Autonomously Generated HD Maps
High-Definition (HD) Maps are a key component for autonomous vehicles. However, cost estimates are $2 Billion to map just the US once using special LIDAR mapping vehicles. Given the dynamic nature of the roadway system, frequent cost prohibitive updates are needed. Netradyne Drive-I provides an innovative inexpensive scalable low latency HD mapping solution using computer vision at the edge and crowdsourcing across commercial vehicles to autonomously generate HD maps. This autonomous crowdsourcing allows for multiple updates of the road conditions and layouts per day, including road changes due to construction, accidents, and other dynamic changes.
David is the CTO of Netradyne, an edge computing AI company. Before co-founding Netradyne, David worked at NASA’s Jet Propulsion Laboratory (JPL) on Galileo and Cassini deep space missions. He was later a Principal Engineer in Qualcomm Research, where he was awarded over 100 US patents covering a wide range of technologies, and started and led several R&D efforts, including the Qualcomm Zeroth Deep Learning Team. David has a BSEE from New Mexico State University, and an MS and PhD in Electrical Engineering from Stanford.