663 research outputs found
Mobility and Aging: Older Drivers’ Visual Searching, Lane Keeping and Coordination
This thesis examined older drivers’ mobility and behaviour through comprehensive measurements of driver-vehicle-environment interaction and investigated the associations between driving behaviour and cognitive functions. Data were collected and analysed for 50 older drivers using eye tracking, GNSS tracking, and GIS. Results showed that poor selective attention, spatial ability and executive function in older drivers adversely affect lane keeping, visual search and coordination. Visual-motor coordination measure is sensitive and effective for driving assessment in older drivers
Driver Behavior Analysis Based on Real On-Road Driving Data in the Design of Advanced Driving Assistance Systems
The number of vehicles on the roads increases every day. According to the National Highway Traffic Safety Administration (NHTSA), the overwhelming majority of serious crashes (over 94 percent) are caused by human error. The broad aim of this research is to develop a driver behavior model using real on-road data in the design of Advanced Driving Assistance Systems (ADASs). For several decades, these systems have been a focus of many researchers and vehicle manufacturers in order to increase vehicle and road safety and assist drivers in different driving situations. Some studies have concentrated on drivers as the main actor in most driving circumstances. The way a driver monitors the traffic environment partially indicates the level of driver awareness. As an objective, we carry out a quantitative and qualitative analysis of driver behavior to identify the relationship between a driver’s intention and his/her actions. The RoadLAB project developed an instrumented vehicle equipped with On-Board Diagnostic systems (OBD-II), a stereo imaging system, and a non-contact eye tracker system to record some synchronized driving data of the driver cephalo-ocular behavior, the vehicle itself, and traffic environment. We analyze several behavioral features of the drivers to realize the potential relevant relationship between driver behavior and the anticipation of the next driver maneuver as well as to reach a better understanding of driver behavior while in the act of driving. Moreover, we detect and classify road lanes in the urban and suburban areas as they provide contextual information. Our experimental results show that our proposed models reached the F1 score of 84% and the accuracy of 94% for driver maneuver prediction and lane type classification respectively
Impact of Smart Phones’ Interaction Modality on Driving Performance for Conventional and Autonomous Vehicles
Distracted driving related to cell phone usage ranks among the top three causes of fatal crashes on the road. Although forty-eight of 50 U.S. states allow the use of personal devices if operated hands-free and secured in the vehicle, scientific studies have yet to quantify the safety improvement presumed to be introduced by voice-to-text interactions. Thus, this study investigated how different modes of interaction of drivers with a smart phone (i.e., manual texting vs. vocal input) affect drivers’ distraction and performance in both conventional and semi-autonomous vehicles. The study was executed in a full-car integrated simulator and tested a population of 32 drivers. The study considered two scenarios: (1) conventional manual driving in a suburban environment with intersection stops; and (2) control takeover from an engaged autonomous vehicle that reverted to manual driving at a highway exit. The quality of execution of maneuvers as well as timing and tracking of eye-gaze focus areas were assessed in both scenarios. Results demonstrated that while participants perceived an increased level of safety while using the hands-free interface, response times and drift did not significantly differ from those manually texting. Furthermore, even though participants perceived a greater effort in accomplishing the text reply through the manual interface, none of the measured quantities for driving performance or eye-gaze focus revealed statistical difference between the two interfaces, ultimately calling into question the assumption of greater safety implicit in the laws allowing hands-free devices
Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?
This study demonstrates the need for a holistic safety-impact assessment of an advanced driver assistance system (ADAS) and its effect on eye-glance behaviour. It implements a substantial incremental development of the what-if (counterfactual) simulation methodology, applied to rear-end crashes from the SHRP2 naturalistic driving data. This assessment combines (i) the impact of the change in drivers’ off-road glance behaviour due to the presence of the ADAS, and (ii) the safety impact of the ADAS alone. The results illustrate how the safety benefit of forward collision warning and autonomous emergency braking, in combination with adaptive cruise control (ACC) and driver assist (DA) systems, may almost completely dominate the safety impact of the longer off-road glances that activated ACC and DA systems may induce. Further, this effect is shown to be robust to induced system failures. The accuracy of these results is tempered by outlined limitations, which future estimations will benefit from addressing. On the whole, this study is a further step towards a successively more accurate holistic risk assessment which includes driver behavioural responses such as off-road glances together with the safety effects provided by the ADAS
Driver Attention Assessment from Gaze and Situational Variables
Fahrer, die der Fahrsituation nicht genügend Aufmerksamkeit widmen, stellen eine Gefahr für die Verkehrssicherheit dar. Dies liegt daran, dass in diesem Fall das Fahrvermögen der Betroffenen deutlich verringert ist, was in Folge zu einem erhöhten Unfallrisiko führt. Deshalb versprechen Systeme, die die Fahreraufmerksamkeit automatisch beurteilen und entsprechend warnen oder eingreifen können, eine große Verbesserung der Verkehrssicherheit. Hierbei ist aber eine genaue und echtzeitfähige Beurteilung der Fahreraufmerksamkeit bezüglich des damit verbundenen Unfallrisikos erforderlich.
