<p>Emerging technologies such as real-time travel information
systems and automated vehicles (AVs) have profound impacts on driver decision-making
behavior. While they generally have positive impacts by enabling drivers to
make more informed decisions or by reducing their driving effort, there are
several concerns related to inadequate consideration of cognitive and
psychological aspects in their design. In this context, this dissertation
analyzes different aspects of driver cognition and psychology that arise from
drivers’ interactions with these technologies using physiological data
collected in two sets of driving simulator experiments.</p>
<p>This research analyzes the latent cognitive and psychological
effects of real-time travel information using electroencephalogram (EEG) data
measured in the first set of driving simulator experiments. Using insights from
the previous analysis, a hybrid route choice modeling framework is proposed
that incorporates the impacts of the latent information-induced cognitive and
psychological effects along with other explanatory variables that can be
measured directly (i.e., route characteristics, information characteristics,
driver attributes, and situational factors) on drivers’ route choice decisions.
EEG data is analyzed to extract two latent cognitive variables that capture the
driver’s cognitive effort during and immediately after the information provision,
and cognitive inattention before implementing the route choice decision. </p>
<p>Several safety concerns emerge for the transition of control
from the automated driving system to a human driver after the vehicle issues a
takeover warning under conditional vehicle automation (SAE Level 3). In this
context, this study investigates the impacts of driver’s pre-warning cognitive
state on takeover performance (i.e., driving performance while resuming manual
control) using EEG data measured in the second set of driving simulator
experiments. However, there is no comprehensive metric available in the
literature that could be used to benchmark the role of driver’s pre-warning
cognitive state on takeover performance, as most existing studies ignore the
interdependencies between the associated driving performance indicators by
analyzing them independently. This study proposes a novel comprehensive
takeover performance metric, Takeover Performance Index (TOPI), that combines
multiple driving performance indicators representing different aspects of
takeover performance. </p>
<p>Acknowledging the practical limitations of EEG data to have
real-world applications, this dissertation evaluates the driver’s situational
awareness (SA) and mental stress using eye-tracking and heart rate measures,
respectively, that can be obtained from in-vehicle driver monitoring systems in
real-time. The differences in SA and mental stress over time, their
correlations, and their impacts on the TOPI are analyzed to evaluate the
efficacy of using eye-tracking and heart rate measures for estimating the overall
takeover performance in conditionally AVs.</p>
The study findings can assist information service providers and auto
manufacturers to incorporate driver cognition and psychology in designing safer
real-time information and their delivery systems. They can also aid traffic
operators to incorporate cognitive aspects while devising strategies for
designing and disseminating real-time travel information to influence drivers’
route choices. Further, the study findings provide valuable insights to design
operating and licensing strategies, and regulations for conditionally automated
vehicles. They can also assist auto manufacturers in designing integrated
in-vehicle driver monitoring and warning systems that enhance road safety and
user experience.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/13362923 |
| Date | 14 December 2020 |
| Creators | Shubham Agrawal (9756986) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Understanding_the_Cognitive_and_Psychological_Impacts_of_Emerging_Technologies_on_Driver_Decision-Making_Using_Physiological_Data/13362923 |
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