Development and Demonstration of Scalable Methods for Research on Human Factors in Driving

Despite the crucial need to ensure that sufficient attention is directed to the driving task, our research understanding of detailed relationships between driving task complexity, in-vehicle and off-road distractions, and driving performance remains limited. Part of this limitation is due to inherent challenges of carrying out large scale research, on complex driving scenarios, using driving simulators and instrumented vehicles. The objective of my dissertation research was to develop efficient methods to support detailed research on driving human factors. The methods that I used included 1) realistic rendering of driving scenarios in computer graphics generated videos, 2) simplified mental workload assessment using ratings of workload associated with driving scenarios (as represented in videos), and 3) a method of synchronized driving that may be used to assess distraction based on the visual scanning behavior of a “driver” who makes steering, braking and acceleration inputs intended to mimic the driver actions in a recorded driving video. I examined the application of the methods developed in three studies. Study 1 looked at the reliability of the simplified driver workload assessment method that I introduced in this dissertation. Study 2 then examined how scenario based workload assessments and workload-inducing features of the driving environment affect notification receptivity (i.e., willingness to listen to or respond to notifications while driving). The results of this study should provide useful insights for the development of \acrfull{sdn} systems that help to maintain workload within a manageable range while driving under the influence of information technology. Study 3 then used combined eye tracking and think aloud, along with the synchronized driving method developed in this dissertation, to identify instances of off-road distraction. The results of Study 3 identified the temporal and movement feature groups that best predict on-road and off-road distraction in drivers. The methods developed in this dissertation are intended to be a useful supplement to the driving simulator and instrumented vehicle methods that are typically used to study human factors in driving, particularly in regard to research on IT-enabled vehicles with varying levels of automation.

Department dc:contributor.department

Mechanical and Industrial Engineering

Year dc:date.issued

2025

Author dc:creator

• Jiang, Haoyan

Advisor dc:contributor.advisor

• Chignell, Mark H.

Statement dc:rights

• Attribution-NonCommercial-ShareAlike 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-sa/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1807/145137

OAI identifier oai:identifier

oai:utoronto.scholaris.ca:1807/145137