Diese Dissertation führt eine neue Methode zur Beurteilung von Fahreraufmerksamkeit im situativen Kontext ein. Es wird vorgeschlagen angemessenes Blickverhalten durch Blickstrategien in einem entscheidungstheoretischen Formalismus festzulegen. In diesem Ansatz werden Modelle der Fahrsit- uation sowie der Wahrnehmung und der Fahrzeugführung des Fahrers verwendet. Bisherige Arbeiten beurteilen Fahreraufmerksamkeit zumeist alleine anhand Fahr- und Blickverhaltens. Ein deutlicher Nachteil ist dabei, dass somit das Zusammenspiel aus Fahrerverhalten, Fahrsituation und Unfallrisiko vernachlässigt wird. Das ist umso gravierender, da bekannt ist, dass erfahrene Fahrer an die Fahrsituation abgestimmte Blickstrategien zeigen, die die Beeinträchtigung ihrer Fahrleistung abmildern können. Ähnliche Blickstrategien enstehen auf natürliche Art und Weise aus dem gewählten entscheidungstheoretischen Ansatz.
In der Arbeit wird der entscheidungstheoretische Ansatz beispielhaft an der Fahraufgabe des Spurhaltens untersucht. Hier wird auf die Modellbildung, die Echtzeitberechnung, die passende Parametrisierung sowie auf die Evaluierung der Methode in der Anwendung in einem neuen Warnsystem eingegangen.
Zuerst wird die Aufgabe des Spurhaltens bei einer Nebenaufgabe, die um die visuelle Aufmerksamkeit konkurriert, modelliert. Dazu wird ein Partially Observable Markov Decision Process (POMDP) verwendet, der ein kinematisches Model der Fahraufgabe, ein Model der sensorischen Eigenschaften des Fahrers sowie ein Modell der Nebenaufgabe enthält. Danach wird die Berechnung von Strategien in dem POMDP untersucht. Diese Strategien dienen dazu das angemessene Blickverhalten festzulegen. Schließlich wird die Wirklichkeitstreue dieser Strategien überprüft und der erforderliche Rechenaufwand analysiert.
Zweitens wird die Wahl einer passenden Belohnungsfunktion betrachtet. Diese ist deswegen von Bedeutung, da sie schlussendlich das angemessene Blickverhalten festlegt. Es wird ein neues Verfahren der inversen optimalen Steuerung entwickelt, das es vermag Parameter der Belohnungsfunktion aus dem Verhalten erfahrener Fahrer zu schätzen. In einem Experiment im Realverkehr erhobenes Fahrerverhalten wird benutzt um die entwickelte Methode hinsichtlich der Genauigkeit in der Verhaltensvorhersage zu prüfen.
Die vorliegende Arbeit untersucht drittens die Schätzung von Modellen der sensorischen Eigenschaften von Fahrern. Dazu wird der erste allgemeine Ansatz für dieses Inferenzproblem in sequenziellen Entscheidungsproblemen vorgestellt. Darauffolgend wird eine Umsetzung des Ansatzes für den vorherig eingeführten POMDP entwickelt. Das resultierende Verfahren wird mittels Fahrverhaltensdaten aus einem weiteren Fahrversuch geprüft.
SchlieĂźlich wird viertens die Entwicklung eines Warnsystems und dessen Einbindung in ein Versuchsfahrzeug verfolgt. Das System zielt darauf ab den Fahrer bei der Aufrechterhaltung von genĂĽgen Aufmerksamkeit zu unterstĂĽtzen. In einem abschlieĂźenden Nutzertest wird das entwickelte System mit einem Warnsystem nach dem aktuellen Stand der Technik verglichen, wobei sowohl die Akzeptanz durch die Nutzer als auch die Auswirkungen auf die Fahrleistung untersucht werden.
Im Ganzen verdeutlicht diese Arbeit die Umsetzbarkeit und die Vorteile des verfolgten Ansatzes des angemessenen Blickverhaltens für die automatische Bewertung von Fahreraufmerksamkeit. Es wurde gezeigt, dass der benötigte Rechenaufwand eine Echtzeitanwendung zulässt und dass geeignete Modellparameter automatisch geschätzt werden können. Schließlich wurde die Verbesserung eines Ablenkungswarnsystems belegt. Folglich stellt die Methodologie, die in dieser Arbeit eingeführt wurde, einen vielversprechenden neuen Ansatz zur Bewertung von Fahreraufmerksamkeit dar, der die Probleme des aktuellen Standes der Technik vermeidet
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HOW MUCH DO IN-VEHICLE TASKS WITH SWAPPING, SWITCHING AND SPILLOVER EFFECTS INTERFERE WITH DRIVERS’ ABILITY TO DETECT AND RESPOND TO THREATS ON THE FORWARD ROADWAY?
