Inside Self-Driving: The AI-Driven Evolution of Autonomous Vehicles

0:19 Hello everyone and welcome to Business Insiders inside self-driving the AIdriven evolution of autonomous vehicles presented by Mobile Eye. I'm Steve Russell, chief news editor here at BI. And today we're diving into one of the most transformative and debated frontiers in technology. How AI is turning autonomous mobility from a long promised dream into a fast approaching reality. We'll explore how automakers, tech innovators, and policy makers are working together to make autonomy safe, scalable, and trusted, and what that means for businesses, cities, and all of us who share the road together. First, we're starting today with a conversation presented by our sponsor, Mobile Eye, that goes inside the company's collaboration with Lyft as they work to bring driverless technology to scale.

1:22 Thank you, Steve. I'm Dr. Deborah Bervishes and I'm happy to be here. Robo taxis already operate in a few cities, but taking autonomous vehicles mainstream comes down to three things: auditable safety, consumer trust, and economics that work. I'm joined here by JJ Youngworth, executive vice president of autonomous vehicles at Mobile Eye, and Stephen Hayes, VP of autonomous fleets and driver operations at Lyft.

1:53 Thank you both for being here. Let's talk about trust and safety. It seems like one of the main goals of autonomous vehicle companies is to convince both the writers and the regulators that these kinds of vehicles are safe. So, Stephen, at Lyft, your role is to interact directly with a rider. What would they need to see or experience to feel comfortable using an autonomous vehicle? >> Great question. at Lyft, our purpose is to serve and connect, and that's something that we do about 800 million times a year, helping riders get to where they need to go. and over time, AVs are going to make up a bigger and bigger percentage of those trips on our platform. And if you are tuned into this broadcast, chances are you are a bit of a tech enthusiast and an early adopter. But the reality is for most of the people who open up the Lyft app on a daily basis, they're just looking to get to where they need to go. and that's where it is our privilege and responsibility to introduce millions of new riders to exciting autonomous technology. And in order to do that effectively, we need to find the right autonomous trip for the right rider at the right time. And then once they come in, we need to educate them about the experience that they're going to have.

3:13 and make sure it's a delightful one. And all the while, what is going to be really important for us is making sure that we have happy repeat customers who are getting where they need to go even more quickly and efficiently than they are today. >> Awesome. JJ, from your perspective at Mobile Eye, which three pieces of proof would you hand to a regulator to prove that autonomous vehicle technology is ready and safe? >> Yes. So, of course, number one is is safety as you just mentioned. and there are different metrics, different KPIs, on on how, safety is measured. one is, you know, to look at, the crash rates and there of course the goal is to be safer than human drivers. You know, we believe that eventually the technology will support to be 10x safer, maybe 100x safer.

4:04 and you know, the technology basically never sleeps. it has eyes all around the vehicles. It can react in milliseconds. It doesn't have, you know, a second reaction time like like human drivers. It can see better at night with all the sensors technologies and redundancies. And then of course, you know, looking at let's say cities, customers. I mean it's also very important that these vehicles you know are fitting into regular traffic. you don't need you know special lanes, special infrastructure. but you know regular like a regular you know human driver fitting in there also not being too slow having a certain assertiveness and then of course trust it's very important for both for riders as well as for you know cities and operators and companies like Lyft who are offering the services.

4:52 I like your answer because it reminds me that it's not only safety that's important, but the vehicle also needs to operate in an assertive enough way that it inspires confidence with the writer that it's going to get them from point A to B. maybe Stephen, can you comment on this? >> Yeah. I think it's such an important and underappreciated aspect of taking autonomous vehicles from the prototype phase to scaled commercial deployments is the ride experience and the ride feel of the autonomous vehicle itself because the vehicle could from a technical perspective be incredibly safe. But as JJ mentioned, if it is so cautious that you end up waiting three or four different light cycles to take an unprotected lefthand turn, you're going to end up with a lot of riders who are like, "Well, that was that was kind of cool, but this is not the way that I I'm going to get choose to get around on a day-to-day basis." And so the ride feel and being able to kind of fine-tune the the ride experience to make sure that it gets you where you need to go in the right amount of time is going to be really important because today human driver trips tend to be a little bit shorter from a a trip duration standpoint than AVs. so it'll be really interesting to see AV companies like Mobilei continuing to kind of like move the dial on what the ride experience and feel of the AV after you know continuing to master all of the fundamentals of the autonomous driving because the the style is is actually very important.

