Hayden AI Announced as Finalist in Urban Tech Challengers Competition

San Francisco, CA – Hayden AI, a leader in artificial intelligence and machine learning, was named as an Urban Tech Challengers finalist. The winner will be announced in October following a pitch competition at Urban Tech Forward, Europe’s premier event for urban technology innovators.

Hayden AI is one of 15 finalists in the Optimising Urban Operations category. The company’s technology solutions for public transportation have been demonstrated to increase bus speeds, improve on-time performance, lower greenhouse gas emissions, and reduce vehicle collisions.

Urban Tech Challengers is a global competition for technology startups with products designed to make cities sustainable, resilient and equitable. The competition is hosted by Urban Tech Forward, an annual conference occurring this year on October 25th and 26th in Warsaw.

“Making cities safer, smarter, and more sustainable is the center of what we do at Hayden AI,” said Chris Carson, Hayden AI founder and CEO. “We’re thrilled to be an Urban Tech Challengers finalist and look forward to competing for – and winning – the top spot in October.”

Hayden AI is the United States market leader in mobile automated bus lane enforcement and bus stop enforcement, with over 650 camera systems installed on transit buses across the country. The company provides this technology to New York City’s Metropolitan Transportation Authority (MTA) and the Washington Metropolitan Area Transit Authority (WMATA) in Washington, DC, and has completed successful pilots with transit agencies in Philadelphia and Santa Monica, California.

Hayden AI’s mobile perception platform is packed with patented, industry-leading technologies in categories such as computer vision, machine learning, edge processing and hardware. Most recently, the company was awarded patents for its blockchain-powered data management system for traffic enforcement applications – designed to keep data secure and private – and its behind-the-windshield camera system that can successfully detect objects and license plates in low light and all weather conditions.