[0:11] [Music]
[0:19] Hello everyone and welcome to Business
[0:21] Insiders inside self-driving the
[0:24] AIdriven evolution of autonomous
[0:27] vehicles presented by Mobile Eye. I'm
[0:29] Steve Russell, chief news editor here at
[0:32] BI. And today we're diving into one of
[0:34] the most transformative and debated
[0:37] frontiers in technology. How AI is
[0:40] turning autonomous mobility from a long
[0:42] promised dream into a fast approaching
[0:45] reality. We'll explore how automakers,
[0:48] tech innovators, and policy makers are
[0:50] working together to make autonomy safe,
[0:53] scalable, and trusted, and what that
[0:56] means for businesses, cities, and all of
[0:58] us who share the road together. First,
[1:01] we're starting today with a conversation
[1:03] presented by our sponsor, Mobile Eye,
[1:05] that goes inside the company's
[1:07] collaboration with Lyft as they work to
[1:09] bring driverless technology to scale.
[1:17] [Music]
[1:22] Thank you, Steve. I'm Dr. Deborah
[1:25] Bervishes and I'm happy to be here. Robo
[1:28] taxis already operate in a few cities,
[1:31] but taking autonomous vehicles
[1:33] mainstream comes down to three things:
[1:36] auditable safety, consumer trust, and
[1:39] economics that work. I'm joined here by
[1:42] JJ Youngworth, executive vice president
[1:45] of autonomous vehicles at Mobile Eye,
[1:48] and Stephen Hayes, VP of autonomous
[1:51] fleets and driver operations at Lyft.
[1:53] Thank you both for being here. Let's
[1:56] talk about trust and safety. It seems
[1:59] like one of the main goals of autonomous
[2:01] vehicle companies is to convince both
[2:03] the writers and the regulators that
[2:06] these kinds of vehicles are safe. So,
[2:09] Stephen, at Lyft, your role is to
[2:12] interact directly with a rider. What
[2:15] would they need to see or experience to
[2:17] feel comfortable using an autonomous
[2:19] vehicle?
[2:21] >> Great question. Uh, at Lyft, our purpose
[2:23] is to serve and connect, and that's
[2:26] something that we do uh about 800
[2:28] million times a year, helping riders get
[2:30] to where they need to go. Uh, and over
[2:32] time, AVs are going to make up a bigger
[2:34] and bigger percentage of those trips on
[2:37] our platform. And uh if you are tuned
[2:40] into this broadcast, chances are you are
[2:42] a bit of a tech enthusiast and an early
[2:45] adopter. But the reality is for most of
[2:47] the people who open up the Lyft app on a
[2:49] daily basis, they're just looking to get
[2:50] to where they need to go. Uh and that's
[2:52] where uh it is our privilege and
[2:55] responsibility to introduce millions of
[2:57] new riders to exciting autonomous uh
[3:01] technology. And in order to do that
[3:03] effectively, we need to find the right
[3:05] autonomous trip for the right rider at
[3:07] the right time. And then once they come
[3:09] in, uh we need to educate them about the
[3:12] experience that they're going to have.
[3:13] Uh and make sure it's a delightful one.
[3:15] And all the while, what is going to be
[3:18] really important for us is making sure
[3:20] that we have happy uh repeat customers
[3:22] who are getting where they need to go
[3:24] even more quickly and efficiently than
[3:26] they are today.
[3:27] >> Awesome. JJ, from your perspective at
[3:30] Mobile Eye, which three pieces of proof
[3:33] would you hand to a regulator to prove
[3:35] that autonomous vehicle technology is
[3:38] ready and safe?
[3:41] >> Yes. So, uh, of course, number one is is
[3:43] safety as you just mentioned. Um, and
[3:45] there are different metrics, different
[3:46] KPIs, uh, on on how, uh, safety is
[3:50] measured. Um, one is, you know, to look
[3:52] at, uh, the crash rates and there of
[3:54] course the goal is to be safer than
[3:56] human drivers. You know, we believe that
[3:58] eventually the technology will support
[4:00] to be 10x safer, maybe 100x safer. Um,
[4:04] and uh, you know, the technology
[4:05] basically never sleeps. Uh, it has eyes
[4:08] all around the vehicles. It can react in
[4:10] milliseconds. It doesn't have, you know,
[4:12] a second reaction time like like human
[4:14] drivers. It can see better at night uh,
[4:16] with all the sensors technologies and
[4:18] uh, redundancies. And uh, then of
[4:21] course, you know, looking at let's say
[4:23] cities, customers. I mean it's also very
[4:25] important that these vehicles you know
[4:27] are fitting into regular traffic. Uh you
[4:30] don't need you know special lanes,
[4:31] special infrastructure. Uh but you know
[4:34] regular like a regular you know human
[4:36] driver fitting in there also not being
[4:37] too slow having a certain assertiveness
[4:40] and uh then of course trust it's very
[4:42] important uh for both for riders as well
[4:45] as for you know cities and operators and
[4:48] uh companies like Lyft uh who are
[4:50] offering the services.
[4:52] I like your answer because it reminds me
[4:55] that it's not only safety that's
[4:57] important, but the vehicle also needs to
[4:59] operate in an assertive enough way that
[5:01] it inspires confidence with the writer
[5:03] that it's going to get them from point A
[5:05] to B. Uh maybe Stephen, can you comment
[5:08] on this?
[5:10] >> Yeah. Uh I think it's such an important
[5:13] and underappreciated aspect of taking
[5:17] autonomous vehicles from uh the
[5:20] prototype phase to scaled commercial
[5:22] deployments is the ride experience and
[5:24] the ride feel of the autonomous vehicle
[5:27] itself because the vehicle could from a
[5:29] technical perspective be uh incredibly
[5:32] safe. But as JJ mentioned, if it is so
[5:36] cautious that you end up waiting three
[5:38] or four different light cycles to take
[5:40] an unprotected lefthand turn, you're
[5:43] going to end up with a lot of riders who
[5:45] are like, "Well, that was that was kind
[5:46] of cool, but this is not the way that I
[5:48] I'm going to get choose to get around on
[5:50] a day-to-day basis." And so the ride
[5:53] feel and being able to kind of fine-tune
[5:56] uh the the ride experience to make sure
[5:58] that it gets you where you need to go in
[6:00] the right amount of time is going to be
[6:02] really important because today uh human
[6:04] driver trips tend to be a little bit
[6:06] shorter uh from a a trip duration
[6:08] standpoint uh than AVs. Uh so it'll be
[6:11] really interesting to see uh AV
[6:14] companies like Mobilei continuing to
[6:16] kind of like move the dial on what the
[6:18] uh ride experience and feel uh of the AV
[6:22] after you know continuing to master all
[6:24] of the fundamentals of the autonomous
[6:25] driving because the the style is is
[6:27] actually very important.
[6:29] >> Sure. Yeah. It's fascinating how it
[6:31] works. So let's move on to another topic
[6:34] about economic e the economics of scale.
[6:37] We know that we can already hail a
[6:39] driverless taxi in cities like San
[6:41] Francisco, Austin, and Phoenix, but for
[6:44] most of the country, autonomous vehicles
[6:47] still feels like maybe five years away.
[6:50] What are the make or break economic
[6:52] realities of turning a test program into
[6:55] a viable business? And why has it been
[6:58] so hard to make self-driving mainstream
[7:00] everywhere?
