FSD v12: Tesla's Autonomous Driving Game-Changer w/ James Douma (Ep. 757)

hey it's Dave welcome today I'm joined

by James dama and we've got a whole host

of things to talk about we've got um

Tesla's FSD V12 release that just

happened this past month we've got um

Optimus to talk about um and this robot

taxy reveal in August so anyway it's

been a long time it's been like at least

a half a year was last August or

something like that so yeah yeah I

remember the last time we met we talked

about V12 cuz they did a demo mhm and um

we were quite excited about the

potential but also a little bit cautious

in terms of how it will first roll out

and how capable but um curious just what

has been your first experiences and

first impressions of you talk how long

have you been driving it for uh I got it

a few Sundays back I think I I got it

the first weekend that it really went

right so I think I've had it three weeks

or something like that maybe four three

probably and uh of course drove it out

here to Austin from Los Angeles drove it

quite a bit in Los Angeles on the way

out here so my my wife has this hobby of

like visiting supercharges we've never

been to so every cross country trip

turns it's ends up being way longer than

otherwise would be but one of the cool

things about that on the FSD checkout to

her is that we end up driving around all

the cities on the way you know because

you're driving around to the different

Chargers and stuff and so you get a

chance to see what it's like in you know

this town or that town or um different

you know highways are different we drive

a lot of rural areas so I got lots of

rural we uh we did like the whole back

Country tour coming out here through

across Texas and so feel like it was it

was a good experience for like trying to

compress a whole lot of FSD yeah and I

got to say I'm just like really

impressed like it's I was not expecting

it to be this good because it's a really

like this is not a small change to the

planner was yeah with v11 we had gotten

to a point where the perception stack

was good enough that we just weren't

seeing perception failures I mean they

just but people almost all the

complaints people had had to do with

planning not getting in the right lane

not being able to move far enough over

um not knowing when it was its turn uh

stopping in the wrong place creeping the

wrong way these are all planning

elements they're not uh you know so if

you're going to take a planning stack

that you've been working on for years

you've really invested a lot and you

like literally throwing it away like

there just not retaining any at least

that's what they tell us they got rid of

300K lines they went end to end it's

harder to actually mix heuristics into

end to end so it makes sense that they

actually got rid of almost everything

anything they have in there that's

heuristic now would be built new from

scratch for the end to end stack and yet

they managed to

outdo in what seems to me like a really

short because they weren't just

developing this they were developing the

way to develop it you know they were

having to figure out what would work

there's all of these layers of stuff

that they had to do so my you know my

expectation was that the first version

that we were going to see was going to

be like on par it would have some

improvements it would have a couple of

meaningful regressions and there would

they would be facing some challenges

with you know figuring out how to

address so because it makes sense that

they want to get it out soon and the

sooner they get it out into the fleet

the faster they learn um but the the

degree of polish on this was yeah in a

much higher than I expected and like you

know Bradford stopped by and I got a

chance to see 1221 as he was coming

through we only had about 40 minutes

together I think I it was just like the

spur of the moment thing and uh and yet

even in because he was kind enough to to

take it places that I knew well that I

had driven on 11 a lot and I think it

took me about three blocks to realize

like right away and after 45 minutes I

just knew that this is going to be

completely different and every

everything that I've experienced since

getting it and

I you know what have I got I'm I must be

at like 50 hours in the seat with it

right now a few thousand miles highly

varied stuff yeah it's super solid yeah

yeah I think um yeah I wanted to dive

into kind of how big of a jump this fsd2

is because when I drove it I was shocked

um because this is not like a is I think

V12 is a little bit of a misnomer

because this is a drastic you know

rewrite of their whole planning

architecture approach different

different um I mean on their perception

it seems like they probably kept a lot

of their neuron Nets um in terms of the

perception stack added on as well but in

their planning stack this is where they

pretty much it seemed like they're

starting from I would say scratch

completely but they're taking out all of

the guard rails all their hortic and

they're taking putting on this n10

neural approach where it's deciding

where and how to navigate right the the

perceived environment but I would have

imagined and this is kind of my

expectation also is like you you would

be better in some ways it would be more

natural Etc but then there would be some

just like weird mistakes or things that

it just doesn't get because all of the

guard rails are off theistic ones and so

you're just like it's D more dangerous

than some other ways right and that on

par though Tesla would wait until it

would be a little more safer before

releasing V12 but what we ended up

getting was we got this V12 that just

seems like really polished you know

we're not it's not easy to catch those

big mistakes in V12 and I'm kind of like

where did all these big mistakes go like

you know that was my expectation at

least and so I'm wondering like like

what was your did that catch you off

guard like just seeing the the the small

number you know of of big mistakes or

