Skip to main content

Computer Science > Human-Computer Interaction

arXiv:2404.19021 (cs)
[Submitted on 29 Apr 2024]

Title:Enhancing Autonomous Vehicle Design and Testing: A Comprehensive Review of AR and VR Integration

Authors:Emanuella Ejichukwu, Lauren Tong, Gadir Hazime, Bochen Jia
View PDF
Abstract:This comprehensive literature review explores the potential of Augmented Reality and Virtual Reality technologies to enhance the design and testing of autonomous vehicles. By analyzing existing research, the review aims to identify how AR and VR can be leveraged to improve various aspects of autonomous vehicle development, including: creating more realistic and comprehensive testing environments, facilitating the design of user centered interfaces, and safely evaluating driver behavior in complex scenarios. Ultimately, the review highlights AR and VR utilization as a key driver in the development of adaptable testing environments, fostering more dependable autonomous vehicle technology, and ultimately propelling significant advancements within the field.
Subjects: Human-Computer Interaction (cs.HC)
Cite as: arXiv:2404.19021 [cs.HC]
  (or arXiv:2404.19021v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2404.19021

Submission history

From: Bochen Jia [view email]
[v1] Mon, 29 Apr 2024 18:04:30 UTC (451 KB)
Full-text links:

Access Paper:

  • View PDF
license icon view license

Current browse context:

cs.HC
< prev   |   next >
new | recent | 2024-04
Change to browse by:
cs

References & Citations

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)