In a special guest feature with insideBIGDATA, Radha Basu, Founder and CEO of iMerit, discusses “Leveraging Data Sharing and Network Intelligence to Solve AI Edge Cases.“
Here are 3 key takeaways from the article:
- Leveraging data sharing and network intelligence to solve edge cases
Edge cases as simple as the traffic cone will continue to be AV technology’s biggest challenge, but solving them will require more than annotating images to train an algorithm. It will also require a combination of smarter algorithms, data sharing, and network intelligence.
- Proactively addressing edge cases before the last mile
Most autonomous vehicle edge cases occur in the last mile. By nature, these situations and scenes are unstructured, with no two scenes looking exactly the same. This makes it difficult for the ML and AI to learn from it. Sometimes edge cases appear when we make assumptions about how images should be annotated to train the algorithm. Proactively addressing the edge cases before AVs hit the road is the key.
- Solving edge cases in real time requires data sharing
Today, it’s possible to better solve edge cases in real time in the context in which they happen, but it requires better data sharing between the AV companies and the municipalities where the vehicles are operating.
