Joshua Domeyer

• Doctor of Philosophy
• Principal Researcher at Toyota Motor North America

Objective This paper characterizes the actions of pedestrian-driver dyads by examining their interdependence across intersection types (e.g., zebra crossings, stop signs). Additionally, the analysis of interdependence captures other external factors, such as other vehicles or pedestrians, that may influence the interaction. Methods A 228 epoch veh...

Developing vehicle automation that accommodates other road users and exhibits familiar behaviors may enhance traffic safety, efficiency, and fairness, leading to tolerance of the technology. However, the interdependence between vehicle automation and other road users makes them more challenging than typical control and path planning tasks. Through...

Communication among road users smooths interactions, improves efficiency, and mitigates risk. Eye contact and waving may be the most salient of this communication, but more often road users use their movement or position as implicit signals. Vehicle automation may disrupt these signals by introducing unfamiliar or unclear interactions that may not...

When automobiles were first introduced in the early 1900s, poor communication and unsafe interactions between drivers and other road users generated resistance. This created a need for new infrastructure, vehicle design, and social norms to mitigate their negative effects on society. Vehicle automation may lead to similar challenges as drivers are...

Since the introduction of automobiles in the early 1900s, communication among elements of the transportation system has been critical for efficiency, safety, and fairness. Communication mechanisms such as signs, lights, and roadway markings were developed to send signals about affordances (i.e., where and when can I go?) and constraints (i.e., wher...

div class="section abstract"> Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral...

Phone use while driving, particularly visual-manual phone engagement (VMPE) like texting, dialing, and browsing, can significantly increase crash likelihood due to prolonged diversion of attention from the driving task. However, significant knowledge gaps remain regarding the circumstances surrounding VMPE, whether different types of VMPE are initi...

This study delves into the complexity of tailgating behavior using advanced analytical techniques, including predictive modeling and cluster analysis. Using a naturalistic driving study dataset containing 3,798 full trips from 44 drivers over a three-week period with data that includes driving kinematics, context, driver demographics and driver psy...

This study investigates the relationship between drivers' hand positions and visual attention while using partial driving automation to understand individual differences in hands-off behavior. We analyzed 298 instances of Tesla Autopilot disengagements. Results showed that longer hands-off durations were associated with reduced on-road glance times...

This study quantifies bidirectional gazing—when drivers and pedestrians look at one another—in a naturalistic setting. Understanding bidirectional gazing provides insights into the communication dynamics between pedestrians and drivers, and their relation to infrastructural support (e.g., a stop sign). Findings demonstrate that 36% of observed road...

Automation’s imperfection requires driver engagement to handle challenging tasks, creating interdependent relationships where both parties influence each other. Therefore, it is vital to support driver-automation interdependence to align their behaviors with team goals. We expand the Interdependence Analysis (IA) method to evaluate interdependent r...

We propose the concept of directive driving automation that positively influences drivers’ intentions to achieve shared goals. As a step toward directive driving automation, this survey study explored how social norms can persuade drivers to continue using driving automation. We tested social norm messages using a 2x2x2x2 factorial within-subject d...

Advanced driver assistance systems are changing the way that drivers interact with vehicles and may benefit from changes to consumer education that aim to improve the driver’s understanding of changes to their role. System updates will increasingly be applied over the air, which may lead to confusion about system capabilities if there is a substant...

Extended exposure to reliable automation may lead to overreliance as evidenced by poor responses to auto-mation errors. Individual differences in trust may also influence responses. We investigated how these factors affect response to automation errors in a driving simulator study comprised of stop-controlled and uncon-trolled intersections. Driver...

Over-the-air (OTA) advanced driver assistance systems (ADAS) updates may require drivers to adjust their mental models to maintain appropriate system use. Based on the magnitude of change to the ADAS, hands-on experience with the updated system may not require consumer education or training. In this study, 96 drivers experienced adaptive cruise con...

Objective: Right-of-way negotiation between drivers and pedestrians often relies on explicit (e.g., waving) and implicit (e.g., kinematic) cues that signal intent. Since effective driver-pedestrian communication is important for reducing safety-relevant conflicts, this study uses information theory to identify vehicle kinematic behaviors that prov...

During the use of partial driving automation, transfers of control (TOC) between the driver and automation happen routinely while the driver supervises and manages the driving environment. Although driver-initiated TOCs are more prevalent, they have not been explored to the same extent as system-initiated TOCs due to their low criticality. Drivers...

Objective: This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation. Background: Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent's act...

Autonomous driving has attracted interest for interpretable action decision models that mimic human cognition. Existing interpretable autonomous driving models explore static human explanations, which ignore the implicit visual semantics that are not explicitly annotated or even consistent across annotators. In this paper, we propose a novel Interp...

This study expands existing taxonomies for transfer of control (TOC) with driving automation. A TOC taxonomy is necessary to categorize types of TOC, interpret drivers' behavior during a transition, and assess the safety implications of each TOC type. However, existing taxonomies do not capture important aspects of the driver's reaction to the tran...

Automated driving desires better performance on tasks like motion planning and interacting with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high performance in these tasks with remarkable visual scene understanding and generalization abilities. However, when common scene-parsing methods are used to train end-to-e...

