CURRANT.AI believes that better street level data will drive our society to a safer and smarter future. We aggregate street level information and apply urban analytics to provide:
(1) safer driving to individual drivers;
(2) smarter mobility to cities; and
(3) spatial intelligence to business organizations.
Video taken in Cambridge, MA, USA
Alert drivers to potential driving hazards such as incoming vehicles and pedestrians
Share aggregated and anonymized weather hazards and congestion information for smarter routing
Apply artificial intelligence to intelligently store critical video data
Generate collision report with video data together with estimated collision statistics
Integrate video footage analysis and geospatial data to provide real time parking availability
Pioneer a universally-applicable, manufacturer-independent, non-intrusive driver/vehicle-to-vehicle communication standard
Other crowdsourcing urban data particularly from streets and roads
Provide businesses with information like pedestrian volume and socio-economic estimates to assess commercial attractiveness
To Individual Drivers (2C)
The App will provide smarter and safer driving assistantships (such as hazard alerts, distance alerts, real-time parking assistant ) during driving and generate liability claim reports for drivers in case of accidents.
To Business & Cities (2B)
The smart urban analytics platform will generate street-level urban information, such as pedestrian volumes, usage of parking lot. All such information would provide new insight for commercial companies and government agencies.
Currant.AI is founded in Cambridge, Massachusetts in 2017 by a group of graduates from Massachusetts Institute of Technology (MIT). While the co-founders have very various training and backgrounds ranging from computer science, urban planning, transportation, and geography, they share the same vision that better street-level data will lead to a better city.
Li Xiaojiang is a postdoctoral fellow at MIT Senseable City Lab (SCL), and is an expert in data-driven geospatial analysis. His work has been featured in popular media outlets like the TIME, Wall Street Journal, Wired, The Guardian and Forbes.
AI & ML Scientist
Bill has a S.M. in Computational Engineering from MIT and specializes in deep learning and computer vision. As a data scientist in One Concern, he builds deep learning pipelines for city-wide analysis. He previously interned in Thumbtack and Arcstone.
Urban Planner & Designer
Waishan obtains city planning master degree from MIT and is now a Ph.D. student in Regional Science at Cornell University. He is an experienced front-end designer. He was a researcher at the Media Lab, Center for Advanced Urbanism, SCL working on geospatial analysis and urban computing.
Jintai is a Ph.D. student in Transportation at MIT. His current research work with the Transit Lab and the Urban Mobility Lab focuses on 1) demand prediction in response to the introduction of ride-hailing services provided by autonomous vehicles, and 2) user preference and segmentation.