Cyclist Safety Enhancement: A Multi-Modal Hazard Detection System

Year : 2024 | Volume :01 | Issue : 02 | Page : 35-83

Naval Joshi

Vaishnavi Bhutada

Manaswini Chittepu

  1. Student University of Petroleum and Energy studies Uttarakhand India
  2. Student Bharati Vidyapeeth’s College of Engineering for Women, Pune Maharashtra India
  3. Student Amrita Vishwa Vidyapeetham Hyderabad India


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, cyclist safety, urban environments, SLAM, RCNN

[This article belongs to International Journal of Machine Systems and Manufacturing Technology(ijmsmt)]

How to cite this article: Naval Joshi, Vaishnavi Bhutada, Manaswini Chittepu. Cyclist Safety Enhancement: A Multi-Modal Hazard Detection System. International Journal of Machine Systems and Manufacturing Technology. 2024; 01(02):35-83.
How to cite this URL: Naval Joshi, Vaishnavi Bhutada, Manaswini Chittepu. Cyclist Safety Enhancement: A Multi-Modal Hazard Detection System. International Journal of Machine Systems and Manufacturing Technology. 2024; 01(02):35-83. Available from:

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Regular Issue Subscription Original Research
Volume 01
Issue 02
Received March 14, 2024
Accepted March 20, 2024
Published May 9, 2024