[7:04] >> I can jump in and take a take a stab at
[7:06] that. and then I'll hand it over to JJ
[7:08] who is definitely in the best position
[7:10] to speak to the the engineering of what
[7:12] makes it hard to build a self-driving
[7:14] vehicle. Um you know the the reality you
[7:17] said Deborah is there are there are
[7:18] cities around the country where you see
[7:20] autonomous vehicles. Some of them are
[7:22] commercially deployed, some of them are
[7:24] in testing, but that we should all
[7:26] remember represents the very tip of the
[7:29] iceberg. And underneath that deployed
[7:32] asset, there is an entire value chain uh
[7:36] of different partners and ecosystem of
[7:39] players that needs to be marching in
[7:41] lock step in order to support the
[7:43] commercialization of that asset. And I
[7:46] think this is really important. So just
[7:47] want to unpack it for for a moment. uh
[7:50] uh that value chain starts with
[7:52] companies like Mobilei which are
[7:53] building the self-driving technology. It
[7:56] spans to OEMs, the auto manufacturers
[7:59] who are building and producing the
[8:00] vehicles. And today uh we're generally
[8:03] taking uh retrofitted vehicles. Uh so
[8:06] they're not they're not built for
[8:07] autonomous specifically and that means
[8:09] you need to kind of like tear them apart
[8:11] and then put them back together with the
[8:12] tech stack on it and that has
[8:13] significant implications from a cost and
[8:15] scale standpoint. And then once you have
[8:17] the vehicle and you have the AV stack on
[8:19] it, then you need a fleet manager and an
[8:22] operator and a financing partner who's
[8:24] going to hold that vehicle. Uh, and then
[8:27] you need a mobility marketplace where
[8:29] you can deploy that asset and
[8:30] commercialize it. And you need a
[8:32] front-end customer experience, a way
[8:34] that riders can interact with your
[8:36] technology. And uh in order to go from
[8:40] hundreds to thousands of vehicles,
[8:42] again, you really need all the different
[8:44] component parts of that value chain
[8:46] coming together uh in order to uh
[8:49] achieve sustainable economics. And uh I
[8:52] think that's where the the industry has
[8:54] a a growing appreciation for the
[8:56] complexity of doing that. Uh and that's
[8:58] where our partnership with Mobile Eye,
[9:00] the fact that Lyft owns and operates a
[9:02] subsidiary called Flex Drive. We 15,000
[9:05] vehicles that we directly manage today
[9:07] and obviously a thriving marketplace.
[9:08] These are all really important
[9:10] ingredients
[9:12] >> that is
[9:12] >> yeah maybe to add to that just just
[9:14] quickly um um is you know from a
[9:17] technical side I think you know in order
[9:19] to scale it's very important to have you
[9:22] know a product uh that uh basically has
[9:25] you know high efficiency and also is
[9:27] built from a cost perspective and u
[9:30] actually from an overall technical
[9:32] approach uh in a way that you can
[9:34] actually go quickly from city to city
[9:35] because this is something you know where
[9:37] we look back the last five years uh the
[9:40] deployment rate you know has been very
[9:41] slow. We are still not at the beginning
[9:43] of the actual scaling phase and you know
[9:45] for us as mobile you know we always say
[9:47] like safety is first and then
[9:49] scalability second and efficiency third.
[9:52] >> Yeah I love that. I mean from what
[9:54] you're saying it's pretty obvious that
[9:56] no single company is going to scale
[9:58] autonomous vehicles alone but
[10:00] collaboration in this in industry is
[10:02] notoriously tough. So can you talk about
[10:06] like a gridlock or or or a bottleneck in
[10:09] operational uh work between the OEMs and
[10:13] the mobility platforms and the provider
[10:15] that you feel is slowing down progress.
[10:19] >> So um based on my experience now and
[10:22] I've been in this space now also since
[10:24] uh you know about 15 years um this is
[10:27] actually not the case. I mean yes there
[10:28] is competition between you know
[10:30] automakers uh and there's competition
[10:32] between the technology providers and so
[10:34] on. So looking at these, you know, four
[10:36] or five like value chain layers uh that
[10:39] Stephen just explained basically um
[10:43] there's a lot of collaboration and and
[10:45] you know it's also there are different
[10:47] business models you know you look at you
[10:48] know maybe Whimo an all-in kind of more
[10:50] vertical type of approach and and look
[10:53] at our you know approach and
[10:54] partnerships uh for example with Lyft um
[10:57] and and with you know Folkswagen as our
[10:59] main strategic partner for uh uh you
[11:03] know the first VW ID as you know
[11:04] platform vehicle, beautiful vehicle also
[11:07] nicely integrated technology from from
[11:09] our side and uh also with others. So we
[11:12] try to be very open in this regards to
[11:14] actually work with different platform
[11:16] providers on the vehicle side but also
[11:18] on the go to market side and we think
[11:20] this is the better approach this open
[11:22] approach
[11:24] >> and I I I'll just chime in and agree
[11:26] with uh JJ's sentiment that I think
[11:29] whereas in the very early stages you saw
[11:32] some players say hey we need to
[11:33] vertically integrate and own the whole
[11:35] thing ourselves and both from a
[11:38] complexity standpoint as well as the
[11:40] growing recog recognition that the
[11:42] market opportunity here is enormous and
[11:45] we are in the bottom of the first
[11:47] inning. Uh I think more and more players
[11:50] are realizing that we can go further by
[11:53] partnering together uh and finding
[11:55] partners that have very complimentary
[11:57] skill sets.
[11:59] >> Great. Talk to me about the future.
[12:02] What's the next milestone that's going
[12:04] to really matter in the next two years?
[12:09] I'll answer this from a from a customer
[12:11] perspective and I'd love to hear JJ's uh
[12:13] perspective from a technical one. Um the
[12:16] metric that we at Lyft are going to be
[12:19] obsessing over for the next uh two years
[12:21] and beyond with uh our autonomous
[12:24] partners is the percentage of riders who
[12:28] opt in after taking uh an autonomous
[12:30] trip into the next autonomous trip.
[12:33] Because again, going back to what I said
[12:34] in the beginning, for a lot of people,
[12:36] AVs just aren't on their radar or it's a
[12:39] bit of a novelty. It's like, yeah, I'm
[12:41] coming to San Francisco. I want to try
[12:42] this new experience. It's kind of like
[12:44] going on a a new theme park ride at
[12:46] Disneyland. And then there are people
[12:49] who uh are are getting habituated to
[12:51] taking AVs, and we want more and more
[12:53] people in that category. And so you only
[12:55] have one chance to make a first
[12:57] impression. And we want to make sure
[12:59] that we are delighting writers in
[13:01] setting appropriate expectations and
[13:03] that they're giving that back to us by
[13:04] saying, "Yeah, I will take Navy with you
[13:06] again."
[13:08] >> And um I I personally, you know, I'm I'm
[13:11] very interested in and you know, looking
[13:13] forward to, you know, kind of this dual
[13:15] path, you know, of bringing this
[13:16] technology to market on the one hand
[13:18] with fleets. I mean this is kind of
[13:20] natural because the technology is you
[13:22] know pretty expensive at the moment. But
[13:25] then the second path was consumer
[13:26] vehicles. uh basically you know letting
[13:29] people um own or lease such vehicles
[13:32] maybe they even put them into fleets
[13:33] when they don't use them themselves but
[13:35] basically you know having consumer AVs
[13:37] and then fleet AVs um and and you know
[13:40] looking at you know which type of
[13:41] services what type of vehicle you know
[13:43] interiors exteriors I mean there's going
[13:45] to be a lot of innovation uh in in the
[13:48] in the vehicle design you know maybe
[13:49] there will be even collaboration between
[13:51] automakers and you know design studios
[13:54] or furniture companies or you you know,
[13:57] uh, interior designers to come up with
[13:59] completely, uh, new new designs,
[14:01] partnerships. You know, you can have an
[14:03] office on wheels, you can have a lounge
[14:04] on wheels, you can have, you know, movie
[14:07] theater on wheels, anything you want.
[14:08] And maybe, you know, depending on your
[14:10] needs and and once uh you order, you
[14:13] know, this or that type of uh vehicle or
[14:15] or rider service.
[14:17] >> I love the office on wheels. Okay, so
[14:20] this is a short question for both of
[14:22] you. What is one myth about autonomous
[14:26] vehicle safety that you want retired?
[14:32] >> I'll go. Uh I think the myth about
[14:34] autonomous vehicle safety is that safety
[14:37] is enough because safety is critical.
[14:40] It's necessary and it's not efficient.
[14:42] As we talked about earlier, if riders
[14:44] feel like the ride is jerky or it's too
[14:46] cautious, they're not going to be future
[14:48] customers of it. And so of course we
[14:51] need safety and we need a delightful
[14:54] experience around it.