seeing how polish this V12 is um and

then I also wanted to go into like how

did Tesla do that in terms of um because

once you take off the heris sixs at

guardrails you really have to

like like be confident you need I don't

know like yeah I'm curious to hear

what's your take on how you think they

achieve this with B12 you know the the

the the Polish they have well first yeah

it

was well there's two components of like

starting out experience there's like my

sort of abstract understanding of the

system and what I sort of rationally

expected and then there's you know

there's my gut you know because I've got

I've got like 200,000 miles on various

layers of autopilot including you know

maybe I don't know 50,000 miles on FSD

so I have this muscle memory and this

you know sort of sense of the thing and

I expected that to sort of be dislocated

I mean you know going from 10 to 11 and

was also I mean they added a lot this is

not the first time that they've made

pretty substantive changes it's the

biggest change for sure right but I was

expecting it to feel a little bit weird

and uncomfortable but but sort of

intellectually I was expecting all the

old problems to go away and a new set of

problems to come in because it's a

different product

like because the perception was pretty

polished and and the things that people

were most aware of is failings of the

system were essentially baked into this

heuristic code well of course you take

theistic code away all those failings go

away too but what do you get with the

new thing right so and you know so that

did happen like all the old failings

went away like rationally right but it

was weird to sit in the SE in the seat

and you know there you know there's this

street you've driven over and over and

over again where there was this

characteristic behavior that it had

which is you know maybe not terrible but

not comfortable maybe or less ideal than

you would are slower annoying whatever

the deal and those are just gone like

all of them not just like one or two

they're just like gone all of them so

that was sort of like it was such a big

disconnect that it was kind of

disquieting the first you know week or

two I mean delightful but also

disquieting because now you're like

Uncharted Territory you know what demons

are looking here that I'm not prepared

to

you know after you drive theistic thing

for all you kind of got a sense of the

character of the failures I mean even if

you haven't seen it before you know the

kind of thing that's not going to work

and now but I didn't I didn't really

find those like I haven't really found I

haven't seen something and I was

expecting to see a couple of things that

were kind of worrisome and where I

wasn't clear to me how they were going

to get go about addressing them and I

just I really haven't right and so like

in that sense I'm really I'm more

optimistic about it than I expected to

be at this point um how do they do it

yeah okay so let me give context to that

question a bit more because I know it

could be open-ended so I would imagine

that if you go end to end with planning

that um driving is is very high stakes

you have one mistake let's say you go

into the center divider aisle or there's

a there's a concrete wall or you there's

a signpost you drive into or a treat or

something it just seems like you have

one second of mistake or even Split

Second and your car is you know it's

just catastrophic it could be and with

V1 up until v11 you had these guard

rails of like oh stay in the lane and do

this and all that stuff but with those

guard rails off like V12 could when it's

confused just make a bad move you know

and just go into some you know another

car another Lane another you know object

or something but what about it is

preventing it you know without the

guardrails is it just the data of

mimicking humans or is there something

else attached on top of that where

they're actually doing some simulation

or stuff where it's showing like what

happens when you go out of the lane into

the next Lane you know into oncoming

traffic or if you do something like is

it is are they you know pumping the the

the the neuron nest with lots of

examples of bad things also that could

happen if you know if it doesn't you

know follow a certain path like what's

your take on

that um so that question prompts a

couple of thoughts um so one

thought are okay first of all preface at

all like I don't know what the nuts and

bolts of how they are tuning the system

they've told us it's end to end right so

that basically constrains the things

that they could be doing but when you

train in a system you can you don't have

to train it end to end I mean some

training will be done endend end but you

can break it into blocks and you can

pre-train blocks in certain ways and we

know that they can use simulation we

know that they can curate the data set

um so there're you know what's the mix

of stuff that they're doing is really

hard to predict they're going to be a

bunch of you know uh learned methods for

things that work well that are going to

be really hard to predict externally

just from first principles um this whole

field it's super empirical one thing

that we keep learning about neural

networks even like the language models

we can talk about those some if you want

to cuz that's also super exciting but

the they keep surprising us right like

so you take somebody who knows the field

pretty well and you at one point and

they make predictions about what's going

to be the best way to do this and

whatnot and aside from some really basic

things I mean there's some things are

just kind of P prohibited by basic

information Theory right but when you

start getting into the Nuance of oh will

this way of tweaking the system work

better than that way or if I scale it if

I make this part bigger and that part

smaller will that be a win or a lot you

know there's so many small decisions and

the training is like that too like how

do you curate the data set like what in

