International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 4 (July-August 2026) Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
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Volume 8 Issue 4
July-August 2026

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Comprehensive Study on Object Detection for Autonomous Vehicles to Avoid Collision Risk

Author(s) Ms. Sharmistha Dey, Prof. Dr. Kuldeep Chouhan
Country India
Abstract The proliferation of autonomous vehicle necessitates robust perception systems capable of functioning within inherently dynamic traffic environments. This dissertation systematically evaluates state-of-the-art object detection methodologies for autonomous vehicles through comprehensive taxonomic analysis. The prime objectives encompass: establishing a comparative framework for evaluating heterogeneous detection approaches; identifying domain-specific optimization strategies; and elucidating research trajectories within this rapidly evolving domain. The investigation categorizes detection paradigms across multiple dimensions: spatial representation (2D/3D), computational architecture (single/multi-stage algorithms), and sensory modalities (camera/LiDAR/fusion approaches). Methodologically, this work employs systematic review protocols incorporating bibliometric analysis of high-impact publications from Scopus, Elsevier, Springer, and Google Scholar (2018-2025). Performance evaluation integrates multiple benchmarks utilizing standardized metrics (mAP, IoU, latency) to facilitate cross-architectural comparison. Implementation will adapt high-performing architectures through transfer learning with emphasis on edge-optimization techniques including quantization-aware training and neural architecture search. Validation will employ both simulation environments and instrumented vehicle testing. This investigation addresses critical gaps in perception system robustness while providing actionable insights for autonomous driving technology advancement under diverse operational scenarios, ultimately enhancing traffic safety through more reliable obstacle detection
Keywords 3D Object Detection, LiDAR, Multi-Sensor Fusion, Deep Learning, Perception System, YOLO
Field Engineering
Published In Volume 8, Issue 4, July-August 2026
Published On 2026-08-12
DOI https://doi.org/10.36948/ijfmr.2026.v08i04.85669
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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