[14:56] I personally actually think that uh one
[14:59] of those myth is that you know let's say
[15:01] as regular pedestrians or u you know
[15:04] maybe uh um another vehicle you know
[15:07] driver uh that you might act uh
[15:10] differently you know in front of an AV
[15:12] that people think oh it's an AV I can
[15:14] just you know jump in front of it you
[15:16] know even at you know two feet or three
[15:19] feet I mean basically you know people
[15:21] still need to consider you know the
[15:22] physical limits of you know breaking
[15:24] distance and uh uh and and uh you know
[15:28] reaction and so on. So I think uh you
[15:31] know from that perspective and I know
[15:32] there's you know there are discussions
[15:34] about you know should AVs have this you
[15:36] know light blue light you know when they
[15:37] are active so that people can see
[15:39] pedestrians and so on. Oh this is an AV
[15:41] or an AV function is on. I have to say
[15:43] I'm kind of against this and and uh
[15:45] believe that it's better that people
[15:47] have respect uh you know for any type of
[15:50] vehicles uh and any type of sizes uh
[15:53] because at the end of the day um these
[15:55] uh uh vehicles can only let's say react
[15:58] and break a certain uh let's say with a
[16:00] certain momentum and and brake power and
[16:02] and and so on you know basically just
[16:04] physical um u limitations and I think
[16:07] it's important uh that people you know
[16:10] treat AVs the same way as you know
[16:12] human-driven vehicles just, you know,
[16:14] safer and better.
[16:16] >> Yeah, it sounds like there's a lot to do
[16:18] to educate the market about this new
[16:20] technology. So, thank you JJ and Stephen
[16:23] for the great discussion. I really
[16:25] enjoyed it. It's fascinating to see how
[16:27] technology, trust, and collaboration are
[16:30] shaping the next chapter of mobility.
[16:33] Steve, back to you.
[16:36] >> Thank you, Deborah, JJ, and Stephen.
[16:38] That was a fascinating look at what
[16:40] comes next for autonomous fleets and
[16:42] innovation. Now, we're shifting gears to
[16:45] zoom out and look at the bigger picture.
[16:47] How cities, automakers, and regulators
[16:50] are shaping the infrastructure and the
[16:52] mindset needed to make autonomy work for
[16:55] everyday people.
[16:59] [Music]
[17:07] I'm thrilled to be joined by two leaders
[17:09] in this space. James Philin is the vice
[17:12] president of autonomy and AI at Rivian.
[17:15] And before joining the automaker, James
[17:17] spent years at the forefront of
[17:19] autonomous vehicle development, leading
[17:21] software and perception teams at both
[17:23] Whimo and Zuks, where he helped advance
[17:26] the systems that allow self-driving cars
[17:28] to see and understand the world around
[17:31] them. Now, he's building technology that
[17:33] makes advanced driver assistance and
[17:35] autonomy integral to Rivian vehicles.
[17:38] We're also joined by Charlie Tyson, who
[17:40] plays a key role in Michigan's
[17:42] autonomous vehicle pilot programs,
[17:44] advancing the state's efforts to turn
[17:46] transportation innovation into policy
[17:49] and infrastructure. He works at the
[17:51] intersection of government, industry,
[17:53] and research to make Michigan a national
[17:56] test bed for next generation mobility.
[17:58] James and Charlie, thank you both so
[18:00] much for being here.
[18:03] >> So, let's get right into it. is he
[18:06] >> let's talk about where we are the state
[18:08] of play today where we really are right
[18:10] now. How would you each describe the
[18:12] state of autonomous vehicle technology
[18:15] right now and specifically what's real
[18:18] what's still experimental and what's
[18:21] misunderstood. James, let's go to you
[18:23] first.
[18:25] >> Yeah, I mean I think you're starting to
[18:27] see the phase where um you know full
[18:29] autonomy has gone from the sort of
[18:30] science project into an actual product.
[18:32] Um, and you can go up to, you know, San
[18:34] Francisco, I can drive 40 miles north of
[18:36] me right now. And, you know, it was just
[18:39] going to be flooded with Whimos. Um, and
[18:41] now Zuks is as well. So, I'm I sort of
[18:44] um felt a few years ago that actually
[18:45] the the fundamental problems had been
[18:48] solved. That wasn't that doesn't mean
[18:49] that every every problem has been
[18:51] solved, but it was moving into a more
[18:52] engineering um and deployment and
[18:54] scalability phase.
[18:56] Um, and then I think, you know, with
[18:59] that hat on, you got to think how does
[19:01] this change how people um use
[19:04] transportation in the future. And I'm
[19:06] still a big believer in um personally
[19:08] owned vehicles. I think that for every
[19:11] mile done in a robo taxi, probably in
[19:13] the future, you know, more than 10 times
[19:15] will be done in personally owned
[19:17] vehicles. I think the economics of a
[19:19] robo taxi versus a personally owned
[19:20] vehicle still mean that those two modes
[19:23] will be around for a very long time. And
[19:25] so that's why I made the the leap to
[19:26] Rivian. Essentially, can we bring that
[19:29] sort of L4 technology back to the
[19:31] consumer space and really provide value
[19:32] for our customers.
[19:34] >> And Charlie, what do you think?
[19:37] >> Yeah, I think James hit the nail on the
[19:39] head there, but um from a state's
[19:41] perspective, I think that we are we are
[19:44] going from like the testing phase to
[19:47] some level of commercial operations, but
[19:49] I think we're seeing it in certain
[19:51] states. Um, for example, um, James
[19:53] mentioned California. We're seeing, um,
[19:56] I actually was just in Arizona. I took
[19:58] Whimos in Phoenix. Um, commercial
[20:01] operations really impressive. Um, but I
[20:03] think there are still some challenges.
[20:05] Um, the technology seems to be there for
[20:08] the most part. But um consumer consumer
[20:10] adoption, how does it how do these um
[20:13] techn how do these vehicles whether
[20:15] they're consumer AVs or um consumer
[20:18] vehicles with um some level of of uh
[20:22] autonomy or their um autonomous vehicle
[20:25] fleets? How do we integrate them into
[20:27] our existing transportation network? I
[20:29] think that's still a challenge that
[20:31] we're working on um here in Michigan and
[20:32] and really throughout the throughout the
[20:34] nation.
[20:35] >> You know, it's really interesting. I
[20:36] mean, I guess the one question I have
[20:38] for both of you is how do you get people
[20:40] to really feel comfortable about getting
[20:42] in an autonomous vehicle? I think about
[20:45] my parents who say that they'll never
[20:47] get in one, that they don't just don't
[20:49] trust the technology. And it seems like
[20:50] there's maybe a generational divide.
[20:52] Boomers feel one way, millennials,
[20:54] genzers. I'm curious how you all think
[20:57] about this trust issue, particularly
[20:59] from a generational perspective. And
[21:01] James, do you want to start first?
[21:04] Yeah, I mean my sense is it's pretty
[21:05] non-existent. I mean my my parents are
[21:07] also in that generation and um you know
[21:11] they're interested in what I'm doing but
[21:12] they don't really you know trust all
[21:14] these systems but you know the last time
[21:16] they visited we took a Whimo around um
[21:19] it's very you know very quickly becomes
[21:21] normal actually and um I feel like
[21:23] there's that initial kind of wow moment
[21:26] hesitation but then it's completely
[21:28] normalized people you know just go about
[21:30] their day. So, I think it's um I don't
[21:32] think the trust issue is is going to be
[21:34] um a persistent one. I also think that
[21:37] as people get more used to those higher
[21:40] levels of autonomy, they'll expect more
[21:41] autonomy in their vehicles. So, I think
[21:43] that trust issue actually trickles down
[21:45] and what you'll see is a huge um sort of
[21:47] competitive swing towards um you
[21:50] personally owned vehicles that offer
[21:51] those higher levels of autonomy. Um, so
[21:53] I think that's kind of a bit of the race
[21:55] you see right right now and one I think
[21:57] Rivian is, you know, perfectly poised to
[21:59] execute on.
[22:01] >> Do you agree, Charlie?
[22:02] >> Yeah. And I think Yeah, I agree with
[22:04] that totally. And I I think that, um, we
[22:07] just have to give individuals the the
[22:09] chance to experience the technology. Um
[22:12] that's why I think that although we want
[22:14] to see and we're pushing for commercial
[22:16] operations, um these pilot projects and
[22:19] these these testing activity that allows
[22:21] consumers to experience the technology
[22:23] in different use cases is still really
[22:25] critical. Um we've seen a number of
[22:28] pilot projects uh in Michigan um in some
[22:32] of our larger cities like Detroit of
[22:34] course, Grand Rapids, Ann Arbor. And the
[22:38] feedback from the surveys that we we
[22:40] sent out um was really interesting. Um
[22:43] uh most most of the the riders I think
[22:45] 90% of the riders in the survey
[22:48] respondents um you came back saying that
[22:50] they would absolutely love to take
[22:52] another um trip in an autonomous vehicle
[22:54] and they would recommend it to their
[22:55] their peers. Um they what was
[22:58] interesting though was um we asked a
[23:00] question in the survey um around
[23:03] removing the safety driver and I think
[23:05] that is a big piece here. That's where
[23:08] we started to see some of the um the
[23:10] comfort levels um change a little bit.
