Dear Professors Darrell and Yu,

> 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.

We are honored you have put effort into making LISP better for your use-case. 
We really appreciate the feedback.

> 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. 

That is good to know.

> 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.

One question, what if two detections, roughly at the same time, report 
different visualizations? Is there a policy, such that if one detected nothing 
and another detected an object, that you err on choosing there was an object 
present?

> 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.

Yes, Sharon presented this to the WG many times. We were convinced.  ;-)

> 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

Yes, the mapping system can map from an EID that describes different 
resolutions.

> (2) geo privacy,  preventing unwarranted vehicle tracking by geolocation 
> services

Yes, again, the mapping system provides this service. It is centralized for 
management and also distributed for scale.

> (3) seamless context switching crossing shards while driving, without DNS 
> disruption  

Yes, we call that EID-mobility.

> (4) service and subscription continuity when switching carriers/wlan while 
> driving

Yes, the mapping system was extended from initial design to provide a PubSub 
capability and we are now using it for many of the use-case designs.

> (5) mobile queuing, and metro ethernet edge route coalescing: M Mbps X Few 
> 100GE

Right, EIDs can be aggregated when they are encoded as a power-of-2 address.

> (6) replication of push notifications, network join: Vehicles X Situations X 
> Locations

Yes, PubSub.

> 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.

That is great news. We will make sure we contact you if we need any questions 
answered about the use-case. But Sharon is very fluent with the use-case so he 
answers most of our questions.

Cheers and thanks again,
Dino

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