dRISK awarded Edge Innovator of the year 2022

14th Oct 2022 dRISK

dRISK were awarded Edge innovator of the year 2022 at the Edge Computing World conference in Santa Clara, Silicon Valley. Our Transport Systems specialist, Kiran Jesudasan collected the award in person and gave the audience a view of the work dRISK are pushing toward in the relatively new world of Edge computing. Kiran described how dRISK specialises in collecting Edge cases, which are the countless risky vehicle scenarios which are individually unlikely, but together make up all the risk. These are used by the company to test, train and validate autonomous vehicles. dRISK collect millions of hours of data from many sources from all over the world. One of our largest data sources is CCTV. The data we collect is mostly made up of perfectly normal driving behaviors and does not contain the edge cases we are interested in. At this time we collect all the data and store this in our servers prior to interrogating it to find the valuable edge cases. Money and time is lost in sending the non-valuable data back and storing this. Kiran spoke about how Edge computing would mean that CCTV cameras themselves would be upgraded to have capabilities themselves to decide what was an Edge case and what was not, at source. In this way, only the valuable data would be sent back to dRISK and we would save on time and financial burden, allowing us to collect yet more data and edge cases to make self driving vehicles safer.

Edge computing means that powerful decision making processes are no longer limited to servers in offices, but can now be deployed where they are needed and useful. dRISK are working with suppliers such as Nvidia to develop Edge computing capabilities, which in this case will allow us to collect more accidents and near misses making future self driving vehicles safer and thus, commercially viable.

CEO & Founder of dRISK, Dr Chess Stetson was interviewed by Edge Computing World and the article can be seen here.

Tell us a bit about yourself – what led you to get involved in the edge computing market and dRISK

My background is in computational neuroscience, and I spent a long time realizing that most of the value in data was in the unexpected places or edge cases. dRISK builds tools for training and testing autonomous vehicles on edge cases, and perhaps ironically does so with a Knowledge Graph called dRISK Edge, which both stores knowledge, edge cases, and works on a distributed database, all of which have have either a semantic or technical relationship to edge computing. But in the strictest sense of edge computing, our heavy use is coming up, as the object detection feeding our knowledge graph starts to run on edge devices rather than centrally. Moreover, the advent of AVs will see more V2V, X2V and V2X communication requiring super low-latency, which will move decisionmaking to the edge.

What is it you & your company are uniquely bringing to the edge market?

We are bringing a dramatically new way to train and test autnomous vehicles on edge cases and, as I say above, it’s not just window dressing to say that all AVs will have an edge computing component in the near future.

Tell us more about the company, what’s your advantages compared to others on the market, who is involved, and what are your major milestones so far?

We are by far the most comprehensive resource for training and testing on edge cases, and our customers enjoy a 6x or greater improvement in safety performance.

How do you see the edge market developing over the next few years?

I don’t think it’s controversial to say that centralized heavy compute is expensive and subject to burdensome latency. That will change as a large variety of distributed computing resources take over the load where it’s needed.

How do you see the edge market developing over the next few years?

We focus on AVs, so I’ll keep my answers contained there. See above — V2X, V2V, X2V all mean edge computing. But we also work on large distributed graph databases, which are ideal for distributed computing, and it’s easy to see how edge compute could take graph computing all the way to the periphery of the computing process with ease.