Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Robotics

arXiv:2207.06061 (cs)
[Submitted on 13 Jul 2022 (v1), last revised 1 Sep 2022 (this version, v2)]

Title:DLCSS: Dynamic Longest Common Subsequences

Authors:Daniel Bogdoll, Jonas Rauch, J. Marius Zöllner
View PDF
Abstract:Autonomous driving is a key technology towards a brighter, more sustainable future. To enable such a future, it is necessary to utilize autonomous vehicles in shared mobility models. However, to evaluate, whether two or more route requests have the potential for a shared ride, is a compute-intensive task, if done by rerouting. In this work, we propose the Dynamic Longest Common Subsequences algorithm for fast and cost-efficient comparison of two routes for their compatibility, dynamically only incorporating parts of the routes which are suited for a shared trip. Based on this, one can also estimate, how many autonomous vehicles might be necessary to fulfill the local mobility demands. This can help providers to estimate the necessary fleet sizes, policymakers to better understand mobility patterns and cities to scale necessary infrastructure.
Comments: Accepted for publication at ICECCME 2022
Subjects: Robotics (cs.RO)
Cite as: arXiv:2207.06061 [cs.RO]
  (or arXiv:2207.06061v2 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2207.06061
Related DOI: https://doi.org/10.1109/ICECCME55909.2022.9987849

Submission history

From: Daniel Bogdoll [view email]
[v1] Wed, 13 Jul 2022 09:12:33 UTC (11,704 KB)
[v2] Thu, 1 Sep 2022 10:26:38 UTC (11,705 KB)
Full-text links:

Access Paper:

license icon view license
Current browse context:
cs.RO
< prev   |   next >
new | recent | 2022-07
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

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?)
Papers with Code (What is Papers with Code?)
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?)