Driving Simulator : Driving Performance Under Distraction

This pilot study used a driving simulator experiment to look into how podcast consumption affects driving performance as a continuous distraction. Three volunteers conducted three trials in the study, each with a different driving scenario. Data analysis was done to compare two conditions. The first condition is the Audio, where volunteers listen to podcasts while driving. The second condition is no-audio condition.. The no-audi condition had nothing to play in the background. We used eye-tracking technology to gather gaze data. The study\u27s findings using the post survey and eye fixation data indicate that listening to podcasts leads to continuous distraction while driving which may impair situational awareness and attention to surroundings. On the other hand, the absence of audio stimulation can increase attention and awareness while driving. These findings underline the need for drivers to be aware of audio distractions while driving and have significant consequences for road safety

Research of driver's perception of novel traffic signals using an eye tracker

Novo Mesto is a small town in the Slovenian countryside which is undergoing a new project of road safety. New traffic signals are to be placed in bus stops to give drivers awareness of children standing waiting for the bus. Our mission is to use an eye tracker (the Dikablis device by Ergoneers) to study the reaction of three test drivers against these new signals; if they do detect them or not and if they modify their driving behavior once noticed them. The methodology is to do a first test before the signal placements and another one after, and afterwards compare both situations. Results are satisfying, with high values of reliability, and make us think the new signals will be useful and will contribute to make the road a safer place for children.Outgoin

Modeling eye movements in driving

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (leaves 87-88).by Leandro A. Veltri.M.Eng

Vehicular Instrumentation and Data Processing for the Study of Driver Intent

The primary goal of this thesis is to provide processed experimental data needed to determine whether driver intentionality and driving-related actions can be predicted from quantitative and qualitative analysis of driver behaviour. Towards this end, an instrumented experimental vehicle capable of recording several synchronized streams of data from the surroundings of the vehicle, the driver gaze with head pose and the vehicle state in a naturalistic driving environment was designed and developed. Several driving data sequences in both urban and rural environments were recorded with the instrumented vehicle. These sequences were automatically annotated for relevant artifacts such as lanes, vehicles and safely driveable areas within road lanes. A framework and associated algorithms required for cross-calibrating the gaze tracking system with the world coordinate system mounted on the outdoor stereo system was also designed and implemented, allowing the mapping of the driver gaze with the surrounding environment. This instrumentation is currently being used for the study of driver intent, geared towards the development of driver maneuver prediction models

EYE MOVEMENTS BEHAVIORS IN A DRIVING SIMULATOR DURING SIMPLE AND COMPLEX DISTRACTIONS

Road accidents occur frequently due to driving distractions all around the world. A driving simulator has been created to explore the cognitive effects of distractions while driving in order to address this problem. The purpose of this study is to discover the distraction-causing elements and how they affect driving performance. The simulator offers a secure and regulated setting for carrying out tests while being distracted by different visual distractions, such as solving mathematical equations and number memorizations. Several trials have been conducted in the studies, which were carried out under varied circumstances like varying driving sceneries and by displaying different distractions. Using Tobii Pro Fusion eye tracker, which records the participants\u27 eye movements and pupil dilation to detect distraction events, the cognitive load of distractions was assessed. In order to ascertain how distractions affect driving behavior, the simulator also gathered data on driving performance, such as steering wheel movements. It also gathered data on how much attention was being paid to the distractions by recording the user’s responses to the distractions. The preliminary findings of this study will shed light on the cognitive effects of driving distractions as well as the causes of driver distraction. With the help of this information, initiatives and interventions can be created to lower the prevalence of distracted driving and increase road safety. The results of this pilot study may also aid in the creation of safer standards for using electronic devices while driving and better driver training programs

Human-Centric Detection and Mitigation Approach for Various Levels of Cell Phone-Based Driver Distractions

abstract: Driving a vehicle is a complex task that typically requires several physical interactions and mental tasks. Inattentive driving takes a driver’s attention away from the primary task of driving, which can endanger the safety of driver, passenger(s), as well as pedestrians. According to several traffic safety administration organizations, distracted and inattentive driving are the primary causes of vehicle crashes or near crashes. In this research, a novel approach to detect and mitigate various levels of driving distractions is proposed. This novel approach consists of two main phases: i.) Proposing a system to detect various levels of driver distractions (low, medium, and high) using a machine learning techniques. ii.) Mitigating the effects of driver distractions through the integration of the distracted driving detection algorithm and the existing vehicle safety systems. In phase- 1, vehicle data were collected from an advanced driving simulator and a visual based sensor (webcam) for face monitoring. In addition, data were processed using a machine learning algorithm and a head pose analysis package in MATLAB. Then the model was trained and validated to detect different human operator distraction levels. In phase 2, the detected level of distraction, time to collision (TTC), lane position (LP), and steering entropy (SE) were used as an input to feed the vehicle safety controller that provides an appropriate action to maintain and/or mitigate vehicle safety status. The integrated detection algorithm and vehicle safety controller were then prototyped using MATLAB/SIMULINK for validation. A complete vehicle power train model including the driver’s interaction was replicated, and the outcome from the detection algorithm was fed into the vehicle safety controller. The results show that the vehicle safety system controller reacted and mitigated the vehicle safety status-in closed loop real-time fashion. The simulation results show that the proposed approach is efficient, accurate, and adaptable to dynamic changes resulting from the driver, as well as the vehicle system. This novel approach was applied in order to mitigate the impact of visual and cognitive distractions on the driver performance.Dissertation/ThesisDoctoral Dissertation Applied Psychology 201

