Joonbum Lee

Matching Simulator Characteristics to Highway Design Problems

Driving simulators hold much promise for addressing roadway design issues. However, although simulators have demonstrated their value in experimental research addressing driver performance, their ability to support road design projects has not been as clearly established. This paper describes a design-centered framework to make simulators valuable for traffic engineers and geometric designers. This framework includes several steps: (a) identification of design issues that would benefit from driving simulators, (b) identification of simulator characteristics to match them to design issues, and (c) translation of driver performance data from the simulator to traffic behavior on the road. Several critical obstacles inhibit application of simulators to highway design. First, driving safety researchers and engineers comprise separate communities and their perspectives on how simulators can be applied to address road design issues often diverge. This paper seeks to reduce this divergence and make simulators useful to highway engineers. Interviews with engineers revealed important issues that simulators could address, such as intersection and interchange design. Second, driving simulators are often broadly defined as high fidelity, which provides little value in matching simulators to design issues. A survey of simulators and simulator characteristics clarifies the meaning of simulator fidelity and links it to road design issues. Third, simulators often produce data that do not correspond to data collected by traffic engineers. This mismatch can result from inadequate simulator fidelity, but can also arise from more fundamental sources—traffic engineers focus on traffic behavior and driving simulator researchers focus on driver behavior. Obstacles in using simulators for highway design reflect both technical and communication challenges.

What Can Be Predicted from Six Seconds of Driver Glances

We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6-second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, its valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demographic variables? We analyze 27 binary classification problems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to predict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.

Investigating the correspondence between driver head position and glance location

The relationship between a drivers glance orientation and corresponding head rotation is highly complex due to its nonlinear dependence on the individual, task, and driving context. This paper presents expanded analytic detail and findings from an effort that explored the ability of head pose to serve as an estimator for driver gaze by connecting head rotation data with manually coded gaze region data using both a statistical analysis approach and a predictive (i.e., machine learning) approach. For the latter, classification accuracy increased as visual angles between two glance locations increased. In other words, the greater the shift in gaze, the higher the accuracy of classification. This is an intuitive but important concept that we make explicit through our analysis. The highest accuracy achieved was 83% using the method of Hidden Markov Models (HMM) for the binary gaze classification problem of (a) glances to the forward roadway versus (b) glances to the center stack. Results suggest that although there are individual differences in head-glance correspondence while driving, classifier models based on head-rotation data may be robust to these differences and therefore can serve as reasonable estimators for glance location. The results suggest that driver head pose can be used as a surrogate for eye gaze in several key conditions including the identification of high-eccentricity glances. Inexpensive driver head pose tracking may be a key element in detection systems developed to mitigate driver distraction and inattention.

Sensation Seeking and Drivers’ Glance Behavior while Engaging in a Secondary Task

To investigate possible relationships between drivers’ sensation seeking and glance behavior while interacting with human-machine interfaces, a total of 70 drivers’ eye-glance data, Sensation Seeking Scale (SSS), and Driver Behavior Questionnaire (DBQ) data were collected and analyzed. Participants conducted radio tuning tasks with two standard production interfaces while driving on a highway, and their glance allocations to defined regions were recorded and manually annotated. Results showed that sensation seeking scores were related with self-reported violation scores, off-road glance patterns, and driving speed: (1) violation scores of DBQ were positively correlated with sensation seeking, (2) mean and standard deviation of off-road glance duration were positively correlated with sensation seeking for younger drivers (under 40 years), (3) total off-road glance time per minute and number of off-road glances per minute were positively correlated with sensation seeking for older drivers (over 40 years), and (4) percentage of speed change was negatively correlated with sensation seeking for both younger and older drivers. The results indicate that sensation seeking is associated with drivers’ off-road glance patterns and driving behavior. These observations further highlight the relationship between personal traits and driver behavior.

Effects of a Voice Interface on Mirror Check Decrements in Older and Younger Multitasking Drivers

Older drivers comprise an undue percentage of roadway crashes and fatalities, and existing data implicates decrements to situational awareness as one factor. Although forward attention in older drivers is well studied, rearward attention for this population is little explored. What evidence exists has suggested reduced mirror checks, especially under conditions of multitasking. Voice-enabled in-vehicle systems may represent a partial solution, requiring fewer resources and freeing drivers for behavior which maintains better rearward attention. The present study asked participants to drive on a highway in an instrumented vehicle under conditions of baseline driving, manual radio tuning, and radio tuning assisted by a voice-enabled interface. Results indicate that multitasking greatly reduced mirror checks for all groups. Older participants devoted a greater amount of time to mirror checks than younger participants when just driving, but dropped to levels similar to younger drivers while multitasking. Voice-enabled radio tuning was associated with reduced decrements in mirror checks for all age groups. Discussion centers around this new understanding of differing attentional patterns across lifespan, as well as the impact of voice-enabled interface.