6:29 >> Sure. Yeah. It's fascinating how it works. So let's move on to another topic about economic e the economics of scale. We know that we can already hail a driverless taxi in cities like San Francisco, Austin, and Phoenix, but for most of the country, autonomous vehicles still feels like maybe five years away. What are the make or break economic realities of turning a test program into a viable business? And why has it been so hard to make self-driving mainstream everywhere?

7:04 >> I can jump in and take a take a stab at that. and then I'll hand it over to JJ who is definitely in the best position to speak to the the engineering of what makes it hard to build a self-driving vehicle. you know the the reality you said Deborah is there are there are cities around the country where you see autonomous vehicles. Some of them are commercially deployed, some of them are in testing, but that we should all remember represents the very tip of the iceberg. And underneath that deployed asset, there is an entire value chain of different partners and ecosystem of players that needs to be marching in lock step in order to support the commercialization of that asset. And I think this is really important. So just want to unpack it for for a moment.

7:50 that value chain starts with companies like Mobilei which are building the self-driving technology. It spans to OEMs, the auto manufacturers who are building and producing the vehicles. And today we're generally taking retrofitted vehicles. so they're not they're not built for autonomous specifically and that means you need to kind of like tear them apart and then put them back together with the tech stack on it and that has significant implications from a cost and scale standpoint. And then once you have the vehicle and you have the AV stack on it, then you need a fleet manager and an operator and a financing partner who's going to hold that vehicle. and then you need a mobility marketplace where you can deploy that asset and commercialize it. And you need a front-end customer experience, a way that riders can interact with your technology. And in order to go from hundreds to thousands of vehicles, again, you really need all the different component parts of that value chain coming together in order to achieve sustainable economics. And I think that's where the the industry has a a growing appreciation for the complexity of doing that. and that's where our partnership with Mobile Eye, the fact that Lyft owns and operates a subsidiary called Flex Drive. We 15,000 vehicles that we directly manage today and obviously a thriving marketplace.

9:08 These are all really important ingredients >> that is >> yeah maybe to add to that just just quickly is you know from a technical side I think you know in order to scale it's very important to have you know a product that basically has you know high efficiency and also is built from a cost perspective and u actually from an overall technical approach in a way that you can actually go quickly from city to city because this is something you know where we look back the last five years the deployment rate you know has been very slow. We are still not at the beginning of the actual scaling phase and you know for us as mobile you know we always say like safety is first and then scalability second and efficiency third.

9:52 >> Yeah I love that. I mean from what you're saying it's pretty obvious that no single company is going to scale autonomous vehicles alone but collaboration in this in industry is notoriously tough. So can you talk about like a gridlock or or or a bottleneck in operational work between the OEMs and the mobility platforms and the provider that you feel is slowing down progress. >> So based on my experience now and I've been in this space now also since you know about 15 years this is actually not the case. I mean yes there is competition between you know automakers and there's competition between the technology providers and so on. So looking at these, you know, four or five like value chain layers that Stephen just explained basically there's a lot of collaboration and and you know it's also there are different business models you know you look at you know maybe Whimo an all-in kind of more vertical type of approach and and look at our you know approach and partnerships for example with Lyft and and with you know Folkswagen as our main strategic partner for you know the first VW ID as you know platform vehicle, beautiful vehicle also nicely integrated technology from from our side and also with others. So we try to be very open in this regards to actually work with different platform providers on the vehicle side but also on the go to market side and we think this is the better approach this open approach >> and I I I'll just chime in and agree with JJ's sentiment that I think whereas in the very early stages you saw some players say hey we need to vertically integrate and own the whole thing ourselves and both from a complexity standpoint as well as the growing recog recognition that the market opportunity here is enormous and we are in the bottom of the first inning. I think more and more players are realizing that we can go further by partnering together and finding partners that have very complimentary skill sets.