particular matters what makes data good

like that's a surprisingly subtle thing

we know that good data like some

training sets get you to a good result

much faster than other training sets do

and we have theories about what makes

one good and what makes one bad and

people on some kinds of things like text

databases a lot of work has been done

trying to figure this out and we have

some ideas but at the end the day this

is super empirical and we don't really

have good theory behind it so for me to

kind of sit here not having seen what

they have going on in the back room and

guess I'm just guessing so just like

frankly like I have ideas about what

they could be

doing um but you know I would expect

them to have many clever things that

never would have occurred to me yeah

that they've discovered are important

and they may be doubling down and we we

actually don't know the fundamental

mechanism of like how they're going

about doing the mimicry like what degree

of we you know we know that the you know

they have told us that the final thing

is photons in controls out as end to end

would be right

but uh so the the final architecture but

like how you get to the result of the

behavior that you want you're going to

break the system down

like I don't know it's it's just like

there are many possibilities that are

credible picking them and they vary a

lot and picking the one that's going to

be the best like that's a hard thing to

do sitting in a chair not knowing um

they are doing it really clearly and

they're getting it to work like the

reason why I I it fascinates me on the

on what type of like um uh kind of

catastrophic scenarios or dangerous

things that there may be putting in like

it it the reason why it fascinates me is

because with driving part of the driving

intelligence is knowing that if your car

is like one foot into this Lane and it's

oncoming traffic that that's really

really bad like you know be a huge

accent versus if there's um no cars or

something then it's okay or if there's

or just it the driving intelligence just

requires an awareness of how serious

mistakes are in different situations in

some situations they're really really

bad in some situations the same driving

maneuver is not that dangerous and so it

just seems to me like there have to be

some way to train that right to teach

the the neuronist that so there's an

interesting thing about the driving

system that we have and

people okay first so the failure you're

describing is much more likely with

heuristics like heuristics you build

this logical framework a set of rules

right where um you know when heuristic

Frameworks break they break big like

they because you can get something

logically wrong and there's this gaping

hole this scenario that you didn't

imagine where the system does exactly

the opposite of what you intended

because you have some logical flaw in

the reasoning that got you to there

right so you know bugs that crash the

computer that take it out like we you

know computers generally don't fail

gracefully heuristic computers right

neural networks do tend to fail

gracefully so that's one thing right

they they they're less likely to crash

and they're more likely to give you a

slightly wrong answer or a you know to

get almost everything right and have one

thing be kind of wrong like that's a

more kind of characteristic thing so

neural networks

you know the way that they're going to

fail is going to be a little bit

different than heuristic code and

they're just by their nature they're

going to be somewhat less apt to that

kind of failure not that it's impossible

just that it's not going to be the

default automatic thing you know if you

get an if statement wrong in a piece of

code or something you you know

catastrophic failures are kind of the

norm in logical chains so um then

there's this other thing which is the

the system that we have is for is it's

Evol co-evolved with drivers you know

you uh you know you you learn you

develop reflexes you read the traffic

you read the

environment um you know when the lane

gets narrow people slow down people sort

of have a set of reflexes that adapt to

an environment to try to maximize the

safety margin they have for what they're

doing you're when you're driving down a

row of parked cars if you have space you

move over to give your safe a little

more space um you know if you're coming

up on an intersection and you can't see

what's coming you may slow down you may

move over to give yourself more space to

see what like all of these unconscious

behaviors right and the road system has

been developed over a lot of years to

like take advantage of the strengths of

people and and minimize the weaknesses

of people right I mean the way this the

amount of space that we provide on roads

and the way that we shape our

intersection sight lines that kind of

stuff the rationale for how our our

traffic controls work and all that kind

of stuff is

uh it's evolved to the strengths and

weaknesses of human beings right so

human beings are constantly trying to

within certain margins maximize their

safety margin give themselves make

themselves more comfortable that they

understand what what's going on right so

and now we have a system that's

mimicking people right so like there are

funny things that the that the that the

car will do that that just really is

kind of underscore this like you know

you're in a line of cars and that they

suddenly slow down and you have a truck

in front of you so one like one of the

most natural things is people will pull

over a little to if they can't see to

see what's happening up there to help

them prepare for what might be happening

to give them more situational awareness

well you see the cars do this sometimes

the funny thing about the car is the car

the the car it like it's camera is in

the center so moving a little to the

left doesn't let the car see around the

car ahead of it right it still can't see

but it still mimics that action so

similarly coming up to an intersection

