Smart Intersection Monitoring for Pedestrian Safety: A Multi-Sensor Approach to Urban Mobility

Abstract

The Smart Intersection Monitoring System (SIMS) represents a significant advancement in urban mobility safety through the integration of multi-sensor perception and artificial intelligence. By fusing data from high-resolution cameras, LiDAR, and radar technologies, SIMS creates a robust environmental awareness layer that can detect, track, and predict the behavior of vulnerable road users in complex intersection environments. The system employs a multi-tiered machine learning framework that progresses from object detection to trajectory prediction and ultimately to risk assessment, enabling preemptive identification of potential conflicts. Implementation follows a distributed computing paradigm, balancing edge processing for time-critical operations with cloud analytics for long-term pattern recognition. Field validations across multiple urban intersections demonstrate the system's effectiveness in maintaining high detection accuracy across varied environmental conditions, achieving precise trajectory predictions, and significantly reducing traffic conflicts through targeted interventions. SIMS provides a scalable framework for enhancing pedestrian safety in increasingly dense urban environments while maintaining privacy through careful data handling practices. The fusion of these complementary technologies enables resilient operation during adverse weather and lighting conditions where traditional monitoring systems fail, addressing a critical vulnerability in urban safety infrastructure. Additionally, the system's modular architecture allows for incremental deployment and scalability across diverse intersection types, from simple four-way junctions to complex multi-modal transit hubs, ensuring applicability across the full spectrum of urban environments.