Research Overview
My research interests involve a range of multi-disciplinary efforts that bring together the skills of psychologists, engineers, and computer scientists to solve complex problems involving human behavior, particularly in regards to how humans interact with advanced technologies. In collaboration with several colleagues, I have developed a broad research agenda that aims to bring new innovations to vehicle safety through new observational and experimental data collection, creation of analytic methods and tools for large scale data processing, and innovative algorithm development. The aim is to transfer knowledge we gain through traditional academic channels, collaboratively with companies that support the team’s efforts, and through interaction with global regulatory and policy organizations. A sample of select work areas include:
- Multi-modal vehicle interfaces – laboratory, simulation, field experimental and naturalistic assessment of system demands; creation of new safety efficacious demand assessment approaches
- Driver state – algorithm development for the detection of gaze, drowsiness, emotion, activity, and cognitive load
- Computer vision – implementation of traditional and new cutting edge techniques (e.g., deep neural networks) in the development and application for internal and external vehicle scene perception and interpretation
- Older drivers – consideration of the cognitive, behavioral and medical impairments that alter the strategic and operational control of vehicles; consideration of the roll of automation and advanced driver assistance systems in enhancing safety and mobility
- Decision fusion – creation of algorithms for real-time detection of driver state from multiple data streams and the integration of feedback into advanced driver assistance systems
- Micro-traffic simulation and real-world data collection – study of heterogeneous shared road-user interactions that include manually-controlled, semi-autonomous, and fully autonomous vehicles; interactions with vulnerable road users such as pedestrians, bicyclists
- Naturalistic driving data – analytical efforts that consider the complexity of real-world driving aimed at uncovering the comorbidity of factors that increase crash risk that are often overlooked in classical controlled experimental studies
- Physiology – utilization of physiological sensors to obtain objective data on arousal, stress, workload, and health related variables; exploration and validation of recording technologies for transfer from controlled laboratory settings to the challenges of real-world monitoring conditions such as driving
- Validation of methods – Consideration of the absolute and relative validity of laboratory assessment, driving simulation and field experimental studies
- Clear information presentation – a range of experimental efforts aimed to develop deeper insight into glance based legibility in the context of automotive, portable and wearable technologies
- Safety benefits of advanced driver assistance systems (ADAS) – development of objective data to support understanding of when, how, and to what extent drivers do or do not utilize various production ADAS technologies under both field operational test and naturalistic studies
- Medical impairments – research on the impact of disease and medication on driver behavior (e.g. ADHD, ASD, cognitive impairment, diabetes, cardiovascular conditions)
- The future of mobility – development of scenarios on how automated vehicles may impact global mobility to support planning for the future now
- Human centered vehicle automation – consideration of the effects of human performance capabilities (attention, vigilance, mental models) on the control and oversight of highly automated driving systems