Road safety investigation of the interaction between driver and cyclist

With growing global concern to reduce CO2 emissions, the transportation modal shift from car to bicycle is an encouraging alternative, which is getting more popular in Europe and North America, thanks to very low impact on the environment. On the other hand, the infrastructure for cyclist should be improved, since cyclists are vulnerable road users and with an increase in the number of cyclists the concern for their safety also gets increased. In this thesis, the analysis of accidents in which cyclists have been involved and understanding the reason for these accidents have been discussed, then the necessary requirements to design and implement a safe bicycle network is introduced. The study focuses on the drivers’ behavior in terms of interaction with cyclists when there is a presence of a cyclist crossing. Therefore the road safety investigation on cyclist infrastructure was made with observing drivers’interaction with cyclists. Then the time-based surrogacy measures used to investigate the safety level of the cylist, in particular PET (Post Encroachment Time) and TTC (Time to Collision) between driver and bicyclist were determing keeping in mind the right-angle collision. Furthermore we tried to find the reaction time of the drivers especially on signals and also with the presence of cyclist on the crossing to understand the time which is needed for the driver to stop the car. All of this data could be later useful for the reconstruction of the accidents. Understanding the instants at which driver applies the brakes was made possible by installing a V-Box device inside our test vehicle which also used to determine measures such as speed, distance and other important. Finally using mobile eye tracker the driver visual behavior when arriving the crossing point where observed and results showed that at number of situations driver’s gaze was distracted and only cyclist became an important focus only when he was at a considerable length from the crossing

Algorithm for Monitoring Head/Eye Motion for Driver Alertness with one Camera

Visual methods and systems are described for detecting alterness and vigilance of persons under the conditions of fatigue, lack of sleep, and exposure to mind altering substances such as alcohol or drugs. In particular, the invention can have particular application for truck drivers, bus drivers, train operators, pilots and watercraft controllers and stationary heavy equipment operators, and students and employees during either daytime or nighttime conditions. The invention robustly tracks a person\u27s head and facial features with a single on-board camera with a fully automatic system, that can intitalize automatically, and can reinitialize when it needs to and provide outputs in realtime. The system can classify rotation in all viewing directions, detects eye/mouth occlusion, detects eye blinking, and recovers the 3D (three dimensional) gaze of the eyes. In addition, the system is able to track both through occlusion like eye blinking and through occlusion like rotation. Outputs can be visual and sound alarms to the driver directly..

Domain adaptation for driver's gaze mapping for different drivers and new environments

Distracted driving is a leading cause of traffic accidents, and often arises from a lack of visual attention on the road. To enhance road safety, monitoring a driver's visual attention is crucial. Appearance-based gaze estimation using deep learning and Convolutional Neural Networks (CNN) has shown promising results, but it faces challenges when applied to different drivers and environments. In this paper, we propose a domain adaptation-based solution for gaze mapping, which aims to accurately estimate a driver's gaze in diverse drivers and new environments. Our method consists of three steps: pre-processing, facial feature extraction, and gaze region classification. We explore two strategies for input feature extraction, one utilizing the full appearance of the driver and environment and the other focusing on the driver's face. Through unsupervised domain adaptation, we align the feature distributions of the source and target domains using a conditional Generative Adversarial Network (GAN). We conduct experiments on the Driver Gaze Mapping (DGM) dataset and the Columbia Cave-DB dataset to evaluate the performance of our method. The results demonstrate that our proposed method reduces the gaze mapping error, achieves better performance on different drivers and camera positions, and outperforms existing methods. We achieved an average Strictly Correct Estimation Rate (SCER) accuracy of 81.38% and 93.53% and Loosely Correct Estimation Rate (LCER) accuracy of 96.69% and 98.9% for the two strategies, respectively, indicating the effectiveness of our approach in adapting to different domains and camera positions. Our study contributes to the advancement of gaze mapping techniques and provides insights for improving driver safety in various driving scenarios