sensation seeking). Together, these pieces of evidence suggest that DUI offenders display patterns of impulsive action and risk-taking while driving. However, to-date DUI offenders are rarely studied in a laboratory setting, and not much is known about how they respond to a dose of alcohol. The present study examined the degree to which DUI offenders display an increased sensitivity to the acute impairing effects of alcohol on mechanisms of behavioral impulsivity, skill and risk-based driving simulations, and subjective evaluations of driving fitness and perceived intoxication following alcohol consumption. A sample of 20 DUI offenders were compared to a demographically-matched sample of 20 control drivers. All participants attended two dose sessions in which they received either a 0.65 g/kg dose of alcohol or a placebo dose, counterbalanced, on separate days. Results indicated that alcohol affected all of the behavioral outcome measures. More specifically, alcohol increased impulsive choice responses and decreased response inhibition on the behavioral impulsivity tasks. Alcohol also increased risky driving behaviors and decreased driving-related skills. Furthermore, alcohol generally decreased participants’ self-reported willingness and ability to drive a motor vehicle, and increased levels of intoxication and BAC estimations relative to placebo. With regard to group differences, DUI offenders showed an increased sensitivity to the disrupting effects of alcohol on impulsive choices, such that DUI offenders showed a significantly greater preference for impulsive choices under alcohol relative to placebo than controls. Taken together, these findings provide some of the first pieces of evidence that compared to controls, DUI offenders display an increased tendency for impulsive decisions under alcohol, which likely contributes to risky decisions to drive after drinking, despite clear evidence for their behavioral impairment. These findings could have important implications for understanding the mechanisms underlying maladaptive behaviors in this high-risk population, and sheds light on possible targets for intervention to reduce DUI recidivism.