slowing down moving over you know

preparing yourself so essentially

there's this interesting characteristic

that you're going to get out of that is

it is it the is that the planning system

is going to mimic the margin you know

that do the little Preparatory things

that give you a little more margin a

little more situational awareness and

help you prepare give you a little more

time to react in case something happens

it's mimicking all those things now so

uh instead of the her istics having to

kind of be perfect instead what the

system is doing is it's learning to

mimic PE you know drivers who already

have all these reflexes and and and

behaviors in a really complicated

contextual environment so it's not like

we're not talking about four or five

behaviors you know we're talking about

four or five thousand behaviors the kind

of things that people were as drivers

were not even aware that we're doing

them and the car is mimicking that right

in the thing and so so so they're going

to fail more gracefully and they're

mimicking drivers who are you know who

are cautious in situations where they

need to be cautious and they're you know

they're they're making small adjustments

to give themselves more margin all the

time and I think we may have under

appreciated the degree to which you know

human drivers with a lot of experience

have

reflexively you know developed a lot of

behaviors that are actually because

we're talking about Good drivers here

right uh they've they've unconsciously

developed a lot of habits that actually

have a an appreciable impact on their

safety and and the system is now getting

those for free kind of because it's

mimicking drivers right even all the

little Nuance things that we that kind

of don't make sense like I said like

pulling over to see what's ahead of the

car uh ahead of you or we see the like

the the behavior where that the very

Charming Behavior where you know it

doesn't block the Box you come to an

intersection and if it's not clear that

it can get across it stops right like

nobody had to program that and if you

look at intersections like when to do

that and when to not do that that's kind

of subtle right like is the car ahead of

you going to move forward Enough by the

time you cross intersection or is it not

and if you look at the flow of traffic

like as a human you're like better than

even odds there will be space when I

cross or no I should definitely stop

here because I don't want to be caught

in the intersection the cars mimic all

that yeah even in really complicated

context I mean I would say I mean

mimicking it it seems like it goes even

a little beyond the mimicking at times I

think this is like the unch territory

which V12 surprises me is it mimics with

some level of understanding sometimes

like why it because for example you're

going you don't know whether to to go

into the intersection or not or let's

say you're you're turning into pedest

left turn into and pedestrians are here

every situation is a little bit

different and so just because in your

data you have a bunch of examples it's

like there it might not be the perfect

like you might not be able to mimic

perfectly because it's a new situation

so you've got to infer kind of in this

new situation what should I do and

that's where I think it's not just

mimicry it and it could be just mimicry

right now but the the the big I guess

jump in in ability is is is UN is it's

kind of like llms you know like they

they can understand to a certain extent

what you're asking for in a new

situation or a new you know dialogue I

think the word you're looking for is

General

yeah yeah yeah maybe generalize like

taking that the specific mimicry

situations that the data provides and

generalizing those but there's a certain

level in order to generalize that you do

need um capability Beyond just mimicry

right some level of of maybe application

or so mimicry I mean we talk about

mimicry mimicry is the training goal

right do what a human would do in this

situation that's why we call it mimicry

right but

the system it doesn't have the capacity

to record every single possibility right

and so it's frequently going to see a

situation that's kind of a combination

of situations it's seen before it's not

a duplicate of any of them and it h and

you have to kind of figure out how to

combine what you learned in these other

situations that were similar but come up

with something that's different and yet

somehow it follows the same rules so a

way you could think about it is that the

using the block the Box Thing depending

on how many lanes of traffic there are

and how aggressive the drivers are and

what the weather is like what the cross

traffic is like you know just all of

these variables you you you as a human

you come up to the intersection you have

to make the decision whether you're

going to cross and maybe get stuck or

whether you're going to you're going to

pause and wait for the other car to move

up you know I saw one i' I've seen one

where where I had the the block box and

you could see the light at the end of

the row of cars right and like this is

the thing humans do when this light

turns red you know you have plenty of

time to cross because it's not going to

turn GRE you're not going to get stuck

and you see the next light up there turn

green well even if you get stick in the

Box it doesn't matter I was been in that

situation twice now and the car moved

into the intersection even though it

would block it because it's confident

that the row of cars well who coded

nobody coded that right there's now as a

human I'm describing this thing well

here's a rule I just made up if this

light has just turned red you know there

will be no cross traffic and the light

ahead turns green while the car is ahead

they're definitely going to move forward

almost certainly right unless there's a

broken down car or something like that

and so you see humans do this they move

up because they know they're going to be

able to and they want to all they want