Review by BDD of ietf-lisp-nexagon Will send comments by authors on the H3 steering committee later this week.
--szb Cell: +972.53.2470068 WhatsApp: +1.650.492.0794 Begin forwarded message: > From: Trevor Darrell <[email protected]> > Date: August 1, 2022 at 05:22:21 GMT+3 > To: [email protected] > Cc: Fisher Yu <[email protected]>, Sharon Barkai <[email protected]>, Bruno > Fernandez-Ruiz <[email protected]> > Subject: Review of the LISP-NEXGON draft > > > Dear [email protected], > > This is a review of the draft available at > https://datatracker.ietf.org/doc/html/draft-ietf-lisp-nexagon by Prof. Trevor > Darrell of UC Berkeley and Prof. Fisher Yu of ETH Zurich, founders of the > Berkeley Deep Drive Consortium (BDD; https://bdd-data.berkeley.edu/) and the > largest academic driving dataset, BDD100K (https://bdd100k.com). > > Professors Darrell and Yu are leading researchers in AI, Computer Vision, and > Autonomous Driving, and have pioneered open-source frameworks and datasets > for autonomous driving research. Darrell has been in the field for over > three decades, founded the UC Berkeley BAIR and BDD centers, and is the > second most highly cited scholar in autonomous driving and the ninth-most in > computer vision according to Google Scholar. Yu is a leading researcher of > his generation in the area of perception for autonomous vehicles and was > recently hired as a tenure-track Assistant Professor at ETH after completing > a Postdoc at UC Berkeley, where he led the development of deep learning > models for autonomous driving and oversaw the collection of the BDD100K > dataset, which has been widely adopted in industry and academia. > > The draft describes network aggregation of detections made by vehicles with > AI cameras driving at speeds of between 0 to 50 meters per second. Detections > are marked, enumerated, and localized by the vehicle, and are snapped to a > geospatial grid tile based on the vehicle position and geo-perspective > calculation. The enumeration and localization specified by the draft are > feasible with a reasonable onboard vehicle computer and are consistent with > current research results from our labs at UC Berkeley and ETH Zurich. > > Detections from each area are aggregated in algorithmically (location) > addressable shards. > > A consolidation process is applied to merge multiple detections from multiple > points of view, varying time-stamps, and varying detection and localization > errors. The consolidation process emerges the current state - enumeration of > the condition of each grid tile aggregated by the shard. Both condition > enumeration, data-clustering, and consolidation processing applied on network > edge computers are aligned with BDD research. > > The formal geospatial grid used for localization and consolidated aggregation > is the H3geo.org hierarchical hexagonal grid, as it provides for clear tile > adjacency of the grid in each resolution level. This is a useful quality in > calculating perspective, propagating impact of conditions, and resolving > shard border-line detections. We believe these design decisions are > reasonable. > > We understood the detection aggregation network is based on IETF LISP RFCs to > provide: > > (1) seamless (to vehicles) edge compute expansion-contraction of per street > activity > (2) geo privacy, preventing unwarranted vehicle tracking by geolocation > services > (3) seamless context switching crossing shards while driving, without DNS > disruption > (4) service and subscription continuity when switching carriers/wlan while > driving > (5) mobile queuing, and metro ethernet edge route coalescing: M Mbps X Few > 100GE > (6) replication of push notifications, network join: Vehicles X Situations X > Locations > > Therefore we believe that [email protected] is the appropriate review venue for > this draft. Please do not hesitate to contact us for further discussion of > this important topic. > > Kind Regards, > > Profs. Darrell and Yu > [email protected] > [email protected] >
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