Developing, Analyzing, and Evaluating Self-Drive Algorithms Using Drive-by-Wire Electric Vehicles
Reliable lane-following algorithms are essential for safe and effective autonomous driving. This project was primarily focused on developing and evaluating different lane-following programs to find the most reliable algorithm for a Vehicle to Everything (V2X) project. The algorithms were first teste...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2024-09 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | Froemming-Aldanondo, Beñat Rastoskueva, Tatiana Evans, Michael Machado, Marcial Vadella, Anna Johnson, Rickey Escamilla, Luis Jostes, Milan Butani, Devson Ryan Kaddis Chan-Jin, Chung Siegel, Joshua |
| description | Reliable lane-following algorithms are essential for safe and effective autonomous driving. This project was primarily focused on developing and evaluating different lane-following programs to find the most reliable algorithm for a Vehicle to Everything (V2X) project. The algorithms were first tested on a simulator and then with real vehicles equipped with a drive-by-wire system using ROS (Robot Operating System). Their performance was assessed through reliability, comfort, speed, and adaptability metrics. The results show that the two most reliable approaches detect both lane lines and use unsupervised learning to separate them. These approaches proved to be robust in various driving scenarios, making them suitable candidates for integration into the V2X project. |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_3101379271</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3101379271</sourcerecordid><originalsourceid>FETCH-proquest_journals_31013792713</originalsourceid><addsrcrecordid>eNqNjd0KgjAcxUcQJOU7DLpt4LbMupQ0uu_rUpb91clytqlgT59JD9DVOed3DpwJchjnlGzXjM2Qa23peR7bBMz3uYNEBB0oXcsqX-GwEqp_j1ZUDxx3QrWiGTI-gcpIZGQHOFS5NrIpnhZf7LcbMbn35CYN4FhB2hiZ4isUMlVgF2iaCWXB_ekcLQ_xeX8ktdGvFmyTlLo1w7NNOPUoD3YsoPy_1QfdeUSU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3101379271</pqid></control><display><type>article</type><title>Developing, Analyzing, and Evaluating Self-Drive Algorithms Using Drive-by-Wire Electric Vehicles</title><source>Free E- Journals</source><creator>Froemming-Aldanondo, Beñat ; Rastoskueva, Tatiana ; Evans, Michael ; Machado, Marcial ; Vadella, Anna ; Johnson, Rickey ; Escamilla, Luis ; Jostes, Milan ; Butani, Devson ; Ryan Kaddis ; Chan-Jin, Chung ; Siegel, Joshua</creator><creatorcontrib>Froemming-Aldanondo, Beñat ; Rastoskueva, Tatiana ; Evans, Michael ; Machado, Marcial ; Vadella, Anna ; Johnson, Rickey ; Escamilla, Luis ; Jostes, Milan ; Butani, Devson ; Ryan Kaddis ; Chan-Jin, Chung ; Siegel, Joshua</creatorcontrib><description>Reliable lane-following algorithms are essential for safe and effective autonomous driving. This project was primarily focused on developing and evaluating different lane-following programs to find the most reliable algorithm for a Vehicle to Everything (V2X) project. The algorithms were first tested on a simulator and then with real vehicles equipped with a drive-by-wire system using ROS (Robot Operating System). Their performance was assessed through reliability, comfort, speed, and adaptability metrics. The results show that the two most reliable approaches detect both lane lines and use unsupervised learning to separate them. These approaches proved to be robust in various driving scenarios, making them suitable candidates for integration into the V2X project.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Algorithms ; Drive by wire ; Electric vehicles ; Unsupervised learning</subject><ispartof>arXiv.org, 2024-09</ispartof><rights>2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Froemming-Aldanondo, Beñat</creatorcontrib><creatorcontrib>Rastoskueva, Tatiana</creatorcontrib><creatorcontrib>Evans, Michael</creatorcontrib><creatorcontrib>Machado, Marcial</creatorcontrib><creatorcontrib>Vadella, Anna</creatorcontrib><creatorcontrib>Johnson, Rickey</creatorcontrib><creatorcontrib>Escamilla, Luis</creatorcontrib><creatorcontrib>Jostes, Milan</creatorcontrib><creatorcontrib>Butani, Devson</creatorcontrib><creatorcontrib>Ryan Kaddis</creatorcontrib><creatorcontrib>Chan-Jin, Chung</creatorcontrib><creatorcontrib>Siegel, Joshua</creatorcontrib><title>Developing, Analyzing, and Evaluating Self-Drive Algorithms Using Drive-by-Wire Electric Vehicles</title><title>arXiv.org</title><description>Reliable lane-following algorithms are essential for safe and effective autonomous driving. This project was primarily focused on developing and evaluating different lane-following programs to find the most reliable algorithm for a Vehicle to Everything (V2X) project. The algorithms were first tested on a simulator and then with real vehicles equipped with a drive-by-wire system using ROS (Robot Operating System). Their performance was assessed through reliability, comfort, speed, and adaptability metrics. The results show that the two most reliable approaches detect both lane lines and use unsupervised learning to separate them. These approaches proved to be robust in various driving scenarios, making them suitable candidates for integration into the V2X project.