[23:12] Uh the survey respondents um I think it
[23:14] was about 5050, you know, responding
[23:18] that they would be willing to get get in
[23:19] an autonomous vehicle without a safety
[23:21] driver. So I think just um getting
[23:23] people more um accustomed with the
[23:26] technology um sharing the benefits um
[23:29] and also I think uh it's it's critical
[23:32] to um be able to you know educate the
[23:35] public um and just give them that
[23:37] experience to to get inside the vehicle
[23:40] um provide feedback uh and and let them
[23:43] know that these aren't being forced on
[23:45] them. They're they're being deployed in
[23:47] safe ways and and kind of a a crawl,
[23:49] walk, run approach. I think that's
[23:51] critical.
[23:52] >> Charlie, you mentioned these these
[23:53] pilots. Um, I guess my question is how
[23:56] how do you measure readiness? Is it, you
[23:59] know, the the number of miles driven? Is
[24:01] it disengagement rates? Is it just
[24:03] consumer trust or something else
[24:05] entirely? Like h how do we know that
[24:07] these things truly are ready to be out
[24:09] on the road?
[24:12] >> Yeah, I think um it's different for it's
[24:14] kind of case by case, different for each
[24:16] project. Um, for example, we had a
[24:18] project in Northern Michigan looking at
[24:21] um supporting a a a full-size autonomous
[24:24] transit bus deployed at Sleeping Bear
[24:27] Dunes National Park. So, looking at a a
[24:30] very unique use case in northern
[24:32] Michigan. Um, a majority of the of the
[24:35] riders were tourists visiting the area.
[24:37] they either uh they had challenges with
[24:40] with parking, but um I think uh it the
[24:44] way that we kind of measured read
[24:46] readiness for that project was how many
[24:47] times um the the autonomous vehicle um
[24:51] had to be essentially how many times the
[24:54] um safety driver had to take control of
[24:57] the vehicle. Um were there challenges
[24:59] with connectivity in that region due to
[25:02] to Wi-Fi or um you know infrastructure
[25:05] uh capabilities? Uh so I think that's
[25:07] the big thing is looking at
[25:08] disengagement um and just looking at
[25:11] based on the use case based on the
[25:12] region what are the key factors to
[25:14] enable um adoption and and that kind of
[25:16] is how we determine what are the key uh
[25:19] metrics around uh success.
[25:23] >> I'm curious
[25:23] >> yeah maybe I can just add to that please
[25:25] um yeah and just just talk about the
[25:27] Rivian process. So we we actually do an
[25:30] extensive kind of release readiness um
[25:33] process every month for our software and
[25:35] that involves you know many aspects to
[25:37] it the sort of metrics across the stack
[25:39] but also a key part of that is actually
[25:40] simulation. So we take millions of miles
[25:42] of real customer data and we can replay
[25:45] them through our stack and kind of
[25:47] measure the performance the safety the
[25:49] smoothness um and all those aspects and
[25:51] I think all of those pieces go into that
[25:53] readiness uh report. So I think that's a
[25:55] very important part is um having that
[25:58] scale of data and also the ability to to
[26:00] replay it and to to you know gain
[26:02] insights from it.
[26:03] >> Charlie, you mentioned in your survey
[26:05] data that you cited that there's a
[26:07] distinction between how people feel uh
[26:09] when they're in a self-driving car that
[26:12] does not have a driver versus one that
[26:14] does have a driver. And how do you sort
[26:16] of bridge the gap between people's
[26:18] perceptions on on those two experiences?
[26:23] Yeah, it's a great question. Um, I think
[26:26] it's understandable that someone may
[26:28] have um less willingness to get an
[26:32] autonomous vehicle without a safety
[26:33] driver. But I think that goes to the
[26:36] importance of getting autonomous fleets
[26:38] out there. For example, some of these
[26:40] states that have been doing it without a
[26:41] safety driver. Again, was just in a
[26:44] Whimo in Arizona. Um, and got out of the
[26:48] vehicle feeling totally safe. Um and I
[26:50] and so that kind of goes to um the
[26:52] importance of um states and and um
[26:56] industry working together, government
[26:58] and industry working together to safely
[26:59] deploy these vehicles and provide them
[27:01] and um you know provide the opportunity
[27:04] for the public to get in get in the
[27:06] vehicle. Um ideally we move from safety
[27:10] drivers to to non-safety drivers and
[27:12] fully autonomous vehicles. Um and that's
[27:14] what we're working on doing here in
[27:15] Michigan. But there are some challenges,
[27:16] right? And I think that's um why
[27:18] Michigan feels that we are um posi
[27:21] positioned well to be a kind of a great
[27:24] test bed to to move from from the
[27:27] testing phase to commercial operations.
[27:29] For example, how do we um ensure that
[27:32] autonomous vehicles can operate in in
[27:34] harsh weather conditions? In order to be
[27:36] able to fully um to see widescale
[27:38] adoption of AVs, we need them to be able
[27:40] to operate in in harsh weather
[27:42] conditions, rain, snow, etc. And so, um,
[27:45] there's certain states and and Michigan
[27:47] definitely feels we are one of them
[27:48] that, um, you not only due to our
[27:51] automotive heritage, but also, um, just
[27:54] our our demographics, our our weather
[27:56] conditions, um, we are positioned well
[27:58] to be able to to test these different,
[28:00] um, uses and to hopefully support, um,
[28:03] that path towards commercialization
[28:04] while we do so with, um, public
[28:07] engagement and and providing, you know,
[28:09] individuals the experience to, uh, to
[28:11] get in the vehicles. I always kind of go
[28:13] back to, you know, 10, 20 years ago, how
[28:16] many parents um were on social media?
[28:19] Not very many. I was always I was always
[28:20] getting flack from my parents for being
[28:22] on Facebook or or um you know, whatever
[28:25] in Instagram, for example. But, you
[28:27] know, now my grandma's on Facebook and
[28:29] she's she has a smartphone and and
[28:31] they're on it more than I am. So I think
[28:33] it just takes time for adoption and for
[28:35] for uh the public to get um accustomed
[28:38] to technology and feel comfortable in it
[28:40] and and I think industry is doing a
[28:42] great job and it's important for
[28:43] government to to align and to partner
[28:46] with industry to to make this path as
[28:48] smooth as possible.
[28:49] >> You know it's a great point you raised
[28:51] and just this week we obviously had the
[28:53] big Amazon AWS outage that knocked out
[28:56] wide parts of the internet and how
[28:58] people go about their everyday lives. To
[29:00] me, this is one of the concerns perhaps
[29:02] for for autonomous vehicles is what
[29:05] happens if there's an outage and then
[29:06] these things don't operate the way that
[29:08] they're supposed to operate. Um, what do
[29:11] you do? How do you adapt? Um, so how do
[29:13] you how do you both think about that?
[29:14] James, do you want to take that one
[29:15] first?
[29:18] >> Yeah. So, I think I think it's key to to
[29:20] in your safety case sort of think
[29:22] through these um these outages that
[29:24] could happen in the cloud. So the the
[29:26] the software that's on our Rivian
[29:28] vehicles um it it doesn't require a
[29:31] cloud connection. So the idea is that we
[29:32] can be um sort of in a safe state even
[29:36] if that external connection goes down.
