The Need
Intersections are among the most dangerous areas on U.S. roadways, accounting for approximately 25% of traffic fatalities and nearly half of all injuries annually. Vulnerable Road Users (VRUs), including pedestrians and cyclists, face increasing risk due to complex traffic dynamics and limited situational awareness. Current infrastructure lacks the intelligence and responsiveness needed to proactively prevent collisions, especially under low visibility or unpredictable conditions. There is a critical need for scalable, real-time safety systems that can protect all road users.

The Technology
Researchers at The Ohio State University’s Center for Automotive Research have developed an AI-powered Intersection Safety System (ISS) that integrates low-cost radar and camera sensors with advanced machine learning algorithms. The system detects, tracks, and predicts the behavior of vehicles and VRUs in real time. It issues proactive visual and audio warnings and can interface with traffic control systems to mitigate imminent threats (e.g., turning all lights red and issuing audible and visual warnings in the ambient environment and to local personal devices). The ISS is designed for seamless integration with existing infrastructure and supports both connected and non-connected road users.

Benefits/Advantages
• Cost-effective deployment: Utilizes existing infrastructure and low-cost sensors to minimize installation and maintenance costs.
• Real-time, multi-modal safety: Simultaneously protects pedestrians, cyclists, and vehicles using AI-driven prediction and alerts.
• Scalable and modular: Easily adaptable to various intersection types and urban environments.
• Privacy-conscious design: Processes anonymized data to ensure compliance with privacy regulations.
• Federal Support: Among the winners of the Department of Transportation Intersection Safety Challenge Stages 1A and 1B.

Provisional patent application filed