Capturing and understanding people's experiences in cars : design research methods for real roads
Abstract/Contents
- Abstract
- This thesis develops and evaluates three primary methods for capturing and understanding in-context car experiences. A deeper, more nuanced understanding of the in-car driving experience will enable designers to generate user-centered concepts to improve the in-vehicle experience. Understanding the qualitative nature of the driving experience is not only useful for the effective design of vehicles, but also for the development of novel transportation technologies and infrastructure improvements. Critically, this work has implications for the design of self-driving technologies being developed and deployed today. This thesis asks: How we might enable researchers and designers to gain a novel perspective of the driving experience centered on naturalistic driving events and environments? To answer this question, I developed a platform, a method, and a dataset aimed at understanding driver's on-road experiences. The work presents novel insights attained by examining drivers in experimental as well as prosaic scenarios. It also positions the work relative to other approaches for understanding, eliciting, and tagging driver states. This thesis has three major components: Part One presents the Real Road Driving Simulator, a platform that can be used to safely simulate an autonomous vehicle on real roads in order to enable designers to evaluate prototypes and interaction principles intended to be deployed in autonomous driving scenarios. Part Two presents a method to safely and reliably elicit a state of distress in drivers of single-occupancy vehicles. This methodology can be used to test novel approaches for identifying driver states, as well as evaluate the effectiveness of interventions intended to address such a state of distress. Part Three develops an open-source dataset and tagging methodology using 50 real-world commutes. I discuss a novel approach for tagging data to leverage both qualitative and quantitative data to understand driver experience within the context of the urban commute.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Baltodano, Sonia Amanda |
|---|---|
| Degree supervisor | Ju, Wendy, 1975- |
| Degree supervisor | Leifer, Larry J |
| Thesis advisor | Ju, Wendy, 1975- |
| Thesis advisor | Leifer, Larry J |
| Thesis advisor | Gerdes, J. Christian |
| Degree committee member | Gerdes, J. Christian |
| Associated with | Stanford University, Department of Mechanical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Sonia Amanda Baltodano. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/kw071yj7937 |
Access conditions
- Copyright
- © 2021 by Sonia Amanda Baltodano
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...