[29:38] So there's all the processing happens on
[29:39] vehicle. I think um it's pretty
[29:42] important when you build a resilient
[29:44] system that you're not taking those
[29:45] dependencies unnecessarily or if you are
[29:47] that you have um sort of good backup
[29:49] processes. Um
[29:53] >> and Charlie, what do you think? And I
[29:55] would I would say I don't have a highly
[29:56] technical answer there. Um obviously
[29:58] that's it's a it's a challenge. We also
[30:00] worry about cyber security um the you
[30:03] know grid resiliency. But um one of the
[30:06] things that we want we try to do here in
[30:07] Michigan and um within the office of
[30:10] future mobility electrification is is we
[30:13] reach out we we like to engage industry
[30:15] and to call out some of these challenges
[30:16] and say how can you help us solve some
[30:19] of these challenges that we're talking
[30:20] about here? for example, um you know,
[30:22] the Amazon um outage, you know, we on an
[30:26] ongoing basis, we like to um call out or
[30:30] identify some of these ch these major
[30:31] challenges and concerns and
[30:32] considerations and and to work with ind
[30:34] industry to solve them. And that's why
[30:36] our our grant programs and our um pilot
[30:39] projects have been so successful.
[30:41] Identifying challenges and finding uh
[30:44] solution providers to address them and
[30:46] working together in public private
[30:47] partnerships is something that we are
[30:48] we've seen a lot of success in and
[30:50] continue to do. You know, Charlie, you
[30:53] mentioned you use the social media
[30:54] comparison 20 years ago, how people were
[30:56] just starting to figure out how to use
[30:58] social media. The older generation
[31:00] wasn't on it and now all of a sudden
[31:02] they're all on it. Um what do you think
[31:04] is the biggest variable now to achieving
[31:06] a fully autonomous world?
[31:12] Yeah, I think um
[31:16] I would probably say one economics. How
[31:18] do we make it economically feasible for
[31:20] the fleet operators or the auto um
[31:22] automakers um to fully integrate
[31:24] autonomous systems into their vehicles?
[31:26] Um I think it's going to be critical to
[31:29] continue supporting and seeing um
[31:32] increased level of autonomy and consumer
[31:35] u vehicles and and pro production
[31:37] vehicles. I think that will really help
[31:40] um you know uh drivers and individuals
[31:44] um feel comfortable with the technology
[31:46] getting it into their day-to-day
[31:48] vehicle. I think that's critical and
[31:50] also um being able to support um you
[31:53] know commercial fleets throughout um our
[31:56] nation and doing so uh and continuing to
[31:58] do so in a safe way. But um I I think
[32:02] addressing some of the challenges with
[32:04] infrastructure and addressing some of
[32:06] the challenges with um um deploying
[32:08] harsh weather conditions is something
[32:10] that is really important. Um but again,
[32:13] I'd probably, you know, go back to how
[32:15] do we get this um how do we get
[32:17] increased level of of autonomy, excuse
[32:20] me, into consumer vehicles and into more
[32:23] production vehicles. I think that's
[32:24] going to be um a critical way of of
[32:26] increasing adoption.
[32:30] James, what do you think?
[32:33] >> Um, yeah, I think I think, you know,
[32:35] Charlie's right that um a certain amount
[32:37] of this is just just time that people,
[32:39] you know, it takes time to try and then
[32:41] gain acceptance. I think it's actually
[32:43] changing, you know, pretty quickly. So,
[32:45] we we released our, you know, hands-free
[32:47] feature earlier this year and since
[32:50] then, you know, every month we've
[32:51] essentially seen the number of miles
[32:52] driven hands-free increase. So I think
[32:54] it's around 20% of all Rivian models now
[32:56] are done in that hands-free mode. So I
[32:59] think as these features get better, they
[33:00] become more capable, people become more
[33:02] comfortable with them, they get used to
[33:04] the, you know, the UI, the UX aspects.
[33:06] Um, and then also, you know, the system
[33:09] is learning through the data that we're
[33:10] able to gather um during those those
[33:13] drive events and all of that kind of
[33:15] ladders into this um sort of upward
[33:17] spiral of improvement. So, I think it's
[33:19] important that um OEMs are able to
[33:23] really learn from their fleets. I think
[33:25] that's something very new that they
[33:27] haven't had to do in the past. Um and we
[33:29] think it's a it's a key competitive
[33:31] advantage for us.
[33:33] >> It's so interesting as as adoption rates
[33:35] increase, I have to think too that
[33:38] driving fatality fatalities will
[33:39] ultimately go down. I mean, we have
[33:41] 40,000 driving fatalities a year right
[33:43] now with cars that people drive, right?
[33:46] Traditional cars. Um but so but at the
[33:49] same time though once if god forbid a
[33:53] whimo happens to kill someone who's
[33:55] crossing the street you're gonna get a
[33:56] there's gonna be a ton of attention on
[33:58] that and people are going to say
[34:00] computers kill people and how do you how
[34:02] do you go about this or or what do we do
[34:04] to combat this? So how do you all think
[34:06] about that and and the the safety
[34:08] element of all of this?
[34:12] >> Yeah, maybe I can start. So um I think
[34:15] we we have to recognize that the the
[34:17] baseline here is the average human
[34:18] driver and you know there's far too many
[34:20] fatalities in the US. I think the last
[34:23] stat I heard is something like a 747
[34:25] full of people die every day in the US
[34:27] you know on the roads in the US. So I
[34:29] think you know for me that's the number
[34:32] we have to drive down. We're not saying
[34:34] that these um systems are going to be
[34:37] perfect. I think that's an unrealistic
[34:39] expectation. In fact, I think if we have
[34:40] that expectation, we'll delay launching
[34:42] something that could be saving lives um
[34:45] potentially now. Um and so I think
[34:48] that's the number we should focus on and
[34:49] drive down. Um we spend a lot of work,
[34:52] you know, Rivian on our active safety
[34:54] systems. Um so a lot of that is actually
[34:56] fed by the same world model investments
[34:59] um on the ML side uh that are powering
[35:02] sort of the L2 plus features. And so
[35:04] really our goal is for you know Rivian
[35:06] vehicles to be the safest vehicles to be
[35:07] in and around. um through those active
[35:10] safety features. So you can imagine
[35:11] almost like a safety bubble where we're
[35:13] preventing the vehicle from getting into
[35:15] these collisions and I think systems
[35:17] like that deployed widely on consumer
[35:19] vehicles will really start to move the
[35:20] needle um on these fatality rates.
[35:25] Yeah, I think it's a really important um
[35:27] question and um I think one of the
[35:30] important things here is to um make sure
[35:33] that we are being with the government
[35:35] being in government um working closely
[35:38] with industry to ensure that safety
[35:40] standards are are top-notch and are um
[35:43] are we're aligning on um you know making
[35:47] sure that bad actors aren't able to
[35:50] access autonomous vehicles or you know,
[35:53] in cyber security um capabilities are
[35:55] are in place. Um and then making sure
[35:58] that we educate the community um that we
[36:00] that the vehicles are in. Um I think
[36:03] most most communities want to um know
[36:07] that the roads have be roads have gotten
[36:10] less and less safe. Um people, you know,
[36:14] technology has the ability to actually
[36:16] um make our make our road safer and the
[36:19] average person is not going to drive as
[36:22] safe as autonomous vehicle. Um they are
[36:26] um you know just to be able to trust the
[36:28] technology and and that's going to take
[36:29] some time and um just constant
[36:31] engagement with with communities that
[36:33] the the vehicles are in. I think that's
[36:34] really important. When you talk about
[36:36] trusting the technology, it gets me to
[36:38] this broader question in the industry
[36:40] about what's the better approach to
[36:42] autonomy. Is it, you know, it's the
[36:44] cameras versus the LAR question. Is it
[36:46] using machine learning or a rules-based
[36:49] system? James, what do you think?
[36:53] >> Yeah, I mean, I feel like those
[36:54] arguments have sort of largely been been
[36:56] settled. Um, so I think you really want
[36:59] to use and leverage machine learning for
[37:01] as much of the driving task as possible.
[37:03] The reason you you should do that is
[37:05] because um specifying driving in rules
[37:07] is actually it's very complicated. You
[37:09] end up with huge you know spaghetti code
[37:12] of heristics. Um it is actually not well
[37:15] specified in many cases. So you know
[37:17] think of the example of a lot of
[37:18] vehicles entering a a stop intersection.
[37:21] You know there are rules on how that's
[37:22] supposed to be handled but that's not
[37:24] how humans actually navigate those
[37:26] things. And so to sort of encode all of
[37:28] that in in a rulesbased system is is
[37:30] almost impossible. So we believe in um
[37:33] doing as much in the machine learning
[37:34] model as possible really learning from
[37:36] um customer driving data. There's an
[37:38] effort we have ongoing at the moment
[37:40] called the Rivian large driving model
[37:42] and this is supposed to be an offboard
[37:44] um huge model that can um essentially
[37:46] learn all the nuances of human driving
[37:49] from all the data we receive and then
[37:51] but then I think you have to you have to
[37:53] sort of couch that in a system that
[37:56] provides those those guardrails, right?