A Saliency-Based Search Model Application of the Saliency Map for Driver-Vehicle Interfaces

Driver distraction is a leading cause of motor vehicle crashes. As more in-vehicle systems are developed, they represent increasing potential for distraction. Designers of these systems require a quantitative way to assess their distraction potential that does not involve time-consuming test track or simulator testing. A critical contribution to driver distraction concerns the search time for items in an in-vehicle system display. This study tests the saliency map’s ability to predict search time, and proposes a potential application of the saliency map in assessing driver distraction. Empirical data for search tasks were collected and used to test a modified driver model based on the saliency map. The results show that the modified saliency map can predict search time, and suggest that the driver model could be used to understand how design features influence the bottom-up visual search process. More broadly, such a model can complement guidelines and user testing to help designers to incorporate human factors considerations earlier in the design process.

How safe is tuning a radio?: using the radio tuning task as a benchmark for distracted driving

Drivers engage in non-driving tasks while driving, such as interactions entertainment systems. Studies have identified glance patterns related to such interactions, and manual radio tuning has been used as a reference task to set an upper bound on the acceptable demand of interactions. Consequently, some view the risk associated with radio tuning as defining the upper limit of glance measures associated with visual-manual in-vehicle activities. However, we have little knowledge about the actual degree of crash risk that radio tuning poses and, by extension, the risk of tasks that have similar glance patterns as the radio tuning task. In the current study, we use counterfactual simulation to take the glance patterns for manual radio tuning tasks from an on-road experiment and apply these patterns to lead-vehicle events observed in naturalistic driving studies. We then quantify how often the glance patterns from radio tuning are associated with rear-end crashes, compared to driving only situations. We used the pre-crash kinematics from 34 crash events from the SHRP2 naturalistic driving study to investigate the effect of radio tuning in crash-imminent situations, and we also investigated the effect of radio tuning on 2,475 routine braking events from the Safety Pilot project. The counterfactual simulation showed that off-road glances transform some near-crashes that could have been avoided into crashes, and glance patterns observed in on-road radio tuning experiment produced 2.85-5.00 times more crashes than baseline driving.

Linking the Detection Response Task and the AttenD Algorithm Through Assessment of Human–Machine Interface Workload

Multitasking related demands can adversely affect drivers’ allocation of attention to the roadway, resulting in delays or missed responses to roadway threats and to decrements in driving performance. Robust methods for obtaining evidence and data about demands on and decrements in the allocation of driver attention are needed as input for design, training, and policy. The detection response task (DRT) is a commonly used method (ISO 17488) for measuring the attentional effects of cognitive load. The AttenD algorithm is a method intended to measure driver distraction through real-time glance analysis, in which individual glances are converted into a scalar value using simple rules considering glance duration, frequency, and location. A relationship between the two tools is explored. A previous multitasking driving simulation study, which used the remote form of the DRT to differentiate the demands of a primary visual–manual human–machine interface from alternative primary auditory–vocal multimodal human–machine interfaces, was reanalyzed using AttenD, and the two analyses compared. Results support an association between DRT performance and AttenD algorithm output. Summary statistics produced from AttenD profiles differentiate between the demands of the human–machine interfaces considered with more power than analyses of DRT response time and miss rate. Among discussed implications is the possibility that AttenD taps some of the same attentional effects as the DRT. Future research paths, strategies for analyses of past and future data sets, and possible application for driver state detection are also discussed.

Revisiting Radio Tuning: A Secondary Analysis Comparing Glance Behavior Across Five Vehicles

Visual-manual demand placed on drivers through interactions with operational functions, embedded telematics, infotainment systems, and nomadic technologies has raised concerns associated with diverting attention from the roadway. This analysis draws on data from field studies of five different infotainment systems representing a range of screen placements and control characteristics that diverge in significant ways from the relatively standard car radio layout of previous generations. Participants performed a set of classic visual-manual radio tuning tasks under highway driving conditions. There were significant differences in task completion time, number of off-road glances, mean single off-road glance duration, and total off-road glance time across vehicles. These results highlight that the range of configurations appearing in modern infotainment systems have changed the extent to which they can be used in the classic radio tuning task to provide a standard demand benchmark.

Does Order Matter? Investigating the Effect of Sequence on Glance Duration During On-Road Driving

Previous literature has shown that vehicle crash risks increases as drivers’ off-road glance duration increases. Many factors influence drivers’ glance duration such as individual differences, driving environment, or task characteristics. Theories and past studies suggest that glance duration increases as the task progresses, but the exact relationship between glance sequence and glance durations is not fully understood. The purpose of this study was to examine the effect of glance sequence on glance duration among drivers completing a visual-manual radio tuning task and an auditory-vocal based multi-modal navigation entry task. Eighty participants drove a vehicle on urban highways while completing radio tuning and navigation entry tasks. Forty participants drove under an experimental protocol that required three button presses followed by rotation of a tuning knob to complete the radio tuning task while the other forty participants completed the task with one less button press. Multiple statistical analyses were conducted to measure the effect of glance sequence on glance duration. Results showed that across both tasks and a variety of statistical tests, glance sequence had inconsistent effects on glance duration—the effects varied according to the number of glances, task type, and data set that was being evaluated. Results suggest that other aspects of the task as well as interface design effect glance duration and should be considered in the context of examining driver attention or lack thereof. All in all, interface design and task characteristics have a more influential impact on glance duration than glance sequence, suggesting that classical design considerations impacting driver attention, such as the size and location of buttons, remain fundamental in designing in-vehicle interfaces.