396 research outputs found

    Computational driver behavior models for vehicle safety applications

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    The aim of this thesis is to investigate how human driving behaviors can be formally described in mathematical models intended for online personalization of advanced driver assistance systems (ADAS) or offline virtual safety evaluations. Both longitudinal (braking) and lateral (steering) behaviors in routine driving and emergencies are addressed. Special attention is paid to driver glance behavior in critical situations and the role of peripheral vision.First, a hybrid framework based on autoregressive models with exogenous input (ARX-models) is employed to predict and classify driver control in real time. Two models are suggested, one targeting steering behavior and the other longitudinal control behavior. Although the predictive performance is unsatisfactory, both models can distinguish between different driving styles.Moreover, a basic model for drivers\u27 brake initiation and modulation in critical longitudinal situations (specifically for rear-end conflicts) is constructed. The model is based on a conceptual framework of noisy evidence accumulation and predictive processing. Several model extensions related to gaze behavior are also proposed and successfully fitted to real-world crashes and near-crashes. The influence of gaze direction is further explored in a driving simulator study, showing glance response times to be independent of the glance\u27s visual eccentricity, while brake response times increase for larger gaze angles, as does the rate of missed target detections.Finally, the potential of a set of metrics to quantify subjectively perceived risk in lane departure situations to explain drivers\u27 recovery steering maneuvers was investigated. The most influential factors were the relative yaw angle and splay angle error at steering initiation. Surprisingly, it was observed that drivers often initiated the recovery steering maneuver while looking off-road.To sum up, the proposed models in this thesis facilitate the development of personalized ADASs and contribute to trustworthy virtual evaluations of current, future, and conceptual safety systems. The insights and ideas contribute to an enhanced, human-centric system development, verification, and validation process. In the long term, this will likely lead to improved vehicle safety and a reduced number of severe injuries and fatalities in traffic

    A Context Aware Classification System for Monitoring Driver’s Distraction Levels

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    Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner

    Driver glance behaviors and scanning patterns: Applying static and dynamic glance measures to the analysis of curve driving with secondary tasks

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    Performing secondary tasks (or non‐driving‐related tasks) while driving on curved roads may be risky and unsafe. The purpose of this study was to explore whether driving safety in situations involving curved roads and secondary tasks can be evaluated using multiple measures of eye movement. We adopted Markov‐based transition algorithms (i.e., transition/stationary probabilities, entropy) to quantify drivers’ dynamic eye movement patterns, in addition to typical static visual measures, such as frequency and duration of glances. The algorithms were evaluated with data from an experiment (Jeong & Liu, 2019) involving multiple road curvatures and stimulus‐response secondary task types. Drivers were more likely to scan only a few areas of interest with a long duration in sharper curves. Total head‐down glance time was longer in less sharp curves in the experiment, but the probability of head‐down glances was higher in sharper curves over the long run. The number of reliable transitions between areas of interest varied with the secondary task type. The visual scanning patterns for visually undemanding tasks were as random as those for visually demanding tasks. Markov‐based measures of dynamic eye movements provided insights to better understand drivers’ underlying mental processes and scanning strategies, compared with typical static measures. The presented methods and results can be useful for in‐vehicle systems design and for further analysis of visual scanning patterns in the transportation domain.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151975/1/hfm20798_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151975/2/hfm20798.pd

    Driver distraction from cell phone use and potential for self-limiting behavior

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    This project consists of three parts. The first is a review of the literature on driver distraction that primarily focuses on cell phone use. The second two parts involve analysis of an existing field operational test (FOT) database to examine: 1) self-limiting behavior on the part of drivers who use cell phones, and 2) eye glance patterns for drivers involved in cell phone conversations and visual-manual tasks (e.g., texting) as compared to no-task baseline driving. The literature review discusses the apparent contradiction between results of case-crossover and simulator studies that show increases in instantaneous risk due to talking on a cell phone and results of crash-data analyses that show no substantial increase in crashes associated with increases in cell phone use in vehicles. The first data analysis shows some evidence of self-limiting behavior in cell phone conversations. Drivers initiate calls when on slower roads and at slower speeds, often when stopped. However, they call more at night, which is a higher-risk time to drive. The second analysis showed that eye glances when talking on the phone are fixated on the road for longer periods of time than in baseline driving. In contrast, on-road eye glances when engaged in a visual-manual (VM) task are short and numerous. Eye glances on and off the road are about equal in length, and the average total off-road gaze time for a five-second interval is about 2.8 secs, or 57% of the time. Average off-road gaze time out of five seconds in baseline driving is about 0.8 sec, or 16% of the time. Results show the differences in distraction mechanism between cell-phone conversations and texting. Ramifications for potential interventions are discussed.State Farm Insurancehttp://deepblue.lib.umich.edu/bitstream/2027.42/108381/1/103022.pd

