Cyclist Safety Enhancement: A Multi-Modal Hazard Detection System - Naval Joshi; Vaishnavi Bhutada; Manaswini Chittepu (International Journal of Machine Systems and Manufacturing Technology, 2024)
This study presents a multi-modal hazard detection system to enhance cyclist safety in urban environments. Lever- aging a combination of computer vision, object tracking, and predictive modeling, the system offers a comprehensive approach to identifying and mitigating potential risks. Key contributions include improved depth estimation through object size priors, multi-class tracking utilizing KCF and Brisk, and a novel recurrent neural network architecture for predicting bicycle movement. The system’s collision detection module evaluates the likelihood of collisions by analyzing future object and cyclist positions, incorporating uncertainties. While real-time implementation remains challenging due to hardware limitations, the modular design allows for future optimizations and integration with faster technologies. This research represents a valuable step toward safeguarding cyclists and paves the way for more effective hazard detection systems in the future
Keywords: KCF; RCNN; SLAM; cyclist safety; urban environments