Since the original development of driver distraction guidelines, scientific understanding of driver workload, attention threading, situation awareness, and the influence of driving context has significantly evolved-driven largely by insights from naturalistic driving studies and other research. Concurrently, vehicle technologies have advanced, integrating new forms of internal and external sensing, increased computational power, larger and often multiple screens, multimodal interfaces, and feedback systems. This paper reviews and builds upon prior research and guidelines, particularly those coming out of the U.S., as that is the background of the majority of the authors, to propose a new conceptual framework for attention support. The framework, developed through a pre-competitive collaboration between academic and industry partners promotes an updated, attention-centric approach to the design, validation, and evaluation of driver-vehicle interfaces aimed at enhancing safety and the driving experience. Central to this approach is the real-time assessment of whether the driver's attention is appropriately aligned with situational demands and, if necessary, providing support. By leveraging emerging technologies, the framework not only fosters the rebuilding of situationally relevant knowledge and readiness to respond but also provides timely support when a driver's attention is insufficient for the current driving demands. This work argues for the value of a necessary shift in emphasis from distraction prevention alone to broader consideration of driver attention support. It addresses attention deficits arising from multiple sources-including but not limited to, vehicle interfaces-and moves beyond earlier guidelines. The result is a more proactive, dynamic system, aligned with a Safe Systems Approach (adopted by the U.S. Department of Transportation and others) that shifts responsibility from solely changing behavior to supportive systems that recognize human fallibility and vulnerability.

As the characteristics of in-vehicle human-machine interfaces (HMIs), the driving task, and the expectations and behavior of drivers have evolved, so too should our thinking and approach to HMI design and evaluation. This panel will present background and perspectives on the current status, emerging needs, challenges, and opportunities in this area. A key focus for the panel is an emphasis on attention support. Detailed contextual description is provided below for reference to allow panelists to keep their opening remarks relatively brief to allow for substantive question and discussion time with the audience.

The completion of the driving task by Level 3 (L3) Automated Driving Systems (ADS) allows drivers to turn their attention away from the road and direct it towards other activities; however, the driver must remain receptive to requests to intervene (RTI) and be prepared to complete the driving task upon deactivation of the driving automation. This transfer of control is called a takeover. The objectives of this research activity were to understand the human factors underlying driver disengagement while using L3 ADS and to identify factors influencing takeover quality and performance when the ADS-equipped vehicle has exited its operational design domain. A structured, comprehensive literature review approach was used to synthesize findings in the following areas: common measures used to assess takeover performance, factors that affect takeovers, and strategies that improve driver takeover performance.

Partial driving automation systems are designed to assist drivers in some vehicle operation demands. However, modifications to the driving task that change the driver's role from that of an active participant to a passive supervisor could result in insufficient monitoring of the driving automation system and the surrounding environment. A reduced subset of driving data for 19 drivers from the Virginia Connected Corridors 50 Elite Naturalistic Driving Study was used to assess whether driver eye glance behavior and secondary task engagement were different when driver assistance systems were active compared to when they were available but inactive (n = 148). The results of this study demonstrate that drivers spent more time looking away from the road while driving automation systems were active and that drivers were more likely to be observed browsing on their cell phones while using driving automation systems. Current driving automation features require human monitoring of automation, yet the drivers of these automation-equipped vehicles are inclined to engage in secondary tasks and take longer and more frequent glances away from the roadway. It is possible that performance effects, such as omission errors or delayed reactions, may occur as a result of drivers' substandard monitoring of the driving scene.

The mere idea of automated, connected, and intelligent vehicles (ACIVs) conjures up visions in which our vehicles cater to our every transportation need. The development and deployment of ACIV systems, such as advanced driver assistance systems (ADAS) that help control vehicle acceleration, vehicle deceleration, and lane position, has the potential to improve safety by relieving drivers of tasks that they are prone to performing with errors. However, providing this additional support to drivers also fundamentally changes the human driving task from one of manual control to one of supervisory control. Additionally, as higher levels of ACIV systems become commercially available, the driving task changes further from requiring near constant human supervision to infrequent vehicle system assistance. Despite this dramatic change to the driving task, there has been relatively little work examining the efficacy of training practices and learner-centered human–machine interface (HMI) design to positively impact the safety of ACIV systems, which is likely a critical factor in promoting a safe human/vehicle partnership. This chapter summarizes the general concept of HMI design for ACIVs and provides information on specific training-related factors. The first section, Training Overview, will briefly discuss the importance of training, including ways in which a human–vehicle partnership can be enhanced through training, while the second section addresses the critical question of what content should be included in the training protocols for ACIV systems. The third section, titled Andragogical Considerations for ACIV Systems Training, will identify both driver and non-driver related key factors that should be considered in the development of training protocols. The fourth section provides a review of both current and future protocols that could be employed to train people on the use of ACIV systems. The chapter concludes with a series of recommendations for ACIV systems training practices. This chapter will serve as a foundation for driver training stakeholders, technology developers, consumers, and legislatures to address the growing need to include relevant and effective training for ACIV systems as these technologies are developed and deployed.