[37:58] So we we can use the ML um as much as
[38:01] possible especially to to have this sort
[38:03] of humanistic um and sort of nuanced
[38:05] understanding of how to drive but we can
[38:08] still have guardrails on that system
[38:09] that say okay I don't want to collide
[38:11] with anything I don't want to run a red
[38:12] light um I need to be cautious in this
[38:14] situation and so I think it's it's that
[38:16] combination using ML as much as possible
[38:19] because that is the most scalable and
[38:20] sort of powerful approach but then
[38:22] having still a rules-based um uh set of
[38:26] uh features at the end that you can use
[38:28] to kind of guarantee certain aspects
[38:30] about the driving behavior. And then I
[38:32] think on the when you come to sort of
[38:34] cameras versus versus lighter versus
[38:36] other modalities, I think for us we
[38:38] would say um you know more modalities is
[38:41] better and you know Charlie was talking
[38:42] about um uh you know adverse weather you
[38:45] see in Michigan and I think it you know
[38:48] it's exactly those cases where those
[38:50] additional modalities really really can
[38:52] help. Um so you know for us it's it's
[38:55] really you know can you get the right
[38:57] sensing sort of independence and um
[39:00] different views of the scene at an
[39:02] economically you know affordable price
[39:04] point for consumers and that's what
[39:06] we're really focused on.
[39:08] Yeah, and I I would say we are here in
[39:10] Michigan trying to to support industry
[39:13] and and getting to to where we don't
[39:16] industry and and fleets um autonomous
[39:19] vehicles aren't relying on um
[39:22] infrastructure,
[39:23] but at the same time, how do we improve
[39:25] infrastructure to make um to in increase
[39:29] redundant redundancy, improve safety? Um
[39:32] and so that's kind of what the approach
[39:34] we're taking. How do we support machine
[39:36] learning and autonomous vehicles that
[39:38] aren't completely reliable on VTOX or
[39:42] you know vehicle to infrastructure
[39:43] communication but being able to have
[39:45] extra redundancy um with roadside
[39:48] roadside units and and infrastructure
[39:50] technology that is able to provide kind
[39:52] of a backbone and um additional layers
[39:54] of safety. Uh we for example we have a
[39:57] um a corridor an autonomous vehicle
[39:59] corridor being built built out uh
[40:02] between Detroit and Ann Arbor about a 30
[40:04] m 39 mile segment of of interstate that
[40:07] will have um vehicle to vehicle
[40:10] communication technology um technology
[40:13] ve um vehicle to infrastructure
[40:16] capabilities um that will support the
[40:18] integration of autonomous fleets into
[40:20] our our normal traffic. Um, so I think
[40:22] that's the approach that will will be
[40:25] most su successful down the line and the
[40:27] approach that we're taking here in
[40:28] Michigan.
[40:29] >> You know, you've both touched on the
[40:31] weather issue and Charlie specifically,
[40:33] obviously you're sitting in Michigan
[40:34] where you guys have some pretty severe
[40:36] weather there. Um, what I mean, how do
[40:40] you is this the biggest variable to
[40:42] achieving a fully autonomous world? Is
[40:44] this how do you combat severe weather
[40:46] conditions?
[40:50] I would love to hear um James' thoughts
[40:51] from a technical perspective, but I I
[40:53] would say um you know, Michigan, yes, we
[40:56] have harsh winters, but our summers are
[40:58] absolutely beautiful. Please come visit.
[40:59] Um but I would say that um you know how
[41:02] do we support uh new technology that
[41:06] might be able to help um ensure that the
[41:07] sensors are clean um if there's if in in
[41:10] a rainstorm for example or how do we
[41:12] make sure how do we support industry in
[41:14] new technology that provides better
[41:16] cameras that cameras that can work in an
[41:19] adver adverse condition. So that's kind
[41:21] of what we're doing here is is providing
[41:23] the the platform throughout the state to
[41:26] be able to test new technology and
[41:28] identify, you know, startups and
[41:30] technology providers that are working on
[41:32] some cutting edge technology that will
[41:34] help AVs work in all types of
[41:37] conditions.
[41:39] >> Yeah, I think um you know Charlie
[41:40] touched on some of the the things you
[41:42] know I think you'd start with the
[41:43] sensors, right? So yeah, this sensors
[41:45] see clean and unluded. Can they can they
[41:48] see the scene? you know, in some some
[41:49] foggy conditions, some, you know, very
[41:51] heavy snow, um you actually can't really
[41:53] rely on cameras. You have to then um you
[41:55] know, start using radars. Um
[41:58] you know, even non-weather related
[42:00] scenarios like, you know, the sun, a low
[42:02] sun, you know, shining directly into the
[42:03] cameras that can be challenging um from
[42:05] a vision only. Again, that's where, you
[42:08] know, radar and LAR can really help. So,
[42:09] I think you sort of start start with
[42:11] that. You need to have that um that kind
[42:13] of patchwork of sensing capability and
[42:16] sort of different different sensors as
[42:17] well.
[42:18] Um I think then there's then the data
[42:20] piece is very key. So um how do people
[42:23] actually drive in snowstorms? It's not
[42:25] the same way um that you drive in an
[42:27] unluded, you know, sunny day, right? So
[42:29] do you have the machine learning and do
[42:31] you have the data flywheel that's that's
[42:33] telling you how to handle those
[42:34] situations? I think that's a that's a
[42:36] big advantage that um a kind of fully
[42:39] integrated um sort of databased OEM like
[42:43] Rivian has over for example like a rower
[42:46] taxi where you have to actually send
[42:47] those fleets to go and gather this
[42:49] specific data in these different
[42:50] conditions. Um and then finally um yeah
[42:54] sort of how how do the rules you know I
[42:56] talked about those guardrails. Do those
[42:57] guardrails need to change? you know, for
[42:59] example, um you know, when you when
[43:01] you've got snow on the ground, people
[43:03] often don't follow the lanes. They can't
[43:04] see them. So, you you sort of have these
[43:06] virtual lanes that pop up. Does that
[43:08] need to be taken into account in your in
[43:10] your guardrails? Um so, it's sort of
[43:12] it's multifaceted. I wouldn't say it's
[43:14] the primary challenge. I still think um
[43:17] you know, density um and complexity in
[43:21] urban areas is is typically where the
[43:24] those final um big challenges are. um
[43:27] you know James to just reason about you
[43:29] know many many objects in a scene maybe
[43:31] there's a lot of nuance negotiation and
[43:33] things happening those can be tricky to
[43:34] handle um in a good way
[43:36] >> on the density issue James you know I I
[43:38] took my first Whimo earlier this year
[43:40] out in San Francisco and like so many
[43:42] other people was just so so fascinated
[43:44] by it um and so and it seems like
[43:47] wherever you turn in San Francisco
[43:49] there's another Whimo on every street
[43:50] corner but in terms of New York City
[43:53] it's I I just walk around here and I
[43:55] think to myself how are we going to have
[43:56] these types of cars in New York City and
[43:58] they're Whimo is already testing um
[44:00] testing their fleet in in the city here.
[44:02] But I mean, how do you how do you adapt
[44:05] to these different densities in
[44:07] different cities and how all the
[44:08] different layouts and how everything is
[44:10] is so different in different places?
[44:13] >> Um yeah, so I think you know you hope
[44:15] that your that some of your base systems
[44:17] obviously generalize to those places.
[44:19] Now, of course, there's going to be, you
[44:21] know, traffic specific um kind of rules
[44:23] of the road almost that exist in that
[44:26] exist in New York but don't exist in San
[44:27] Francisco and things like that. And I
[44:28] think that's where the where that data
[44:30] flywheel really is important. And I
[44:33] mean, you talked about New York City,
[44:34] but you know, if you go outside of the
[44:35] US and you talk about, you know, a
[44:37] country like India where um you know,
[44:39] some of the driving's you know, even
[44:40] more, you know, intense I would say um
[44:43] and very different again. So you have
[44:44] sort of had to think like how does a
[44:46] system um scale and I think that it
[44:49] really sort of tips you in favor of the
[44:51] MLbased approaches right because there
[44:53] you can gather the data you can see how
[44:55] people drive you can uh you know learn
[44:57] start to learn how um to sort of mimic
[45:00] it versus you know rules based systems
[45:02] that can really they can be brittle so
[45:04] you take them to a new place and
[45:06] suddenly those rules um don't work
[45:07] anymore.
