[lisp] Review of the LISP-NEXGON draft

[Trevor Darrell <[e-mail]>]

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]