Browse Publications Technical Papers 2019-01-0872
2019-04-02

Driver Workload in an Autonomous Vehicle 2019-01-0872

As intelligent automated vehicle technologies evolve, there is a greater need to understand and define the role of the human user, whether completely hands-off (L5) or partly hands-on. At all levels of automation, the human occupant may feel anxious or ill-at-ease. This may reflect as higher stress/workload. The study in this paper further refines how perceived workload may be determined based on occupant physiological measures. Because of great variation in individual personalities, age, driving experiences, gender, etc., a generic model applicable to all could not be developed. Rather, individual workload models that used physiological and vehicle measures were developed. Unlike some existing methods of workload estimation where one, or a few signals are used, such as electroencephalography (EEG), electrocardiography (ECG), we developed intelligent systems that use multiple physiological and vehicle signals based on an end-to-end deep neural learning architecture to make a robust estimation of workload. The deep neural learning system, MTS-CNN, is designed to learn workload patterns from synchronized, heterogeneous temporal signals. All data collected for training and testing are from real-world driving trips along the same route which comprised urban local roads and highways. Data from twenty participants whose driving experience ranged from a few months to several years were collected and analyzed. The experimental results indicate that the proposed driver workload estimation model is capable of learning well from the combined temporal physiological and vehicle signals and good performance was obtained on workload estimation.

SAE MOBILUS

Subscribers can view annotate, and download all of SAE's content. Learn More »

Access SAE MOBILUS »

Members save up to 18% off list price.
Login to see discount.
Special Offer: Download multiple Technical Papers each year? TechSelect is a cost-effective subscription option to select and download 12-100 full-text Technical Papers per year. Find more information here.
We also recommend:
TECHNICAL PAPER

Emergency Autonomous to Manual Takeover in a Driving Simulator: Teen vs. Adult Drivers – A Pilot Study

2018-01-0499

View Details

TECHNICAL PAPER

Driver Response Time to Cyclist Path Intrusions

2018-01-0531

View Details

TECHNICAL PAPER

Driver Behavior Detection based On PPP-GNSS Technology

2014-01-2006

View Details

X