11:59 >> Great. Talk to me about the future. What's the next milestone that's going to really matter in the next two years? I'll answer this from a from a customer perspective and I'd love to hear JJ's perspective from a technical one. the metric that we at Lyft are going to be obsessing over for the next two years and beyond with our autonomous partners is the percentage of riders who opt in after taking an autonomous trip into the next autonomous trip.

12:33 Because again, going back to what I said in the beginning, for a lot of people, AVs just aren't on their radar or it's a bit of a novelty. It's like, yeah, I'm coming to San Francisco. I want to try this new experience. It's kind of like going on a a new theme park ride at Disneyland. And then there are people who are are getting habituated to taking AVs, and we want more and more people in that category. And so you only have one chance to make a first impression. And we want to make sure that we are delighting writers in setting appropriate expectations and that they're giving that back to us by saying, "Yeah, I will take Navy with you again."

13:08 >> And I I personally, you know, I'm I'm very interested in and you know, looking forward to, you know, kind of this dual path, you know, of bringing this technology to market on the one hand with fleets. I mean this is kind of natural because the technology is you know pretty expensive at the moment. But then the second path was consumer vehicles. basically you know letting people own or lease such vehicles maybe they even put them into fleets when they don't use them themselves but basically you know having consumer AVs and then fleet AVs and and you know looking at you know which type of services what type of vehicle you know interiors exteriors I mean there's going to be a lot of innovation in in the in the vehicle design you know maybe there will be even collaboration between automakers and you know design studios or furniture companies or you you know, interior designers to come up with completely, new new designs, partnerships. You know, you can have an office on wheels, you can have a lounge on wheels, you can have, you know, movie theater on wheels, anything you want.

14:08 And maybe, you know, depending on your needs and and once you order, you know, this or that type of vehicle or or rider service. >> I love the office on wheels. Okay, so this is a short question for both of you. What is one myth about autonomous vehicle safety that you want retired? >> I'll go. I think the myth about autonomous vehicle safety is that safety is enough because safety is critical.

14:40 It's necessary and it's not efficient. As we talked about earlier, if riders feel like the ride is jerky or it's too cautious, they're not going to be future customers of it. And so of course we need safety and we need a delightful experience around it. I personally actually think that one of those myth is that you know let's say as regular pedestrians or u you know maybe another vehicle you know driver that you might act differently you know in front of an AV that people think oh it's an AV I can just you know jump in front of it you know even at you know two feet or three feet I mean basically you know people still need to consider you know the physical limits of you know breaking distance and and and you know reaction and so on. So I think you know from that perspective and I know there's you know there are discussions about you know should AVs have this you know light blue light you know when they are active so that people can see pedestrians and so on. Oh this is an AV or an AV function is on. I have to say I'm kind of against this and and believe that it's better that people have respect you know for any type of vehicles and any type of sizes because at the end of the day these vehicles can only let's say react and break a certain let's say with a certain momentum and and brake power and and and so on you know basically just physical u limitations and I think it's important that people you know treat AVs the same way as you know human-driven vehicles just, you know, safer and better.

16:16 >> Yeah, it sounds like there's a lot to do to educate the market about this new technology. So, thank you JJ and Stephen for the great discussion. I really enjoyed it. It's fascinating to see how technology, trust, and collaboration are shaping the next chapter of mobility. Steve, back to you. >> Thank you, Deborah, JJ, and Stephen. That was a fascinating look at what comes next for autonomous fleets and innovation. Now, we're shifting gears to zoom out and look at the bigger picture.

16:47 How cities, automakers, and regulators are shaping the infrastructure and the mindset needed to make autonomy work for everyday people. I'm thrilled to be joined by two leaders in this space. James Philin is the vice president of autonomy and AI at Rivian. And before joining the automaker, James spent years at the forefront of autonomous vehicle development, leading software and perception teams at both Whimo and Zuks, where he helped advance the systems that allow self-driving cars to see and understand the world around them. Now, he's building technology that makes advanced driver assistance and autonomy integral to Rivian vehicles.

