Distracted Driving
Hosted by Dr. Matthew Romoser
Dr. Romoser currently serves as a Principal Research Associate and Consultant at Dunlap & Associates Inc. His research background is in cognitive and physical human factors engineering with research interests that include transportation systems and safety, advanced training systems, and the assessment and training of at-risk drivers. With a career spanning over 25 years, he has dedicated himself to advancing human factors engineering research and providing industry-specific consultation. Dr. Romoser is known for his work as a researcher, trainer, and authority in human factors engineering, particularly in transportation, industrial, and health care domains.
Dr. Romoser has cultivated years of experience designing, executing, and analyzing human subject research studies both in the laboratory and in the field and has personally interacted with several hundred research participants. His work includes the development and testing of hazard anticipation and attention maintenance training programs such as RAPT and FOCAL for younger drivers on multiple platforms, simulator-based training programs for elderly drivers, and numerous field research studies focused on various aspects of transportation safety.
During his career he has helped secure over $2.2 million worth of grant funding for his research while in academia. As a research consultant with Dunlap & Associates Inc., Dr. Romoser is currently Principal Investigator for multiple projects on behalf of the National Highway Traffic Safety Administration and is providing expertise assistance on several other projects led by other members of the Dunlap team.
Keynote: Distraction Countermeasures: Attention, maintenance, and more
Dr. Donald Fisher
Naturalistic studies have shown that drivers are distracted some 50% of the time and that glances away from the forward roadway for more than two seconds double crash risk. NHTSA has estimated that distracted driving is a causal factor in some 29% of crashes. The problem demands a solution which is both simple and scalable. Three broad approaches will be discussed here, including those that focus on training drivers, monitoring driver state in the vehicle, and messaging children and teens in school before they take to the road. Evidence exists that all three approaches work. For example, in a recent randomized control it was found that an attention maintenance training program reduced the crashes and near crashes of teen drivers diagnosed with ADHD by 40% over the year following training. One of the groups most at risk of distraction and arguably most unlikely to benefit from training. The experimental evidence that supports the efficacy of the approaches will be discussed along with the steps that are needed next to make the interventions both simple and scalable so that all road users, not just a few, can benefit.
Real world digital data from autos and personal devices: a window on distraction, dysfunction, and safety in drivers living with medical conditions
Dr. Matthew Rizzo
This talk focuses on root causes of driver distraction and dysfunction, using comprehensive real-world data from videoelectronic (“black box”) data recording systems from a driver’s own vehicle and personal devices. Distraction is a ubiquitous in tasks and activities of modern living, at home, in the office, and on the road. The many causes differ across the lifespan, from attention deficit disorders commonly diagnosed in childhood, to range of medical conditions in adults that affect sleep, arousal, and executive functions that control our focus of attention. This includes cognitive aging, mild cognitive impairment (MCI), Alzheimer’s and Parkinson’s diseases, diabetes, obstructive sleep apnea, and common medications–– affecting physiology, cognition, and behavior. Comprehensive observations of naturalistic driver behavior and physiology in the driver’s own environments over extended time frames, provide an unprecedented window on the effects of distraction on real-world behavior and safety. Lessons learned are highly relevant to timely interventions in vehicle and in the clinic. Actionable knowledge from these observations requires consideration of pathways to impute causality and triangulate on the “truth”, verification and validation of biometric data, analytic innovations for “drinking from a firehose”, and strategic, actionable, egalitarian paths forward, to improve health and safety for all.
Rethinking driver distraction: A case-control approach in crash analysis
Dr. Jeffrey Muttart
This presentation aims to address a fundamental issue in crash investigations: the misinterpretation of driver distraction. Most crash investigators, often lacking specific training, hastily conclude investigations by attributing incidents to distraction or inattention upon discovering a driver engaged in a secondary task. This approach overlooks the necessity of establishing a baseline for typical driver responses. The few investigators who have received specialized training are taught to compare the driver's response in a crash to similar scenarios from peer-reviewed research, creating a standard for evaluating driver behavior. The identification of distraction should only occur when a driver's performance, while engaging in asecondary task, is below average, particularly in situations where time constraints or typical driver responses (such as in left-turn across path opposite direction crashes) play a crucial role.
The session emphasizes the need for a comprehensive evaluation of distractions, considering the nature of the secondary task, the driver's susceptibility (especially in cases of young or elderly drivers), and the complexity of the driving situation. This "dosage, tolerance, and task" approach recognizes the varying impact of different distractions on diverse driver capabilities.
Attendees will learn about objective, case-control methods for assessing crash sites, moving away from subjective interpretations that have traditionally skewed our understanding of driver distraction. This presentation is essential for crash investigators, traffic safety experts, and policymakers, aiming to enhance the accuracy and reliability of crash analysis by providing a deeper, more nuanced understanding of driver distractions.
The paradox of distraction: driver attention and inattention
Dr. Jing Feng
Traditional views on driver distraction see attention in driving as binary: focusing on driving is good, while distraction is bad. Efforts have been made to minimize driving distractions. However, with advances in vehicle automation and communication technologies, our understanding of distraction needs updating. Our research shows that reducing external distractions increases internal distractions (mind wandering), hazard detection can be impaired by distractors or other targets, distraction is common in automated driving and can be either harmful or harmless, and technology design can lessen distraction's negative impact. These findings reveal the complex nature of human attention and have implications for rethinking driver distraction, understanding its causes, and addressing it in automated vehicles.
Driver Visual Processing of Relevant and Irrelevant Information During Mind Wandering
Dr. Richard B. Wagner
Mind wandering is a common phenomenon in our daily lives, especially in routine tasks such as driving familiar routes. Some evidence suggests that there are detrimental effects of mind wandering on driving performance, but limited research has been conducted to examine the influence of mind wandering on a driver’s attentional processing of relevant or irrelevant information. More specifically, it is unclear as to whether the effects of mind wandering depend on the task relevancy of information presented in the visual field. The current study expands literature on mind wandering during driving using eye tracking to measure driver visual processing of relevant/irrelevant signage information in a simulated driving task while drivers reported their mental states. Preliminary results showed no significant differences in frequency and duration of glances to roadway information based on the mental state of the individual as well as the task relevancy of the information. Implications and future directions are discussed.