[lisp] Fwd: Review of the LISP-NEXGON draft

[Sharon Barkai <[e-mail]>]

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: [phone]

Begin forwarded message:

> From: Trevor Darrell <[e-mail]>
> Date: August 1, 2022 at 05:22:21 GMT+3
> To: [e-mail]
> Cc: Fisher Yu <[e-mail]>, Sharon Barkai <[e-mail]>, Bruno Fernandez-Ruiz <[e-mail]>
> Subject: Review of the LISP-NEXGON draft
> 
> 
> Dear [e-mail],
> 
> 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 [e-mail] 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
> [e-mail]
> [e-mail]
>