    Drivers’ response to attentional demand in automated driving

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    Vehicle automation can make driving safer; it can compensate for human impairments that are recognized as the leading cause of crashes. Vehicle automation has become a central topic in transportation and human factors research. This thesis addresses some unresolved challenges on how to guide attention for safe use of automation and on how to improve the design of automation to account for humans\u27 abilities and limitations. Specifically, this thesis investigated how driver attention changed with automation and the driving situation. The objective was to inform the design of vehicle systems and develop design knowledge to support safe driving. A novelty of this thesis was in the use of real-world driving data and Bayesian methods (improved statistical modeling techniques). The analysis of driver behavior was based on data collected in naturalistic driving studies (to study the effect of assistive automation) and in a simulator experiment (to study the effect of unsupervised automation). Driver behavior was examined with measures of visual and motor response, together with contextual information, on the driving situation. The results show that assistive automation affected driver attention in real-world driving. In general, drivers devoted less attention at the forward path with automation than without. However, driver attention was sensitive to the presence of other traffic and changes in illumination---variations in the surrounding environment that increased the uncertainty of the driving situation---and it was elicited by visual, audio, and vestibular-kinesthetic-somatosensory information (perceptual cues) that alerted to an impending conflict. Driver response to a critical situation with unsupervised automation had a reflexive component (glance on-path, hands on wheel, and feet on pedals) and a planned component (decision and execution of evasive maneuver). Warnings primarily alerted attention rather than triggering an intervention. Expectation, which changed over time depending on experience, affected driver response substantially. This thesis found that the safety implications of diverting attention away from the driving situation need to be interpreted in relation to the characteristics and criticality of the driving situation (driving context) and need to consider the reduction of risk exposure due to automation (e.g., headway maintenance and collision warnings). Drivers were, for example, successful at changing their behavior in the presence of other vehicles and in different light conditions independently of automation. If drivers are not attentive at critical points, warnings are effective for triggering a quick shift of attention to the driving task in preparation to an evasive action. The results improved on those of earlier studies by providing a comprehensive assessment of driver attentional response in routine driving and critical situations. The results can support evidence-based recommendations (inattention guidelines) and be used as a reference for driver modeling and vehicle systems development

    Analysis of SHRP2 Data to Understand Normal and Abnormal Driving Behavior in Work Zones

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    This research project used the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study(NDS) to improve highway safety by using statistical descriptions of normal driving behavior to identify abnormal driving behaviors in work zones. SHRP2 data used in these analyses included 50 safety-critical events (SCEs) from work zones and 444 baseline events selected on a matched case-control design.Principal components analysis (PCA) was used to summarize kinematic data into “normal” and “abnormal”driving. Each second of driving is described by one point in three-dimensional principal component (PC) space;an ellipse containing the bulk of baseline points is considered “normal” driving. Driving segments without-of-ellipse points have a higher probability of being an SCE. Matched case-control analysis indicates that thespecific individual and traffic flow made approximately equal contributions to predicting out-of-ellipse driving.Structural Topics Modeling (STM) was used to analyze complex categorical data obtained from annotated videos.The STM method finds “words” representing categorical data variables that occur together in many events and describes these associations as “topics.” STM then associates topics with either baselines or SCEs. The STM produced 10 topics: 3 associated with SCEs, 5 associated with baselines, and 2 that were neutral. Distractionoccurs in both baselines and SCEs.Both approaches identify the role of individual drivers in producing situations where SCEs might arise. A countermeasure could use the PC calculation to indicate impending issues or specific drivers who may havehigher crash risk, but not to employ significant interventions such as automatically braking a vehicle without-of-ellipse driving patterns. STM results suggest communication to drivers or placing compliant vehicles in thetraffic stream would be effective. Finally, driver distraction in work zones should be discouraged

    License to Supervise:Influence of Driving Automation on Driver Licensing

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    To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ 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 drivers’ 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
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