Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
This paper presents a comprehensive framework
for detecting, classifying, and tracking vehicles using LiDAR
point cloud data in autonomous driving scenarios. Our
approach combines robust ground plane segmentation, deep
learning-based semantic segmentation using the PointSeg
network, L-shape oriented bounding box fitting, and joint
probabilistic data association (JPDA) tracking with an
interactive multiple model filter. Experiments conducted on
highway driving scenarios demonstrate the effectiveness of our
system in accurately detecting and classifying different vehicle
types while maintaining stable tracking through occlusions and
environmental variations. The proposed methodology addresses
several existing challenges in LiDAR-based perception systems,
offering a balanced approach between computational efficiency
and detection accuracy. Our results show that the combined
pipeline achieves robust performance in complex traffic
scenarios, making it suitable for real-world autonomous driving
applications.
"LiDAR-based Vehicle Detection, Classification, and Tracking for Autonomous Driving Systems", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a565-a569, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505060.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator