I-Corps: Smart Street Parking Assistant

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The broader impact/commercial potential of this I-Corps project is the development of technology for smart street parking. An estimated thirty percent of all traffic in the average U.S. city consists of vehicles searching for parking spots. Finding street parking in big cities can be challenging; the time spent searching is long, signage may be confusing or misinterpreted, and errors can be expensive (ticketed or towed). This could be mitigated with: (i) Street parking planning identifying suitable parking areas in advance of a trip, and (ii) curb-surfing verify the parking rules. The estimated market size for smart parking is $3.8 B. The proposed technology offers these two essential street parking services to drivers, delivery providers, and smart vehicle/smart city infrastructure through apps and APIs.This I-Corps project is based on the development of a series of machine learning algorithms and IoT technologies. Specifically, the novel techniques are developed and used in the following steps: (i) Street parking sign detection from pictures/videos taken by cameras on smartphones/vehicles; (ii) text recognition from the detected street parking signs; (iii) Natural language processing (NLP) used to generate parking rules; (iv) allowed parking time calculation; and (v) trust evaluation in crowdsourcing. This technology focuses on street parking sign recognition and interpretation. Although there are many AI-based object detection and text recognition techniques, typically they do not work well in these applications as the text on street parking signs is different and signs vary among cities. In addition, the technology addresses the challenge of distinguishing parking signs from other co-located signs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.