17:38 We're also joined by Charlie Tyson, who plays a key role in Michigan's autonomous vehicle pilot programs, advancing the state's efforts to turn transportation innovation into policy and infrastructure. He works at the intersection of government, industry, and research to make Michigan a national test bed for next generation mobility. James and Charlie, thank you both so much for being here. >> So, let's get right into it. is he >> let's talk about where we are the state of play today where we really are right now. How would you each describe the state of autonomous vehicle technology right now and specifically what's real what's still experimental and what's misunderstood. James, let's go to you first.

18:25 >> Yeah, I mean I think you're starting to see the phase where you know full autonomy has gone from the sort of science project into an actual product. and you can go up to, you know, San Francisco, I can drive 40 miles north of me right now. And, you know, it was just going to be flooded with Whimos. and now Zuks is as well. So, I'm I sort of felt a few years ago that actually the the fundamental problems had been solved. That wasn't that doesn't mean that every every problem has been solved, but it was moving into a more engineering and deployment and scalability phase.

18:56 and then I think, you know, with that hat on, you got to think how does this change how people use transportation in the future. And I'm still a big believer in personally owned vehicles. I think that for every mile done in a robo taxi, probably in the future, you know, more than 10 times will be done in personally owned vehicles. I think the economics of a robo taxi versus a personally owned vehicle still mean that those two modes will be around for a very long time. And so that's why I made the the leap to Rivian. Essentially, can we bring that sort of L4 technology back to the consumer space and really provide value for our customers.

19:34 >> And Charlie, what do you think? >> Yeah, I think James hit the nail on the head there, but from a state's perspective, I think that we are we are going from like the testing phase to some level of commercial operations, but I think we're seeing it in certain states. for example, James mentioned California. We're seeing, I actually was just in Arizona. I took Whimos in Phoenix. commercial operations really impressive. but I think there are still some challenges.

20:05 the technology seems to be there for the most part. But consumer consumer adoption, how does it how do these techn how do these vehicles whether they're consumer AVs or consumer vehicles with some level of of autonomy or their autonomous vehicle fleets? How do we integrate them into our existing transportation network? I think that's still a challenge that we're working on here in Michigan and and really throughout the throughout the nation. >> You know, it's really interesting. I mean, I guess the one question I have for both of you is how do you get people to really feel comfortable about getting in an autonomous vehicle? I think about my parents who say that they'll never get in one, that they don't just don't trust the technology. And it seems like there's maybe a generational divide.

20:52 Boomers feel one way, millennials, genzers. I'm curious how you all think about this trust issue, particularly from a generational perspective. And James, do you want to start first? Yeah, I mean my sense is it's pretty non-existent. I mean my my parents are also in that generation and you know they're interested in what I'm doing but they don't really you know trust all these systems but you know the last time they visited we took a Whimo around it's very you know very quickly becomes normal actually and I feel like there's that initial kind of wow moment hesitation but then it's completely normalized people you know just go about their day. So, I think it's I don't think the trust issue is is going to be a persistent one. I also think that as people get more used to those higher levels of autonomy, they'll expect more autonomy in their vehicles. So, I think that trust issue actually trickles down and what you'll see is a huge sort of competitive swing towards you personally owned vehicles that offer those higher levels of autonomy. so I think that's kind of a bit of the race you see right right now and one I think Rivian is, you know, perfectly poised to execute on.

22:01 >> Do you agree, Charlie? >> Yeah. And I think Yeah, I agree with that totally. And I I think that, we just have to give individuals the the chance to experience the technology. that's why I think that although we want to see and we're pushing for commercial operations, these pilot projects and these these testing activity that allows consumers to experience the technology in different use cases is still really critical. we've seen a number of pilot projects in Michigan in some of our larger cities like Detroit of course, Grand Rapids, Ann Arbor. And the feedback from the surveys that we we sent out was really interesting.

22:43 most most of the the riders I think 90% of the riders in the survey respondents you came back saying that they would absolutely love to take another trip in an autonomous vehicle and they would recommend it to their their peers. they what was interesting though was we asked a question in the survey around removing the safety driver and I think that is a big piece here. That's where we started to see some of the the comfort levels change a little bit.

