SaFeR: A Safety Framework for e-Scooter Riders

Explore a comprehensive lecture on urban micromobility safety through a detailed presentation of the SaFeR framework, designed to enhance e-scooter rider safety. Learn about advanced driver assistance systems adapted for micromobility vehicles, focusing on real-time situational awareness algorithms that can run on everyday hardware. Discover deep learning applications in monocular depth estimation, including optimization techniques for real-time performance with single-camera setups. Examine the human-in-the-loop considerations in safety systems and understand how multiple sensor inputs contribute to a comprehensive safety assessment metric for riders. Delivered by Prof. Mahima Agumbe Suresh from San Jose State University, whose expertise spans edge computing, machine learning, and cyber-physical systems, this talk provides valuable insights into making urban micromobility safer and more efficient.