TIM-6072 v1

Kulhandjian, H., Barron, J., Tamiyasu, M., Thompson, M., & Kulhandjian, M. (2024). AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors. Journal of Sensor and Actuator Networks, 13(3), 34. https://doi.org/10.3390/jsan13030034

This research covers pedestrian detection and avoidance systems. This study explores nighttime conditions, integrating video, infrared (IR) camera, and radar to enhance detection accuracy. The radar sensor improves detection through advancements in pedestrian range and movement direction. The study points out the critical role of AI multi-sensor fusion in improving pedestrian safety, particularly in low-light conditions.

Qiu, C., Tang, H., Yang, Y. et al. Machine vision-based autonomous road hazard avoidance system for self-driving vehicles. Sci Rep 14, 12178 (2024).

This research reviews machine vision-based road hazard avoidance systems for autonomous vehicles. The article aims to address challenges in small-object detection. The research also explores the testing of vehicle-mounted cameras for improved recognition of obstacles and depth position analysis, enhancing the accuracy of autonomous systems. The study stresses the importance of learning in machine vision perception, offering solutions to congestion and safety concerns.

Ekatpure, R. (2023). Machine Learning for Enhancing Vehicle Safety and Collision Avoidance Systems in Automotive Development: Techniques, Models, and Real-World Applications. Journal of Computational Intelligence and Robotics, 3(2), 1–43. Retrieved from https://thesciencebrigade.com/jcir/article/view/257

This research explores the application of machine learning (ML) in collision avoidance systems (CAS) and advanced driver-assistance systems (ADAS) to enhance safety. The article includes an overview of traditional functionalities and their limitations in complex traffic environments. The study highlights sensor fusion, integrating cameras, radar, and LiDAR to improve accuracy. The paper explores real-world applications of ML-driven CAS, demonstrating how AI enhances autonomous vehicle decision-making and human safety.

Saleel H. Abood, Hussein. M. H. Al-Khafaji, Mohanned M. H. Al-Khafaji. (2025). Enhancing Collision Avoidance in Mobile Robots Using YOLOv5: A Lightweight Approach for Unstructured Environments. Journal of Robotics and Control. Vol. 6 No.2.

The study highlights YOLOv5’s lightweight architecture for real-time object detection, making it ideal for unstructured environments where traditional obstacle avoidance struggles to process effectively. Researchers integrate deep learning models with sensor fusion techniques, combining cameras, LiDAR, and radar to enhance detection accuracy and improve safety.