Prediction of pedestrian behavior is critical for fully autonomous vehicles to drive in busy city streets safely and efficiently. The future autonomous cars need to fit into mixed conditions with not only technical but also social capabilities. As more algorithms and datasets have been developed to predict pedestrian behaviors, these efforts lack t...

One way to compensate for the limitations of automated vehicles is to use a remote operator as a fallback controller. Indeed, this has been proposed for fleet management and intermittent vehicle control. However, existing remote operation applications have demonstrated control challenges, such as latency and bandwidth, that inhibit the effectiveness...

Objective Understanding the factors that affect drivers’ response time in takeover from automation can help guide the design of vehicle systems to aid drivers. Higher quantiles of the response time distribution might indicate a higher risk of an unsuccessful takeover. Therefore, assessments of these systems should consider upper quantiles rather th...

Most automated vehicle studies have focused on limited automation where the role of the user is that of a driver, supervisor or fallback, but comparatively fewer have considered riders. If riders’ experiences are ignored, it could undermine the adoption of the technologies and, consequently, the realization of their anticipated benefits. A driving...

Systems and methods for providing a notification of an upcoming acceleration to an occupant of a vehicle are disclosed herein. The vehicle includes a seat that is movable in one or more directions. The vehicle can identify a direction and magnitude of acceleration corresponding to an upcoming maneuver. The vehicle can also track one or more states...

Objective This study examines how driving styles of fully automated vehicles affect drivers’ trust using a statistical technique—the two-part mixed model—that considers the frequency and magnitude of drivers’ interventions. Background Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle’s driving style...

When asked, a majority of people believe that, as pedestrians, they make eye contact with the driver of an approaching vehicle when making their crossing decisions. This work presents evidence that this widely held belief is false. We do so by showing that, in majority of cases where conflict is possible, pedestrians begin crossing long before they...

Humans, as both pedestrians and drivers, generally skillfully navigate traffic intersections. Despite the uncertainty, danger, and the non-verbal nature of communication commonly found in these interactions, there are surprisingly few collisions considering the total number of interactions. As the role of automation technology in vehicles grows, it...

Increasingly vehicle automation may convey greater capability than it actually possesses. The emergence of highly capable vehicle automation (e.g., SAE Level 4) and the promise of driverless vehicles in the near future can lead drivers to inappropriately cede responsibility for driving to the vehicle with less capable automation (e.g., SAE Level 2)...

Human factors research in vehicle automation has focused on user interfaces such as performance feedback through visual and auditory displays (Blanco et al., 2015). Another approach is to use vehicle dynamics and vibrations as communicative tools for guiding attention (e.g., Morando, Victor, & Dozza, 2016; Walker, Stanton, & Young, 2006; Wiese & Le...

Increasingly vehicle automation may convey greater capability than it actually possesses. The emergence of highly capable vehicle automation (e.g., SAE Level 3) and the promise of driverless vehicles in the near future can lead drivers to inappropriately cede responsibility for driving to the vehicle with less capable automation (e.g., SAE Level 2)...

The Strategic Highway Research Program 2 (SHRP2) can provide unique information on how driver behavior leads to crashes or near-crashes. A subset of this dataset was created to examine naturalistic engagement in secondary tasks (NEST; Owens, Angell, Hankey, Foley, & Ebe, 2015). The NEST dataset is composed of crash and near-crash epochs which have...

Rear end crashes are one of the most frequent types for US drivers. One of the factors for this crash type is following the lead vehicle too closely. Since achieving and maintaining an appropriate following distance is a function of vehicle speed and distance, a mental model is difficult to establish. The Following Distance Training system (FDT) wa...

The rapid increase in the sophistication of vehicle automation demands development of evaluation protocols tuned to understanding driver-automation interaction. Driving simulators provide a safe and cost-efficient tool for studying driver-automation interaction, and this paper outlines general considerations for simulator-based evaluation protocols...

Rear-end crashes are the most common crash in US. One factor influencing this type of crash is following distance. Since achieving and maintaining an appropriate following distance is a function of vehicle speed and distance, a mental model is difficult to establish. In general, it is thought that visual feedback is effective to support driver awar...

The goal of the present study was to understand how buttons and images affect glance times for in-vehicle tasks. Search tasks that simulated in-vehicle tasks (e.g., radio tuning, navigation) were used to measure these effects. Participants across various age groups were required to select an item (e.g., “Mango”) on each screen that matched a catego...

Recently the National Highway Traffic Safety Administration issued the final version of its voluntary Visual-Manual Distraction Guidelines and provided specific guidance on how to conduct driver distraction testing (NHTSA, 2013). The guidelines provide evaluation criteria for determining whether a task should be available while driving, including s...

Gives a consistent definition of driver distraction for scientific advancement in understanding driver distraction and driving assessment in general.

The present paper examines simulator adaptation syndrome (SAS) as a barrier to simulator use for older adults. A brief description of the phenomenon is provided and its history discussed. There are generally three domains in which to make changes to alleviate the problem. Changes to the simulator, the scenarios, and the participants are viable aven...