The Backing crash Countermeasures project, part of the U.S. Department of Transportation’s Advanced Crash Avoidance Technologies (ACAT) program, developed a basic methodological framework and computer‐based simulation model to estimate the effectiveness and potential safety benefits of various backing crash countermeasure systems. The project was led by the General Motors Corporation (GM) with support from Virginia Tech Transportation Institute (VTTI), and involved a series of tasks which included: characterizing backing crashes, assembling a research test bed for use within the project, developing a series of objective testing procedures to characterize backing crash countermeasure system performance, and developing and exercising a computer‐based Safety Impacting Methodology (SIM) model used to estimate the effectiveness and potential safety benefits of the prototype backing crash countermeasure system evaluated. The SIM was designed with an emphasis on three key characteristics: accuracy and precision of estimates, modularity, and flexibility. Despite the availability of prior work and extensive data collection within the project, many limitations remain. While the SIM serves as a useful tool to bring together data from a wide array of research into a unified simulation, the limitations identified constrain its usefulness in predicting potential real world safety benefits of emerging backing crash avoidance systems. Benefit estimates from the SIM should be considered preliminary indications of backing crash countermeasure performance useful in studying the interaction of technology with driver behavior at various stages along the crash timeline.
Abstract : Data are presented to demonstrate that resource relocation during reading can occur in response to task demands. These relocations can compensate for experimentally induced lower level deficits. Further, these reallocations appear to operate both top-down and bottom-up. The data are clearly inconsistent with serial and parallel noninteractive models of reading. The necessity of including resource allocations and compensatory resource relocation provisions in interactive models of reading is emphasized.
This paper describes a set of data made available that contains detailed subtask coding of interactions with several production vehicle human machine interfaces (HMIs) on open roadways, along with accompanying eyeglance data.
Objective: The objective was to examine naturalistic usage of infotainment systems to assess use characteristics and patterns. Background: Infotainment systems continue to evolve in terms of their capabilities and information availability, raising concerns about their distraction potential. Assessing potential distraction requires understanding how challenging different tasks are and how frequently they occur during driving. Method: High-end infotainment system use was observed across 17 participants over a period of approximately 4 weeks each. One of two different infotainment systems was provided to participants. Audio, video, and driving performance data were collected and observed by trained reductionists. The two infotainment systems integrated iPod™, satellite radio, CD/DVD/MP3 playback, AM/FM, and, in one case, navigation functionalities. Systems differed in their vehicle integration and advanced infotainment features offered. Results: The median participant interacted with the infotainment systems once every 4 hr (90th percentile: 6.1 interactions/hr). More than 50% of these interactions involved adjusting the volume. Although there were a few lengthy interactions, the median duration was 2.2 s (90th percentile: 24.6 s), which required measurable visual involvement when compared to a matched baseline. The median total eyes-off-road time across interactions was 1 s (90th percentile: 11.4 s) and differed significantly across type of system interaction. Longer interactions tended to occur when the vehicle was stationary. Conclusion: Drivers habitually interact with infotainment systems while driving; this includes advanced functions. Some self-regulation was observed. Application: These data provide a comparison basis for use in examining driver interactions with future infotainment systems.
Much of the driver distraction and inattention work to date has focused on concerns over drivers removing their eyes from the forward roadway to perform non-driving-related tasks, and its demonstrable link to safety consequences when these glances are timed at inopportune moments. This extensive literature has established, through the analyses of glance from naturalistic datasets, a clear relationship between eyes-off-road, lead vehicle closing kinematics, and near-crash/crash involvement.This paper looks at the role of driver expectation in influencing drivers' decisions about when and for how long to remove their eyes from the forward roadway in an analysis that consider the combined role of on- and off-road glances.Using glance data collected in the 100-Car Naturalistic Driving Study (NDS), near-crashes were examined separately from crashes to examine how momentary differences in glance allocation over the 25-s prior to a precipitating event can differentiate between these two distinct outcomes. Individual glance metrics of mean single glance duration (MSGD), total glance time (TGT), and glance count for off-road and on-road glance locations were analyzed. Output from the AttenD algorithm (Kircher and Ahlström, 2009) was also analyzed as a hybrid measure; in threading together on- and off-road glances over time, its output produces a pattern of glance behavior meaningful for examining attentional effects.Individual glance metrics calculated at the epoch-level and binned by 10-s units of time across the available epoch lengths revealed that drivers in near-crashes have significantly longer on-road glances, and look less frequently between on- and off- road locations in the moments preceding a precipitating event as compared to crashes. During on-road glances, drivers in near-crashes were found to more frequently sample peripheral regions of the roadway than drivers in crashes. Output from the AttenD algorithm affirmed the cumulative net benefit of longer on-road glances and of improved attention management between on- and off-road locations.The finding of longer on-road glances differentiating between safety-critical outcomes in the 100-Car NDS data underscores the importance of attention management in how drivers look both on and off the road. It is in the pattern of glances to and from the forward roadway that drivers obtained critical information necessary to inform their expectation of hazard potential to avoid a crash.This work may have important implications for attention management in the context of the increasing prevalence of in-vehicle demands as well as of vehicle automation.
Non-driving-related tasks (NDRTs) have the potential to affect safety in a number of ways, but the conditions under which drivers choose to engage in NDRTs has not been extensively studied. This analysis considers naturalistic driving data in which drivers were recorded driving and engaging in NDRTs at will for several weeks. Using human-annotated video captured from vehicle cabins, we examined the probabilities with which drivers engaged in NDRTs, and we examined the relationship between vehicle speed and NDRT probability, with the goal of modeling NDRT probability as a function of speed and type of NDRT observed. We found that tasks that contain significant visual and manual components, such as phone manipulation, show strong sensitivity to vehicle speed, while other tasks, such as phone conversation, show no effects of vehicle speed. These results suggest that there are systematic relationships between NDRT patterns and vehicle speed, and that the nature of these relationships is sensitive to the demands of the NDRT. The relationship between speed and NDRT probability has implications for understanding the effects of NDRTs on safety, but also for understanding how drivers may differ in terms of the strategies they employ to modulate their NDRT behaviors based upon driving demands.
The occlusion method is one of the verification test alternatives allowed under the Alliance of Automobile Manufacturers guidelines to manage visual demand of certain in-vehicle devices. Since the time those guidelines were published, additional research has become available on the advantages, disadvantages, and considerations of using the occlusion method in this context. The current investigation reviewed this additional research and then asked several occlusion experts about their opinions regarding topics such as method parameter values, test settings, validity, and usefulness of results. Experts’ answers were summarized and suggested that there is consensus about some aspects of the occlusion method and disagreement over other aspects. These findings are discussed in the context of including the occlusion method as a test alternative in future versions of visual-manual device design guidelines.
