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Vol. 10, No. 2, May 2025

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Optimizing Autonomous Navigation: Advances in LiDAR-based Object Recognition with Modified Voxel-RCNN

https://doi.org/10.22219/kinetik.v10i2.2199
Firman
Universitas Garut
Arief Suryadi Satyawan
Research Center for Telecommunication - BRIN
Helfy Susilawati
Universitas Garut
Mokh. Mirza Etnisa Haqiqi
Universitas Garut
Khaulyca Arva Artemysia
Universitas Garut
Sani Moch Sopian
Universitas Garut
Beni Wijaya
Universitas Garut
Muhammad Ikbal Samie
Universitas Garut

Corresponding Author(s) : Firman

24052121044@fteknik.uniga.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 2, May 2025

Abstract

This study aimed to enhance the object recognition capabilities of autonomous vehicles in constrained and dynamic environments. By integrating Light Detection and Ranging (LiDAR) technology with a modified Voxel-RCNN framework, the system detected and classified six object classes: human, wall, car, cyclist, tree, and cart. This integration improved the safety and reliability of autonomous navigation. The methodology included the preparation of a point cloud dataset, conversion into the KITTI format for compatibility with the Voxel-RCNN pipeline, and comprehensive model training. The framework was evaluated using metrics such as precision, recall, F1-score, and mean average precision (mAP). Modifications to the Voxel-RCNN framework were introduced to improve classification accuracy, addressing challenges encountered in complex navigation scenarios. Experimental results demonstrated the robustness of the proposed modifications. Modification 2 consistently outperformed the baseline, with 3D detection scores for the car class in hard scenarios increasing from 4.39 to 10.31. Modification 3 achieved the lowest training loss of 1.68 after 600 epochs, indicating significant improvements in model optimization. However, variability in the real-world performance of Modification 3 highlighted the need for balancing optimized training with practical applicability. Overall, the study found that the training loss decreased up to 29.1% and achieved substantial improvements in detection accuracy under challenging conditions. These findings underscored the potential of the proposed system to advance the safety and intelligence of autonomous vehicles, providing a solid foundation for future research in autonomous navigation and object recognition.

Keywords

Autonomous vehicles LiDAR Voxel-RCNN Object recognition Point cloud dataset 3D detection
Firman, Satyawan, A. S., Susilawati, H., Haqiqi, M. M. E. ., Artemysia, K. A., Sopian, S. M., Wijaya, B., & Samie, M. I. (2025). Optimizing Autonomous Navigation: Advances in LiDAR-based Object Recognition with Modified Voxel-RCNN. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(2). https://doi.org/10.22219/kinetik.v10i2.2199
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Author Biographies

Firman, Universitas Garut

Lead author, Conceptual, Metodology, Experimental, Analisis,Dataset Production, Writing, Editing

Arief Suryadi Satyawan, Research Center for Telecommunication - BRIN

Conceptual, Metodology, Writing Review

Helfy Susilawati, Universitas Garut

Conceptual, Metodology, Writing Review

Mokh. Mirza Etnisa Haqiqi, Universitas Garut

Conceptual, Metodology, Writing Review

Khaulyca Arva Artemysia, Universitas Garut

Dataset Production, Experimental

Sani Moch Sopian, Universitas Garut

Dataset Production, Experimental

Beni Wijaya, Universitas Garut

Dataset Production, Experimental

Muhammad Ikbal Samie, Universitas Garut

Dataset Production, Experimental

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