Distractions have long been associated with crashes. A review of the literature shows drivers engaging in secondary tasks to be three times as likely to crash as compared to attentive drivers. Although several studies report that excessively long glances away from the forward roadway elevate the risk of crashes, little research has been conducted to determine how long a driver needs to glance towards the forward roadway in between glances inside the vehicle to perform a secondary task in order to detect threats present in or emerging from the forward roadway. To determine this, drivers were asked to perform simulated in-vehicle tasks requiring glances alternating inside and outside the vehicle. The glance inside was limited to 2 s. The glance outside was varied between 1 and 4 s. Eighty five participants were evaluated across two experiments involving one continuous view and three alternating view (baseline, low load and high load) conditions. Drivers in all alternating conditions were found to detect far more hazards when the forward roadway duration between two in-vehicle glances was the longest (4 s). The decrease in hazard detection at the shorter roadway durations was a combined consequence of the drivers having to devote more resources to their driving (swapping), and having to switch their attention between the primary (driving) and secondary (in-vehicle) tasks (switching). There was an additional carry over effect of load observed in the alternating high load condition when drivers were loaded even while looking at the forward roadway (spillover). There was an effect of type of processing (bottom up versus top down) and eccentricity (central versus peripheral). The asymptotic estimation of the threshold duration indicated that the drivers’ minimum glance duration on the forward roadway be at least 4 seconds when engaged with an in-vehicle task that elicits swapping effects and at least 7 seconds when engaged with an in-vehicle task eliciting switching effects
Eye-tracking assistive technologies for individuals with amyotrophic lateral sclerosis
Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research
Attention, concentration, and distraction measure using EEG and eye tracking in virtual reality
Attention is important in learning, Attention-deficit/hyperactivity disorder, Driving, and many other fields. Hence, intelligent tutoring systems, Attention-deficit/hyperactivity disorder diagnosis systems, and distraction detection of driver systems should be able to correctly monitor the attention levels of individuals in real time in order to estimate their attentional state. We study the feasibility of detecting distraction and concentration by monitoring participants' attention levels while they complete cognitive tasks using Electroencephalography and Eye Tracking in a virtual reality environment. Furthermore, we investigate the possibility of improving the concentration of participants using relaxation in virtual reality. We developed an indicator that estimates levels of attention with a real value using EEG data. The participant-independent indicator based on EEG data we used to assess the concentration levels of participants correctly predicts the concentration state with an accuracy (F1 = 73%). Furthermore, the participant-independent distraction model based on Eye Tracking data correctly predicted the distraction state of participants with an accuracy (F1 = 89%) in a participant-independent validation setting.La concentration est importante dans l’apprentissage, Le trouble du déficit de l’attention avec ou sans hyperactivité, la conduite automobile et dans de nombreux autres domaines. Par conséquent, les systèmes de tutorat intelligents, les systèmes de diagnostic
du trouble du déficit de l’attention avec ou sans hyperactivité et les systèmes de détection de la distraction au volant devraient être capables de surveiller correctement les
niveaux d’attention des individus en temps réel afin de déduire correctement leur état
attentionnel. Nous étudions la faisabilité de la détection de la distraction et de la concentration en surveillant les niveaux d’attention des participants pendant qu’ils effectuent
des tâches cognitives en utilisant l’Électroencéphalographie et l’Eye Tracking dans un
environnement de réalité virtuelle. En outre, nous étudions la possibilité d’améliorer la
concentration des participants en utilisant la relaxation en réalité virtuelle. Nous avons
mis au point un indicateur qui estime les niveaux d’attention avec une valeur réelle en
utilisant les données EEG. L’indicateur indépendant du participant basé sur les données
EEG que nous avons utilisé pour évaluer les niveaux de concentration des participants
prédit correctement l’état de concentration avec une précision (F1 = 73%). De plus, le
modèle de distraction indépendant des participants, basé sur les données d’Eye Tracking,
a correctement prédit l’état de distraction des participants avec une précision (F1 = 89%)
dans un cadre de validation indépendant des participants
Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems
World-wide injuries in vehicle accidents have been on the rise in recent
years, mainly due to driver error. The main objective of this research is to
develop a predictive system for driving maneuvers by analyzing the cognitive
behavior (cephalo-ocular) and the driving behavior of the driver (how the vehicle
is being driven). Advanced Driving Assistance Systems (ADAS) include
different driving functions, such as vehicle parking, lane departure warning,
blind spot detection, and so on. While much research has been performed on
developing automated co-driver systems, little attention has been paid to the
fact that the driver plays an important role in driving events. Therefore, it
is crucial to monitor events and factors that directly concern the driver. As
a goal, we perform a quantitative and qualitative analysis of driver behavior
to find its relationship with driver intentionality and driving-related actions.
We have designed and developed an instrumented vehicle (RoadLAB) that is
able to record several synchronized streams of data, including the surrounding
environment of the driver, vehicle functions and driver cephalo-ocular behavior,
such as gaze/head information. We subsequently analyze and study the
behavior of several drivers to find out if there is a meaningful relation between
driver behavior and the next driving maneuver
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