[45:09] James, you're a former way Whimo
[45:12] employee before you joined uh Rivian.
[45:14] So, I have to ask about the elephant
[45:16] room. I got to ask about Elon Musk and
[45:18] Tesla's approach uh to autonomous
[45:21] driving right now.
[45:23] How does Tesla compare to Whimo compared
[45:26] to Rivian and others? And who's who's
[45:29] getting it right and perhaps who's not
[45:31] not doing it as well?
[45:35] >> Um yes, I'm obviously not privy to, you
[45:37] know, what's going on inside Tesla. Um I
[45:39] think what I would say from the outside
[45:41] is that um I think they've
[45:45] you know on the good side they've really
[45:46] sort of pushed um the OEMs forwards in
[45:48] the sense that they took a very um sort
[45:51] of MLbased approach early on and um I
[45:55] think that is the right way to build
[45:56] these systems. Now on the on the counter
[45:58] side, I think they have um a sort of
[46:00] very uh rigid point of view I guess on
[46:03] um different sense modalities which I
[46:07] don't think is you know fully
[46:08] explainable just from an engineering
[46:09] point of view. So um I would say it's
[46:12] sort of a a mixed bag. Um I think we're
[46:15] really focused on um you know can we can
[46:18] we bring that L4 technology back to
[46:21] consumers in the best way possible. And
[46:23] I think um you know sensors are sensors
[46:25] can get you there faster and they can
[46:26] get you there in a more robust way. And
[46:28] I think the the price point of a lot of
[46:30] these sensors is is no longer that you
[46:33] know liars are $10,000 um because of the
[46:36] huge um scale that you've seen in China.
[46:38] Those are those are coming down to you
[46:40] know a few hundred which is very much in
[46:42] the in the envelope of um you know
[46:45] consumer vehicles. So, I think, you
[46:47] know, that's that's kind of the approach
[46:48] we're taking and the one that I think is
[46:49] is going to get us there um in the most
[46:52] sort of direct way.
[46:54] >> Charlie, what do you think?
[46:58] >> Yeah, you'll have to um recap that
[47:00] question one more time for me, Steve. My
[47:02] apologies.
[47:02] >> Just comparing the different approaches
[47:04] that uh automakers are taking from Whimo
[47:06] to Tesla. Um, Whimo I has this
[47:11] reputation in the industry for taking a
[47:13] slower perhaps more more cautious
[47:15] approach to how they go about things
[47:17] whereas Tesla is more of a move fast and
[47:20] break things kind of uh approach here
[47:22] and obviously they have the robo taxi
[47:23] fleet that's uh starting to be rolled
[47:25] out. So just curious your thoughts on
[47:27] the different approaches and the pros
[47:29] and cons of both.
[47:31] >> Yeah, you know I think
[47:34] we don't really have like a
[47:37] reference per se on on what a company's
[47:40] approach would be. Being with the state
[47:42] of Michigan's economic development
[47:43] department, we we want to support all
[47:46] companies that want to grow here in
[47:47] Michigan, hire here in Michigan. I think
[47:49] the big thing is um deploying in a safe
[47:52] way. That's that's the most important
[47:54] thing. whether you're taking a a fast
[47:56] approach with um using machine learning
[47:59] and and or you're taking a slower
[48:01] approach with a certain use case and and
[48:03] leveraging infrastructure. Um we don't
[48:06] really have a preference per se as I as
[48:08] I mentioned. It's just um we are trying
[48:10] to to make Michigan a great place for
[48:12] companies to thrive and to to uh to
[48:14] deploy their technology in a safe way
[48:16] within communities. And so um you know
[48:19] we are we think that we have a really
[48:21] strong obviously automotive industry
[48:23] here but how do we make sure that we we
[48:25] remain competitive and how do we kind of
[48:28] bridge the gap between legacy automotive
[48:30] and and the startup world in in the on
[48:33] the west coast and and bring the minds
[48:35] together to ensure that we're you know
[48:37] we see this widespread widespread
[48:39] adoption over time.
[48:41] James, what what do you think is the
[48:43] biggest bottleneck for the robo taxi
[48:45] industry right now?
[48:51] >> Um I I mean I'm no longer in, you know,
[48:53] the robo taxi industry. So um I I would
[48:56] say that um it just takes time to scale
[48:59] these things. They're, you know, fleets
[49:02] are physical things. You have to charge
[49:04] them somewhere. You have to clean them
[49:06] somewhere. you have to you know think
[49:07] about power and um the operational
[49:10] aspects and so that just takes time. Um
[49:13] so I think and and then of course
[49:16] there's you know there could also be you
[49:17] know further engineering development
[49:18] that has to happen to you know maybe
[49:20] cover specific cases that you see in new
[49:22] places. So I don't see a fundamental
[49:24] issue but I think you know this isn't
[49:27] like um I don't know Charlie mentioned
[49:29] social media right it's not like just
[49:30] downloading an app someone has to go and
[49:32] build these vehicles put all the sensors
[49:34] on they have to be kept somewhere they
[49:36] have to be you know kept clean
[49:37] operationalized and that takes time it's
[49:39] like a the physical investment aspects
[49:42] um are you know just take take time
[49:47] >> Charlie what do you think
[49:49] >> yeah you know I think um
[49:52] identif Identifying a clear use case is
[49:54] important for for fleet operations. Um
[49:57] deploying them in in a certain scenario.
[50:00] Um at least right now I think that's an
[50:02] important way to continue momentum and
[50:04] continue adoption. Um you know deploying
[50:07] them in a highly urban complex
[50:10] environment may not be the best best um
[50:13] option right now. So can we identify
[50:15] fixed routes or can we identify certain
[50:18] use cases that um fleets will thrive in
[50:20] and and um it's more feasible you know
[50:23] at this time and for example looking at
[50:26] how can we partner with large employers
[50:28] to provide um you know shuttle services
[50:30] between their facilities or can we
[50:32] deploy autonomous vehicles at airports
[50:34] from the parking to the terminals. um
[50:37] looking at deploying autonomous vehicles
[50:39] on at universities to get um students um
[50:43] engaged in to experience that
[50:45] technology. I think that's the that's a
[50:47] really um critical way at this juncture
[50:50] to continue, you know, supporting fleet
[50:52] operations uh before we see them in in
[50:55] highly complex multimodal environments.
[50:59] >> I'd like to shift gears a little bit. we
[51:01] could do a little bit of a a lightning
[51:02] round here on some of your predictions
[51:04] for what you guys think is going to come
[51:05] true in this industry in the coming
[51:07] years. So, uh I have a my older son is
[51:10] seven years old. In 10 years, he'll be
[51:12] eligible to get his driver's license
[51:13] here in New York. Will he need it and
[51:16] will he even want one? James, what do
[51:19] you think?
[51:22] >> Um so, I I grew up in London and even
[51:24] without autonomous vehicles, um you
[51:26] know, London has a fantastic, you know,
[51:28] public transport system. So when I was
[51:30] young and sticking in the city um you
[51:33] know I didn't need to drive and actually
[51:34] I took my driver's license quite late. I
[51:36] would say once you have a family and you
[51:38] start um needing to get from A to B
[51:42] that's when the real value of you know
[51:44] personally owned vehicle comes. So I I
[51:46] suspect your son will actually still um
[51:50] get a driver's license even if his early
[51:52] years are you know in in a rubber taxi.
[51:55] But I think the world in which the
[51:57] vehicle he'll be driving in I think will
[52:00] be much safer. It will have um you know
[52:03] many more autonomy modes. Um you as a
[52:06] parent, this is actually you know
[52:07] feature I'm excited about you know may
[52:09] be able to engage a teenager mode right
[52:11] which puts the vehicle in a in kind of
[52:13] like a extra safe state. Um so that uh
[52:17] you know your son can't can't speed or
[52:20] you know do donuts in the parking lot or
[52:22] whatever else he wants to do. Um, and
[52:24] so, uh, yeah, I think that's probably
[52:26] most likely. I I don't see a huge shift
[52:29] away, um, from the automobile, at least
[52:32] in the US, just because of, um, you
[52:35] would also need a a commensurate shift
[52:37] in, you know, where houses are built and
[52:39] how people are living and everything
[52:40] else. Um, but I do think that, uh,
[52:43] consumer autonomy will will become, uh,
[52:47] essentially a, you know, a must have on
[52:49] every vehicle.
