The content discusses the application of SLAM technology in the context of automated lane change behavior prediction and environmental perception for autonomous vehicles. It highlights the limitations of traditional positioning methods like GPS and the advantages of SLAM in providing accurate real-time localization and mapping capabilities.
The paper delves into the specifics of SLAM algorithms, sensor technologies like LiDAR and visual SLAM, and the importance of automatic lane changes in driving safety and efficiency. It showcases real-world examples from companies like Tesla, Waymo, and Mobileye, demonstrating the integration of AI-driven technologies, sensor fusion, and SLAM in autonomous driving systems.
The content emphasizes how SLAM enables autonomous vehicles to accurately sense their surroundings, including vehicle location, road structure, and traffic conditions, allowing them to make intelligent lane change decisions and ensure safe passage. It also discusses the future development of automated lane change systems, focusing on improving their intelligence, adaptability, and safety through advancements in sensor technology and AI algorithms.
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arxiv.org
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by Han Lei,Baom... at arxiv.org 04-09-2024
https://arxiv.org/pdf/2404.04492.pdfDeeper Inquiries