</description><subject>Algorithms</subject><subject>Drive by wire</subject><subject>Electric vehicles</subject><subject>Unsupervised learning</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNjd0KgjAcxUcQJOU7DLpt4LbMupQ0uu_rUpb91clytqlgT59JD9DVOed3DpwJchjnlGzXjM2Qa23peR7bBMz3uYNEBB0oXcsqX-GwEqp_j1ZUDxx3QrWiGTI-gcpIZGQHOFS5NrIpnhZf7LcbMbn35CYN4FhB2hiZ4isUMlVgF2iaCWXB_ekcLQ_xeX8ktdGvFmyTlLo1w7NNOPUoD3YsoPy_1QfdeUSU</recordid><startdate>20240904</startdate><enddate>20240904</enddate><creator>Froemming-Aldanondo, Beñat</creator><creator>Rastoskueva, Tatiana</creator><creator>Evans, Michael</creator><creator>Machado, Marcial</creator><creator>Vadella, Anna</creator><creator>Johnson, Rickey</creator><creator>Escamilla, Luis</creator><creator>Jostes, Milan</creator><creator>Butani, Devson</creator><creator>Ryan Kaddis</creator><creator>Chan-Jin, Chung</creator><creator>Siegel, Joshua</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20240904</creationdate><title>Developing, Analyzing, and Evaluating Self-Drive Algorithms Using Drive-by-Wire Electric Vehicles</title><author>Froemming-Aldanondo, Beñat ; Rastoskueva, Tatiana ; Evans, Michael ; Machado, Marcial ; Vadella, Anna ; Johnson, Rickey ; Escamilla, Luis ; Jostes, Milan ; Butani, Devson ; Ryan Kaddis ; Chan-Jin, Chung ; Siegel, Joshua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_31013792713</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Drive by wire</topic><topic>Electric vehicles</topic><topic>Unsupervised learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Froemming-Aldanondo, Beñat</creatorcontrib><creatorcontrib>Rastoskueva, Tatiana</creatorcontrib><creatorcontrib>Evans, Michael</creatorcontrib><creatorcontrib>Machado, Marcial</creatorcontrib><creatorcontrib>Vadella, Anna</creatorcontrib><creatorcontrib>Johnson, Rickey</creatorcontrib><creatorcontrib>Escamilla, Luis</creatorcontrib><creatorcontrib>Jostes, Milan</creatorcontrib><creatorcontrib>Butani, Devson</creatorcontrib><creatorcontrib>Ryan Kaddis</creatorcontrib><creatorcontrib>Chan-Jin, Chung</creatorcontrib><creatorcontrib>Siegel, Joshua</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Froemming-Aldanondo, Beñat</au><au>Rastoskueva, Tatiana</au><au>Evans, Michael</au><au>Machado, Marcial</au><au>Vadella, Anna</au><au>Johnson, Rickey</au><au>Escamilla, Luis</au><au>Jostes, Milan</au><au>Butani, Devson</au><au>Ryan Kaddis</au><au>Chan-Jin, Chung</au><au>Siegel, Joshua</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Developing, Analyzing, and Evaluating Self-Drive Algorithms Using Drive-by-Wire Electric Vehicles</atitle><jtitle>arXiv.org</jtitle><date>2024-09-04</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>Reliable lane-following algorithms are essential for safe and effective autonomous driving. This project was primarily focused on developing and evaluating different lane-following programs to find the most reliable algorithm for a Vehicle to Everything (V2X) project. The algorithms were first tested on a simulator and then with real vehicles equipped with a drive-by-wire system using ROS (Robot Operating System). Their performance was assessed through reliability, comfort, speed, and adaptability metrics. The results show that the two most reliable approaches detect both lane lines and use unsupervised learning to separate them. These approaches proved to be robust in various driving scenarios, making them suitable candidates for integration into the V2X project.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2024-09 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_proquest_journals_3101379271 |
| source | Free E- Journals |
| subjects | Algorithms Drive by wire Electric vehicles Unsupervised learning |
| title | Developing, Analyzing, and Evaluating Self-Drive Algorithms Using Drive-by-Wire Electric Vehicles |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T16%3A47%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Developing,%20Analyzing,%20and%20Evaluating%20Self-Drive%20Algorithms%20Using%20Drive-by-Wire%20Electric%20Vehicles&rft.jtitle=arXiv.org&rft.au=Froemming-Aldanondo,%20Be%C3%B1at&rft.date=2024-09-04&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3101379271%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3101379271&rft_id=info:pmid/&rfr_iscdi=true |