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Advancements, Challenges and Safety Implications of AI in Autonomous Vehicles: A Comparative Analysis of Urban vs. Highway Environments

Journal of Robotics and Control (JRC) · Vol. 5 No. 3 (2024) · hlm. 613–635

DOI
10.18196/jrc.v5i3.21114
ISSN
2715-5072, 2715-5056
Terbit
2024-02-27
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng

Abstrak

This research reviews AI integration in AVs, evaluating its effectiveness in urban and highway settings. Analyzing over 161 studies, it explores advancements like machine learning perception, sensor technology, V2X communication, and adaptive cruise control. It also examines challenges like traffic congestion, pedestrian and cyclist safety, regulations, and technology limitations. Safety considerations include human-AI interaction, cybersecurity, and liability/ethics. The study contributes valuable insights into the latest developments and challenges of AI in AVs, specifically in urban and highway contexts, which will guide future transportation research and decision-making. In urban settings, AI-powered sensor fusion technology helps AVs navigate dynamic traffic safely. On highways, adaptive cruise control systems maintain safe distances, reducing accidents. These findings suggest AI facilitates safer navigation in urban areas and enhances safety and efficiency on highways. While AI integration in AVs holds immense potential, innovative solutions like advanced perception systems and optimized long-range communication are needed to create safer and more sustainable transportation systems.

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40 penulis · 110 tautan kepenulisan bersama · 42 lintas organisasi · menampilkan 40 yang paling banyak terhubung

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  • Sebelas Maret University
  • Universitas Ahmad Dahlan
  • Universiti Teknikal Malaysia Melaka
  • Universitas Gadjah Mada
  • Advanced Semiconductor Engineering (Taiwan)
  • Muhammadiyah University of Yogyakarta
  • King Fahd University of Petroleum and Minerals
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Iswanto SuwarnoMuhammadiyah University of Yogyakarta4
Muhammad NizamSebelas Maret University4
Feri AdriyantoSebelas Maret University4
Chico HermanuSebelas Maret University4
Oyas WahyunggoroUniversitas Gadjah Mada3
Adha Imam CahyadiUniversitas Gadjah Mada3
Magdi S. MahmoudKing Fahd University of Petroleum and Minerals3
Mahmoud A. MossaMinia University3
Davut İzciBursa Uludağ Üni̇versi̇tesi̇3
Serdar EkinciGedik University3
Agus RamelanSebelas Maret University3
Furizal FurizalUniversitas Ahmad Dahlan3
Kuo Lung LianAfiliasi tidak diketahui2
Bwo Ren KeAfiliasi tidak diketahui2
Henry Probo SantosoSebelas Maret University2
Abdullah ÇakanKonya Technical University2
Marco Antonio Márquez-VeraUniversidad Politécnica de Pachuca2
Mostafa JabariSahand University of Technology2
Joko Slamet SaputroSebelas Maret University2
Berlian Shanaza AndianySebelas Maret University2
Miftahul AnwarSebelas Maret University2
Muhammad NizamHasanuddin University1
Inayati InayatiSebelas Maret University1
Mufti Reza Aulia PutraSebelas Maret University1
Anif JamaluddinSebelas Maret University1
Inayati InayatiSebelas Maret University1
Azis UbaidilahSebelas Maret University1
Supriyanto PraptodiyonoSultan Ageng Tirtayasa University1
Sunardi SunardiUniversitas Ahmad Dahlan1
Rio Ikhsan AlfianUniversitas Ahmad Dahlan1
Naufal Rahmat SetiawanAfiliasi tidak diketahui1
Zainal ArifinSebelas Maret University1
Catur HarsitoSebelas Maret University1
Musyaffa’ AhmadSebelas Maret University1
Irwan IftadiSebelas Maret University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Advancements, Challenges and Safety Implications of AI in Autonomous Vehicles: A Comparative Analysis of Urban vs. Highway Environments</dc:title>
	<dc:creator>Abu, N. S.</dc:creator>
	<dc:creator>Bukhari, W. M.</dc:creator>
	<dc:creator>Adli, M. H.</dc:creator>
	<dc:creator>Maghfiroh, Hari</dc:creator>
	<dc:creator>Ma’arif, Alfian</dc:creator>
	<dc:subject xml:lang="en-US">Autonomous Vehicles (AVs)</dc:subject>
	<dc:subject xml:lang="en-US">AI-driven Navigation</dc:subject>
	<dc:subject xml:lang="en-US">AI-enable Sensor Fusion</dc:subject>
	<dc:subject xml:lang="en-US">Machine Learning-Based Perception Systems in AVs</dc:subject>
	<dc:subject xml:lang="en-US">Pedestrian and Cyclist Safety</dc:subject>
	<dc:subject xml:lang="en-US">Urban and Highway Transportations.</dc:subject>
	<dc:description xml:lang="en-US">This research reviews AI integration in AVs, evaluating its effectiveness in urban and highway settings. Analyzing over 161 studies, it explores advancements like machine learning perception, sensor technology, V2X communication, and adaptive cruise control. It also examines challenges like traffic congestion, pedestrian and cyclist safety, regulations, and technology limitations. Safety considerations include human-AI interaction, cybersecurity, and liability/ethics. The study contributes valuable insights into the latest developments and challenges of AI in AVs, specifically in urban and highway contexts, which will guide future transportation research and decision-making. In urban settings, AI-powered sensor fusion technology helps AVs navigate dynamic traffic safely. On highways, adaptive cruise control systems maintain safe distances, reducing accidents. These findings suggest AI facilitates safer navigation in urban areas and enhances safety and efficiency on highways. While AI integration in AVs holds immense potential, innovative solutions like advanced perception systems and optimized long-range communication are needed to create safer and more sustainable transportation systems.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2024-02-27</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/21114</dc:identifier>
	<dc:identifier>10.18196/jrc.v5i3.21114</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 5 No. 3 (2024); 613-635</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:source>10.18196/jrc.v5i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/21114/9058</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 N. S. Abu, W. M. Bukhari, M. H. Adli, Hari Maghfiroh, Alfian Ma’arif</dc:rights>
</oai_dc:dc>