Dr. Ben Schmidt
This session will discuss how machine learning is being combined to facilitate a new type of proactive, instead of reactive, community engagement by municipal mangers and citizens. Utilizing fleets of roving, cheap, ubiquitous sensors, the future of the smart city will come radically into focus. We will discuss some of the RoadBotics progress towards commercializing and engaging with municipal governments to better understand their road and signage infrastructure and how that is changing the way governments approach planning and action.
RoadBotics provides engineers and pavement managers with an unprecedented means to monitor, identify and manage roadway surface problems and anomalies automatically and inexpensively. We have changed the cost/benefit ratio of monitoring roadways to a degree that regular, nearly continuous and precise monitoring is now readily affordable and feasible for everyone. This is done through the combination of RoadWay, our road data visualization platform, and RoadBot, our data collection service.
RoadWay is our AI-driven and cloud-based platform for assessing and visualizing your roadways that can be accessed from anywhere, anytime to provide unprecedented transparency and detail on the status of your roads.
The base Roadway Insights platform allows customers to view road data for their community which has been uploaded to the RoadBotics cloud by RoadBot. Customers usually have a RoadWay Pavement Assessment included, in which our proprietary machine-learning algorithms assesses each individual 3 meter segment of pavement and show these scores at their corresponding location (pictured above). We also offer RoadWay Object Geolocation, by which customers can choose to have various road-related objects (e.g. stop signs, fire hydrants) identified and geolocated, also automatically.
The RoadWay platform is powered by high-definition video data covering the roads of interest for our customer. At the moment, RoadWay is only compatible with data provided by RoadBot either in its full-service form, or its fleet-provision form.
Dr. Ben Schmidt Bio
Dr. Ben Schmidt is passionate about using technology to solve real world challenges. Ben has been involved in everything from front-end web technologies to server and cloud infrastructures to real time big data analytics in a diverse array of application fields.
Prior to Roadbotics, Ben was Chief Technology Officer of kWantera (https://kwantera.com/), a Pittsburgh-based venture backed startup that focuses on energy market forecasting and analytics. After moving from data scientist to CTO, he led the team on the technical due diligence that landed General Electric as a venture capital investor and subsequently scaled the team and the architecture to be able to compute thousands of pricing forecasts seamlessly on a cloud infrastructure and deliver them on a web-based platform to energy customers.
Ben started his career with nearly a decade of work in bioengineering applying an array of advanced image and signal processing techniques. This was initially applied to stem cell therapies (http://www.emeraldinsight.com/doi/abs/10.1108/01439910810854601) and then transitioned to non-invasive brain imaging (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3294084/) and network characterization (http://journal.frontiersin.org/article/10.3389/fnins.2014.00141/full) and prediction while he was pursuing a PhD in Bioengineering at the University of Pittsburgh.
Today Ben leads the team at RoadBotics that is applying the latest advances in deep learning to road infrastructure monitoring and maintenance. Along with his team of software engineers, product specialists and data scientists they are changing the way people manage their roads.