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Automated lane change behavior prediction and environmental perception based on SLAM technology
- 1 Computer Science Engineering, Santa Clara University, Santa Clara, USA
- 2 Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
- 3 Information Studies, Trine University, Phoenix, USA
- 4 Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
- 5 Information Systems, Northeastern University, Boston, MA, USA
* Author to whom correspondence should be addressed.
Abstract
In the automatic driving system, the external environment of the vehicle is perceived, in fact, there is also a perception sensor that has been silently dedicated in the system, that is, the positioning module. This paper explores the role of SLAM (Simultaneous Localization and Mapping) technology in autonomous vehicles, particularly in automatic lane change behavior prediction and environment perception. It emphasizes the limitations of traditional methods and the advantages of SLAM, especially visual SLAM, for accurate positioning and mapping. The discussion covers SLAM fundamentals, challenges, and the significance of visual SLAM's higher perception ability. Case studies on Waymo and Tesla illustrate the application of visual SLAM in achieving high-precision navigation and lane change prediction. The paper concludes by highlighting future research directions to enhance the intelligence and adaptability of automated lane change systems through advancements in AI and sensor technology, alongside optimizing SLAM algorithms for reliable driving in various scenarios.
Keywords
SLAM (Simultaneous Localization and Mapping), Autonomous Vehicles, Automatic Lane Change, Sensor Fusion, Environment Perception
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Cite this article
Lei,H.;Wang,B.;Shui,Z.;Yang,P.;Liang,P. (2024). Automated lane change behavior prediction and environmental perception based on SLAM technology. Applied and Computational Engineering,77,258-264.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
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