G.Swathi Reddy1, Boga Venkat Sai2, Bommadeni Sricharan3, Bookya Madhu Nayak4, Chininga Praveen5, Jula Shiva Rama Krishna6 03 06 Detecting pedestrians in lowlight and nighttime environments is a major challenge for autonomous systems mainly due to poor visibility and the limitations of relying on a single sensor To address this issue this project introduces a multimodal pedestrian detection framework that combines infrared IR imaging with millimeterwave MMW radar data to improve detection accuracy reliability and realtime performance during night conditions An enhanced YOLOv5based deep learning model is utilized to extract meaningful spatial and semantic features from infrared images while radar signals are processed to obtain important information such as distance speed and position of moving objects To further enhance tracking accuracy an Extended Kalman Filter EKF is applied to reduce noise in radar data and to predict the motion of pedestrians over time The system performs spatiotemporal fusion by aligning radar target points with corresponding regions in infrared images followed by a correlation gating mechanism to ensure proper association between the two data sources Finally a decisionlevel fusion strategy integrates the outputs to produce precise and consistent pedestrian detection results Experimental results show that the proposed approach outperforms singlesensor methods offering improved precision and robustness under challenging nighttime conditions This work contributes to enhancing the safety and reliability of autonomous vehicles and intelligent monitoring systems operating in lowvisibility environments Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026