Version 1
: Received: 22 July 2024 / Approved: 23 July 2024 / Online: 24 July 2024 (16:14:22 CEST)
How to cite:
Kozłowski, M.; Racewicz, S.; Wierzbicki, S. Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison. Preprints2024, 2024071857. https://doi.org/10.20944/preprints202407.1857.v1
Kozłowski, M.; Racewicz, S.; Wierzbicki, S. Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison. Preprints 2024, 2024071857. https://doi.org/10.20944/preprints202407.1857.v1
Kozłowski, M.; Racewicz, S.; Wierzbicki, S. Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison. Preprints2024, 2024071857. https://doi.org/10.20944/preprints202407.1857.v1
APA Style
Kozłowski, M., Racewicz, S., & Wierzbicki, S. (2024). Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison. Preprints. https://doi.org/10.20944/preprints202407.1857.v1
Chicago/Turabian Style
Kozłowski, M., Szymon Racewicz and Sławomir Wierzbicki. 2024 "Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison" Preprints. https://doi.org/10.20944/preprints202407.1857.v1
Abstract
The integration of advanced image analysis using artificial intelligence (AI) is pivotal for the evolution of autonomous vehicles (AVs). This article provides a thorough review of the most significant datasets and the latest state-of-the-art AI solutions employed in image analysis for AVs. Datasets such as Cityscapes, NuScenes, and CARLA form the benchmarks for training and evaluating different AI models, with unique characteristics catering to various aspects of autonomous driving. Key AI methodologies, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer models, and Generative Adversarial Networks (GANs), are discussed. The article also presents a comparative analysis of various AI techniques in real-world scenarios, focusing on semantic image segmentation, 3D object detection, and vehicle control in virtual environments. Simultaneously, the role of multisensor datasets and simulation platforms like AirSim, TORCS, and SUMMIT in enriching the training data and testing environments for AVs is highlighted. By synthesizing information on datasets, AI solutions, and comparative performance evaluations, the article serves as a crucial resource for researchers, developers, and industry stakeholders. Offering a clear view of the current landscape and future directions in autonomous vehicle image analysis technologies.
Keywords
autonomous vehicles; image analysis; AI solutions; safety features
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