Advanced driver-assistance systems and partial driving automation are becoming increasingly common, yet despite their growing prevalence, drivers seem to know very little about them. Previous studies have found that owners of ADAS equipped vehicles have demonstrated misperceptions or lack of awareness about system limitations, which may impact driver comfort with and reliance on these systems. The purpose of this study was to determine the effectiveness of two training strategies on drivers’ knowledge and perceived familiarity of vehicle automation as well as their environment monitoring behaviors during system use. Forty volunteers participated in a multi-stage research study in which they were exposed to either a conventional training protocol, self-learning through the owner’s manual, or an experimental (multimedia) training protocol, using the in-vehicle display technologies as training tools. Results indicate training strategy elicits limited differences in knowledge and no difference in driver behaviors or attitudes. Behaviors and attitudes were heavily influenced by time and experience with the driving automation system while knowledge of the vehicle systems remained unchanged.

The objective of this paper is to model driver intersection approach and traversal trajectories in response to traffic signals and driver behavior based on stopping behavior. This study analyzed 12,688 observations from signalized intersections from the CICAS-V project (1) database. The selected data were subjected to Multivariate Adaptive Regression Splining to develop a model of a typical driver’s velocity on their approach to the intersection based on the vehicle’s proximity to the stop bar (range [m]) and other categorical factors such as vehicle type, time of day, road surface condition, and weather. The resulting models highlight how driver approach speeds vary as a function of range and other factors depending on the signal phase and intended course of action. The models predict the vehicle speed as a function of distance to the stop bar of the intersection (range). The results suggest that the behaviors of red and yellow light runners are difficult to distinguish from each other, but it is possible. These models will be used to calculate vehicle approach speeds in real-world intersection crashes.

In 2012, 683 000 crashes occurred at stop-sign-controlled intersections, with 2434 of those crashes being fatal and composing 5.3% of all fatal traffic incidents in the United States. Roughly 50% of all fatal crashes at stop-sign-controlled intersections involve crossing over (i.e., running) the traffic control device. With the advent of connected-vehicle technology, it is possible to provide a salient in-vehicle adaptive stop display to a driver. This display could alert a driver when he or she will have to stop at an intersection due to oncoming traffic. The same display could also permit drivers to pass through an intersection without stopping when a conflicting vehicle is not present. The purpose of this paper was to evaluate these potential improvements in safety and mobility through an empirical research study. An in-vehicle adaptive stop display was developed and tested on the Virginia Smart Road. Forty-nine drivers were exposed to multiple intersection scenarios they would experience in the real world while using connected-vehicle technology. The scenarios included variations in adjacent traffic, equipment malfunctions, and total equipment failures. There were no indications of a safety detriment to using the adaptive stop display in terms of compliance (likelihood of driver adhering to the information presented), driver complacency, or driver risk taking. Furthermore, the study indicates that, with a higher measured rate of compliance, an in-vehicle adaptive stop display would have a positive impact on safety.

This project characterized the performance of Connected Vehicle Systems (CVS) on motorcycles based on two key components: global positioning and wireless communication systems. Considering that Global Positioning System (GPS) and 5.9 GHz Dedicated Short-Range Communications (DSRC) may be affected by motorcycle rider occlusion, antenna mounting configurations were investigated. In order to assess the performance of these systems, the Virginia Tech Transportation Institute’s (VTTI) Data Acquisition System (DAS) was utilized to record key GPS and DSRC variables from the vehicle’s CVS Vehicle Awareness Device (VAD). In this project, a total of four vehicles were used where one motorcycle had a forward mounted antenna, another motorcycle had a rear mounted antenna, and two automobiles had center-mounted antennas. These instrumented vehicles were then subject to several static and dynamic test scenarios on closed test track and public roadways to characterize performance against each other. Further, these test scenarios took into account motorcycle rider occlusion, relative ranges, and diverse topographical roadway environments. From the results, both rider occlusion and approach ranges were shown to have an impact on communications performance. In situations where the antenna on the motorcycle had direct line of sight with another vehicle’s antenna, a noticeable increase in performance can be seen in comparison to situations where the line of sight is occluded. Further, the forward-mounted antenna configuration provided a wider span of communication ranges in open-sky. In comparison, the rear-mounted antenna configuration experienced a narrower communication range. In terms of position performance, environments where objects occluded the sky, such as deep urban and mountain regions, relatively degraded performance when compared to open sky environments were observed.

The purpose of this research and development activity was to build a mobile application with a low-distraction user interface appropriate for use in a connected vehicle (CV) environment. To realize their full potential, future CV applications will involve communicating information to and from drivers during vehicle operation. Mobile devices such as smart phones and tablets may be a reasonable hardware platform to provide this communication. However, there are concerns that a potential increase in driver interaction with CV applications may lead to driver distraction and possible negative impacts on driving safety. The prototype mobile device user interface that was designed and created during this project can be used to test new CV applications, validate their impact on driver safety, and inform future mobile device user interface standards for driving applications.

Virginia Tech Transportation Institute (VTTI), University of Virginia (UVA) Center for Transportation Studies, Morgan State University (MSU)

Motor vehicle collisions are the leading cause of death for individuals between the ages of 15-20 years old in the United States. Top safety concerns involving teen drivers include; safety belt use, impaired driving, and distracted driving. Rules that address these safety concerns have been implemented into multifaceted graduated driver licensing (GDL) programs in the United States as well as in state legislation. There are a limited number of studies focusing on the perspective, knowledge and opinion of GDL policy. The effectiveness of the GDL program in West Virginia is being measured through the administration of surveys. The surveys have been designed to assess awareness among high school students, parents of high school students, and police officers. GDL limits teenage driver exposure to high risk situations but its potential to reduce fatalities is limited by people's willingness to comply with the laws and the enforcement of the program restrictions by parents and law enforcement officers. Using the insights provided by these surveys, ways to improve GDL policy and awareness to increase program effectiveness will be identified.