23:12 the survey respondents I think it was about 5050, you know, responding that they would be willing to get get in an autonomous vehicle without a safety driver. So I think just getting people more accustomed with the technology sharing the benefits and also I think it's it's critical to be able to you know educate the public and just give them that experience to to get inside the vehicle provide feedback and and let them know that these aren't being forced on them. They're they're being deployed in safe ways and and kind of a a crawl, walk, run approach. I think that's critical.

23:52 >> Charlie, you mentioned these these pilots. I guess my question is how how do you measure readiness? Is it, you know, the the number of miles driven? Is it disengagement rates? Is it just consumer trust or something else entirely? Like h how do we know that these things truly are ready to be out on the road? >> Yeah, I think it's different for it's kind of case by case, different for each project. for example, we had a project in Northern Michigan looking at supporting a a a full-size autonomous transit bus deployed at Sleeping Bear Dunes National Park. So, looking at a a very unique use case in northern Michigan. a majority of the of the riders were tourists visiting the area.

24:37 they either they had challenges with with parking, but I think it the way that we kind of measured read readiness for that project was how many times the the autonomous vehicle had to be essentially how many times the safety driver had to take control of the vehicle. were there challenges with connectivity in that region due to to Wi-Fi or you know infrastructure capabilities? so I think that's the big thing is looking at disengagement and just looking at based on the use case based on the region what are the key factors to enable adoption and and that kind of is how we determine what are the key metrics around success.

25:23 >> I'm curious >> yeah maybe I can just add to that please yeah and just just talk about the Rivian process. So we we actually do an extensive kind of release readiness process every month for our software and that involves you know many aspects to it the sort of metrics across the stack but also a key part of that is actually simulation. So we take millions of miles of real customer data and we can replay them through our stack and kind of measure the performance the safety the smoothness and all those aspects and I think all of those pieces go into that readiness report. So I think that's a very important part is having that scale of data and also the ability to to replay it and to to you know gain insights from it.

26:03 >> Charlie, you mentioned in your survey data that you cited that there's a distinction between how people feel when they're in a self-driving car that does not have a driver versus one that does have a driver. And how do you sort of bridge the gap between people's perceptions on on those two experiences? Yeah, it's a great question. I think it's understandable that someone may have less willingness to get an autonomous vehicle without a safety driver. But I think that goes to the importance of getting autonomous fleets out there. For example, some of these states that have been doing it without a safety driver. Again, was just in a Whimo in Arizona. and got out of the vehicle feeling totally safe. and I and so that kind of goes to the importance of states and and industry working together, government and industry working together to safely deploy these vehicles and provide them and you know provide the opportunity for the public to get in get in the vehicle. ideally we move from safety drivers to to non-safety drivers and fully autonomous vehicles. and that's what we're working on doing here in Michigan. But there are some challenges, right? And I think that's why Michigan feels that we are posi positioned well to be a kind of a great test bed to to move from from the testing phase to commercial operations.

27:29 For example, how do we ensure that autonomous vehicles can operate in in harsh weather conditions? In order to be able to fully to see widescale adoption of AVs, we need them to be able to operate in in harsh weather conditions, rain, snow, etc. And so, there's certain states and and Michigan definitely feels we are one of them that, you not only due to our automotive heritage, but also, just our our demographics, our our weather conditions, we are positioned well to be able to to test these different, uses and to hopefully support, that path towards commercialization while we do so with, public engagement and and providing, you know, individuals the experience to, to get in the vehicles. I always kind of go back to, you know, 10, 20 years ago, how many parents were on social media?

28:19 Not very many. I was always I was always getting flack from my parents for being on Facebook or or you know, whatever in Instagram, for example. But, you know, now my grandma's on Facebook and she's she has a smartphone and and they're on it more than I am. So I think it just takes time for adoption and for for the public to get accustomed to technology and feel comfortable in it and and I think industry is doing a great job and it's important for government to to align and to partner with industry to to make this path as smooth as possible.

