RoadBotics Raises $7.5 Million in Series a Funding Round Led by AI-Focused Radical Ventures

RoadBotics, which is standardizing road assessment globally using computer vision Artificial Intelligence, has closed a $7.5 Million Series A investment round lead by AI-focused venture capital fund Radical Ventures. Other investors include Hyperplane Venture Capital and Wharton Alumni Angels of Silicon Valley.

Only two and half years since founding, RoadBotics has over 150 customers in 23 US states and 11 countries. “This fresh capital, together with Radical’s vast network and Artificial Intelligence domain expertise, will enable us to grow even faster, and ultimately to extend our reach into other vertical markets,” said Mark DeSantis, Chief Executive Officer. “No one understands the impact deep learning is and will have on the management of large-scale public infrastructure like the Radical team who previously built their own deep learning company. They share our vision of AI helping to create a safer, better future,” he added.

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Radical Ventures recently launched a $350 Million fund focused on Artifical Intelligence applications. It is one of the largest AI-focused funds in the world. “We’re excited to be part of the RoadBotics journey,” said Jordan Jacobs, Co-Founder & Managing Partner of the fund. “Maintaining roads has an enormous financial cost – hundreds of billions of dollars globally each year, mostly borne by governments and thus taxpayers. Assessing roads has been done the same way for thousands of years – people riding around and subjectively determining what to fix and when. It’s a broken system. RoadBotics’ simple-to-use computer vision system enables continuous, objective assessments that help priortize what needs to be fixed. This saves enormous costs, reduces damage to vehicles and can saves lives. RoadBotics is a perfect example of Artifical Intelligence fixing an enormous global problem.”

RoadBotics was formed from the research of Christoph Mertz, Chief Scientist at RoadBotics, a researcher at the Carnegie Mellon University Navigation Laboratory, which itself is an early pioneer in the application of machine vision to autonomous navigation. “I thought: ‘Why not use this tech to assess the roads on which our autonomous vehicles drive?'” Dr. Mertz commented. “That’s when I knew this could be a big business,” he added. RoadBotics was formed shortly thereafter in December 2016, with the help of the CMU NavLab, Traffic21 and its affiliated University Transportation Centers.

Paved roads are expensive ⁠to build and maintain. Various estimates put the annual cost of maintaining the roughly 4 million miles of roads in the United States at over $100B and many acknowledge that is a significant underspend. The American Society for Civil Engineers’ well-respected annual grading of US infrastructure gives US roadways a D. Globally, road maintenance spending approaches a half a trillion dollars.

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Because of the vast maintenance expense, together with the need to replace and rehab roads in the US and around the world, there is a renewed focus on preventative maintenance, not just for roads but for of the other large-scale public assets around the world. Having accurate and continuous information data on the condition on big assets likes roads is now readily affordable and accessible thanks to the advent of deep learning.

Historically, most local governments, and road managers in particular, have manually inspected their roads for surface distresses, allowing subjective data to creep into the decision process when making plans for repairs. RoadBotics has helped over 150 municipalities around the world receive the data they need for their road planning. RoadBotics technology uses machine learning to rate road conditions, making the data objective, and more cost-effective and faster than traditional inspection methods. The process allows road managers to adopt a more regular inspection schedule.

RoadWay, RoadBotics’ online GIS platform, offers decision-makers an easy way to monitor their road networks, communicate budget needs to city council, or resolve citizen complaints. The roads and photos of each 10-foot segment are color-coded from green to red, allowing anyone from a council member or a citizen to a field operator to use it without any prior knowledge or training.

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RoadBotics will use this round of funding to accelerate its growth and development of new products in order to further help local governments and other infrastructure asset managers to get important, objective data about their roads.

“The new products we’re building use data in ways not available to road managers until now,” said Miguel Dickson, Director of Engineering at RoadBotics. “We’re taking our value proposition ⁠— objective, easy-to-use, easy-to-explain automated assessment ⁠and expanding on it. The new products we’re introducing will continue to leverage AI and data science to make more road management tasks straight-forward and easily communicable to all the relevant stakeholders.”

RoadBotics is committed to making reliable, objective road data available to everyone involved in road maintenance. Now, with this round of funding, RoadBotics will be able to help provide more infrastructure data solutions to those who need them most.

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