4,029 research outputs found
A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition
open access articleDetecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi- autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, driversâ activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi- class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driverâs distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context
License to Supervise:Influence of Driving Automation on Driver Licensing
To use highly automated vehicles while a driver remains responsible for safe driving, places new â yet demanding, requirements on the human operator. This is because the automation creates a gap between driversâ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects driversâ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation
Driving Under the (Cellular) Influence: The Link Between Cell Phone Use and Vehicle Crashes
The link between cell phone use while driving and crash risk has in recent years become an area of active research. The most notable of the over 125 studies has concluded that cell phones produce a four-fold increase in relative crash risk comparable to that produced by illicit levels of alcohol. In response, policy makers in fourteen states have either partially or fully restricted driver cell phone use. We investigate the causal link between cellular usage and crash rates by exploiting a natural experiment induced by a popular feature of cell phone plans in recent years'the discontinuity in marginal pricing at 9 pm on weekdays when plans transition from 'peak' to 'off-peak' pricing. We first document a jump in call volume of about 20-30% at 'peak' to 'off-peak' switching times for two large samples of callers from 2000-2001 and 2005. Using a double difference estimator which uses the era prior to price switching as a control (as well as weekends as a second control), we find no evidence for a rise in crashes after 9 pm on weekdays from 2002-2005. The 95% CI of the estimates rules out any increase in all crashes larger than .9% and any increase larger than 2.4% for fatal crashes. These estimates are at odds with the crash risks implied by the existing research. We confirm our results with three additional empirical approaches'we compare trends in cell phone ownership and crashes across areas of contiguous economic activity over time, investigate whether differences in urban versus rural crash rates mirror identified gaps in urban-rural cellular ownership, and finally estimate the impact of legislation banning driver cell phone use on crash rates. None of the additional analyses produces evidence for a positive link between cellular use and vehicle crashes.
Contextual queries and situated information needs for mobile users
The users of mobile devices increasingly use networked services to address their information needs. Questions asked by mobile users are strongly influenced by contextual factors such as location, conversation and activity. We report on a diary study performed to better understand mobile information needs.
Participantsâ diary entries are used as a basis for discussing the geographical and situational context in which mobile information behaviour occurs. The suitability of user queries to be answered by a portable knowledge collection and web search are also considered.
We find that the type of questions recorded by participants varies across their locations, with differences between home, shopping and in-car contexts. These variations occur both in the query terms and in the form of desired answers. Both the location of queries and the participantsâ activities affected participantsâ questions. When information needs were affected by both location and activity, they tended to be strongly affected by both factors. The overall picture that emerges is one of multiple contextual influences interacting to shape mobile information needs. Mobile devices that attempt to adapt to usersâ context will need to account for a rich variety of situational factors
Revealing driver psychophysiological response to emergency braking in distracted driving based on field experiments
Purpose â The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driverâs physiological states.
Design/methodology/approach â Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure.
Findings â This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driverâs take-over maneuver is needed.
Originality/value â The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authorsâ knowledge, this is the first study using psychophysiological measures to quantify a driverâs response to an emergency stop during distracted driving
Revealing driver psychophysiological response to emergency braking in distracted driving based on field experiments
Purpose: The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driverâs physiological states. Design/methodology/approach: Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure. Findings: This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driverâs take-over maneuver is needed. Originality/value: The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authorsâ knowledge, this is the first study using psychophysiological measures to quantify a driverâs response to an emergency stop during distracted driving
An Ontological Approach to Inform HMI Designs for Minimizing Driver Distractions with ADAS
ADAS (Advanced Driver Assistance Systems) are in-vehicle systems designed to enhance driving
safety and efficiency as well as comfort for drivers in the driving process. Recent studies have
noticed that when Human Machine Interface (HMI) is not designed properly, an ADAS can cause
distraction which would affect its usage and even lead to safety issues. Current understanding of
these issues is limited to the context-dependent nature of such systems. This paper reports the
development of a holistic conceptualisation of how drivers interact with ADAS and how such
interaction could lead to potential distraction. This is done taking an ontological approach to
contextualise the potential distraction, driving tasks and user interactions centred on the use of
ADAS. Example scenarios are also given to demonstrate how the developed ontology can be used
to deduce rules for identifying distraction from ADAS and informing future designs
Driving context influences drivers\u27 decision to engage in visual-manual phone tasks: evidence from a naturalistic driving study
Visual-manual phone tasks (i.e., texting, dialing, reading) are associated with anincreased crash risk. This study investigated how the driving context influences drivers\u27 decisions toengage in visual-manual phone tasks in naturalistic driving. Method: Video-recordings of 1432 car tripswere viewed to identify visual-manual phone tasks and passenger presence. Video, vehicle signals, andmap data were used to classify driving context (i.e., curvature, other vehicles) before and during thephone tasks (N=374). Vehicle signals (i.e., speed, yaw rate, forward radar) were available for alldriving. Results: The drivers were more likely to engage in phone tasks while standing still, and lesslikely while driving at high speeds or executing sharp turns, or when a passenger was present. Leadvehicle presence did not influence how likely drivers were to engage, but they adjusted their tasktiming to situations when the lead vehicle was increasing speed, resulting in increasing time headway.The drivers adjusted task timing until after making sharp turns and lane change maneuvers. Incontrast to previous driving simulator studies, there was no evidence of drivers reducing speed as aconsequence of phone task engagement. Conclusions: The results show that experienced drivers areskilled at using information about current and upcoming driving context to decide when to safelyengage in visual-manual phone tasks. However, drivers may fail to sufficiently increase safety marginsto allow time to respond to possible unpredictable events (e.g., lead vehicle braking). PracticalApplications: Advanced driver assistance systems should facilitate and possibly boost drivers\u27 selfregulatingbehavior. For instance, they might recognize when appropriate adaptive behavior is missingand advice or alert accordingly. The results from this study could also inspire training programs fornovice drivers, or locally classify roads in terms of the risk associated with secondary task engagementwhile driving
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