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A Survey of Machine Learning Algorithms for Autonomous Vehicles

Lazarowska, Agnieszka, Rybczak, Monika, Łącki, Mirosław, Kozakiewicz, Krystian, Lisowski, Józef, Stateczny, Andrzej·2026·Electronics
DOI 10.3390/electronics15102073
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Abstract

This paper presents a comprehensive review of recent works (2020–2026) on machine learning (ML) algorithms applied to autonomous platforms such as unmanned underwater vehicles (UUVs), unmanned surface vehicles (USVs), unmanned aerial vehicles (UAVs), and ground-based mobile robots. The review focuses on the following functional areas: environment perception, simultaneous localization and mapping (SLAM), collision avoidance and path planning, and motion control. Different ML methods are covered, including supervised, semi-supervised, and unsupervised learning, as well as reinforcement learning and deep reinforcement learning. The reviewed methods are analyzed with respect to their performance, robustness, and suitability for different operational environments, including underwater, surface, air, and land domains. Finally, the authors identify key challenges and outline promising future directions aimed at improving the safety, autonomy, and reliability of autonomous vehicles.

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Metadata

Authors
Lazarowska, A., Rybczak, M., Łącki, M., Kozakiewicz, K., Lisowski, J., & Stateczny, A.
Venue
Electronics
Publisher
MDPI AG
Published
2026-05-13
Volume
15
Issue
10
Pages
2073
ISSN
2079-9292
DOI
10.3390/electronics15102073
Cited by
2
References
1
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Gdynia Maritime University (WI/2026/PZ/02)

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.