[52:52] >> I would say must have on every vehicle.
[52:54] >> Your son will.
[52:55] >> Yeah, I would say your son will probably
[52:56] um need to get a driver's license. And I
[52:58] think that's okay. I think we we're
[53:00] going to continue to see more um AVs on
[53:02] the roads, but it's going to be a little
[53:04] bit longer down the line until you you
[53:06] don't need a um a driver's license. I
[53:09] actually p personally enjoy driving from
[53:11] time to time. Um there's also times
[53:13] where I I I would love to get an
[53:15] autonomous vehicle and not drive to get
[53:16] work done or to to get some rest, for
[53:19] example. Um, but uh I think that's a
[53:21] little ways out and it really depends on
[53:23] where you live your lifestyle as as
[53:26] James alluded to. If you're if you live
[53:28] in a rural community, most likely you're
[53:30] going to, you know, at least near-term
[53:33] over the next 5 10 years still want need
[53:36] a driver's license, want a driver's
[53:37] license. If you live in a highly urban
[53:39] area, maybe that's not the case.
[53:42] What percentage of cars on the road will
[53:45] be autonomous versus not in let's say 10
[53:48] years from now?
[53:51] >> 20%.
[53:53] >> What are we at right now?
[53:55] >> And what are we at right now?
[53:59] >> That's a good question. James, do you
[54:00] know?
[54:01] >> I would say sort of define autonomous.
[54:04] So, do you mean like a full robo taxi
[54:05] level autonomy or do you mean vehicles
[54:08] um that for example could provide an L3
[54:10] capability that gives you your time back
[54:12] on the road and makes makes driving
[54:13] safer? So, I think I think that's where
[54:15] there's actually a big spectrum of
[54:17] autonomy here and
[54:19] >> I I think we're I think people focus on
[54:22] the endpoint. Um but I think there's
[54:24] actually many other customer societal
[54:27] benefits you get on the way there. Um,
[54:30] so I think, you know, to be honest, I
[54:32] think every vehicle, new vehicle sold in
[54:34] 10 years time to be competitive will
[54:36] have to have, you know, close to
[54:38] best-in-class autonomy. I think it's
[54:40] becoming more and more of a consumer
[54:42] preference. We we see actually much
[54:45] higher conversion rates um when people
[54:47] try our autonomy features in the in the
[54:49] stores. I think it's something like a 3x
[54:52] conversion rate. And so I think um I
[54:54] think that that shift is really
[54:55] happening. And I think as you see, you
[54:56] know, robo taxis roll out, people will
[54:58] just expect and demand more and more. So
[55:00] I think I think this this tide is coming
[55:02] and um OEMs need to be ready.
[55:06] >> What what matters more better data or
[55:09] the algorithm?
[55:15] >> I think if you had to
[55:18] I think if you had to pick one, you
[55:20] would pick the data.
[55:22] Um but to make the best use of the data
[55:26] you need, you know, excellent, you know,
[55:29] machine learning engineers and
[55:30] approaches to to understand that data
[55:33] and to learn um you know, how to drive
[55:36] from it essentially.
[55:39] But the data is the most important. If
[55:40] you don't have the data, I think you you
[55:43] there's no getting around it really. So,
[55:44] you know, if you have if you don't have
[55:46] the Michigan snowstorm, I don't think
[55:48] there's any feasible way you could build
[55:51] an autonomy system that then can handle
[55:52] those Michigan snowtorrms. You have to
[55:54] go there and see the data.
[55:57] >> And actually, just sticking on that,
[55:58] James, um, Rivian is doing a lot in the
[56:00] Gen AI space. And so, do you want to
[56:02] talk just a little bit about um, how are
[56:04] you using Gen AI to, um, improve the
[56:06] autonomy that you guys are offering in
[56:07] your vehicles?
[56:10] >> Yeah. Yeah. So I think here's maybe two
[56:11] sens in which you know we we use genai.
[56:14] So I think um one is in this like large
[56:17] driving model that I alluded to earlier.
[56:18] So that is actually a very large
[56:21] transformer-based model. It looks a lot
[56:22] like um a large language model in the
[56:24] sense that you have you know data coming
[56:26] in in the LLM space. It's it's text in
[56:29] our in our side. It's really sensor data
[56:31] raw sensor data. And then you have these
[56:33] large transformers that that kind of
[56:34] chew on that data. And then at the end
[56:38] um we we generate tokens. And now these
[56:40] tokens are not words or or um you know
[56:42] letters in the LLM case. They're
[56:44] actually little snippets of trajectories
[56:46] and we kind of we piece them together
[56:48] and that gives you the the best um the
[56:51] model's best interpretation of the
[56:52] future driving path. Um so I think you
[56:56] know that that model is very heavily
[56:57] inspired by a lot of the work that's
[56:59] happening on LMS and of course we we
[57:01] kind of uh slipstream on the work that's
[57:03] happening there. I think that's one
[57:05] sense. The other sense is um we're also
[57:08] you know uh seeing a big sort of um
[57:10] productivity boost from using some of
[57:12] these genai tools. Um uh I would say not
[57:16] replacing people but really making them
[57:18] more effective. So um you know improving
[57:21] the velocity that you can write code
[57:22] that you can test code that you can do
[57:25] things like code review and um uh and
[57:28] you know interface with APIs and things
[57:30] like that. So I I think um yeah it it is
[57:33] an accelerant and and definitely
[57:35] um on both sides. I think very essential
[57:37] to the work we're doing on autonomy.
[57:40] >> Charlie, what's the biggest myth that
[57:42] you'd like to debunk about self-driving?
[57:48] >> That the techn is not ready. I think the
[57:50] technology industry has been um
[57:52] incredible. At least
[57:55] I think in the United States, you know,
[57:56] the technology being developed by
[57:58] industry, by academia is um has gotten
[58:01] us to a point where it's ready. Let's
[58:03] let's get these techn let's get these
[58:04] vehicles out there and get let's allow
[58:06] people to experience them. Um but I
[58:08] that's the big thing. I think too many
[58:10] people think that the technology is not
[58:12] ready, it's not safe, but um I think
[58:14] that's just the opposite.
[58:16] >> James, what do you think?
[58:19] >> Yeah, I you know, plus plus one to
[58:21] Charlie. Um I think um
[58:24] uh yeah I I think we we we just need to
[58:26] get more people experiencing um what is
[58:28] out there and I think we
[58:30] you know we need to push um US OEMs to
[58:34] to be more tech forward um you know you
[58:37] see how the level of autonomy features
[58:39] that are present in Chinese um OEMs and
[58:42] I think uh you know I kind of feel like
[58:45] OEM's got a little bit complacent um in
[58:48] in that regard and I think we have to
[58:49] push everyone forwards Um, so that's
[58:51] what we're, you know, we're trying to do
[58:53] here at Rivian.
[58:55] >> And finally for you, James, what's one
[58:57] word to describe the state of
[58:58] self-driving technology today?
[59:02] >> I think on the cusp, I know it's not one
[59:04] word, but it's, you know, yeah, one
[59:06] idea. So, I think it's really on the
[59:08] cusp where you um you're seeing
[59:10] significant deployments um in certain
[59:12] cities and I think um in in dense
[59:15] metros, I think that will happen quite
[59:17] quickly. And then I think you'll see um
[59:20] a trickle down into consumer vehicles
[59:22] for most other trips
[59:24] >> on the cusp. I like that. Charlie, what
[59:26] do you think? One word to describe the
[59:28] state of self-driving technology today
[59:31] >> here. I think it's here and we're going
[59:33] to see it more and more every day.
[59:36] >> Well, thank you James and Charlie for
[59:39] these insights. Uh and to everyone who
[59:41] joined us today for inside self-driving.
[59:43] I also want to thank our partner MobileI
[59:45] for their support of today's program.
[59:47] I'm Steve Russell from Business Insider.
[59:50] Thank you for being with us. We'll see
[59:51] you next time.
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