28:49 >> You know it's a great point you raised and just this week we obviously had the big Amazon AWS outage that knocked out wide parts of the internet and how people go about their everyday lives. To me, this is one of the concerns perhaps for for autonomous vehicles is what happens if there's an outage and then these things don't operate the way that they're supposed to operate. what do you do? How do you adapt? so how do you how do you both think about that?

29:14 James, do you want to take that one first? >> Yeah. So, I think I think it's key to to in your safety case sort of think through these these outages that could happen in the cloud. So the the the software that's on our Rivian vehicles it it doesn't require a cloud connection. So the idea is that we can be sort of in a safe state even if that external connection goes down. So there's all the processing happens on vehicle. I think it's pretty important when you build a resilient system that you're not taking those dependencies unnecessarily or if you are that you have sort of good backup processes.

29:53 >> and Charlie, what do you think? And I would I would say I don't have a highly technical answer there. obviously that's it's a it's a challenge. We also worry about cyber security the you know grid resiliency. But one of the things that we want we try to do here in Michigan and within the office of future mobility electrification is is we reach out we we like to engage industry and to call out some of these challenges and say how can you help us solve some of these challenges that we're talking about here? for example, you know, the Amazon outage, you know, we on an ongoing basis, we like to call out or identify some of these ch these major challenges and concerns and considerations and and to work with ind industry to solve them. And that's why our our grant programs and our pilot projects have been so successful.

30:41 Identifying challenges and finding solution providers to address them and working together in public private partnerships is something that we are we've seen a lot of success in and continue to do. You know, Charlie, you mentioned you use the social media comparison 20 years ago, how people were just starting to figure out how to use social media. The older generation wasn't on it and now all of a sudden they're all on it. what do you think is the biggest variable now to achieving a fully autonomous world?

31:12 Yeah, I think I would probably say one economics. How do we make it economically feasible for the fleet operators or the auto automakers to fully integrate autonomous systems into their vehicles? I think it's going to be critical to continue supporting and seeing increased level of autonomy and consumer u vehicles and and pro production vehicles. I think that will really help you know drivers and individuals feel comfortable with the technology getting it into their day-to-day vehicle. I think that's critical and also being able to support you know commercial fleets throughout our nation and doing so and continuing to do so in a safe way. But I I think addressing some of the challenges with infrastructure and addressing some of the challenges with deploying harsh weather conditions is something that is really important. but again, I'd probably, you know, go back to how do we get this how do we get increased level of of autonomy, excuse me, into consumer vehicles and into more production vehicles. I think that's going to be a critical way of of increasing adoption.

32:30 James, what do you think? >> yeah, I think I think, you know, Charlie's right that a certain amount of this is just just time that people, you know, it takes time to try and then gain acceptance. I think it's actually changing, you know, pretty quickly. So, we we released our, you know, hands-free feature earlier this year and since then, you know, every month we've essentially seen the number of miles driven hands-free increase. So I think it's around 20% of all Rivian models now are done in that hands-free mode. So I think as these features get better, they become more capable, people become more comfortable with them, they get used to the, you know, the UI, the UX aspects.

33:06 and then also, you know, the system is learning through the data that we're able to gather during those those drive events and all of that kind of ladders into this sort of upward spiral of improvement. So, I think it's important that OEMs are able to really learn from their fleets. I think that's something very new that they haven't had to do in the past. and we think it's a it's a key competitive advantage for us. >> It's so interesting as as adoption rates increase, I have to think too that driving fatality fatalities will ultimately go down. I mean, we have 40,000 driving fatalities a year right now with cars that people drive, right?

33:46 Traditional cars. but so but at the same time though once if god forbid a whimo happens to kill someone who's crossing the street you're gonna get a there's gonna be a ton of attention on that and people are going to say computers kill people and how do you how do you go about this or or what do we do to combat this? So how do you all think about that and and the the safety element of all of this?

34:12 >> Yeah, maybe I can start. So I think we we have to recognize that the the baseline here is the average human driver and you know there's far too many fatalities in the US. I think the last stat I heard is something like a 747 full of people die every day in the US you know on the roads in the US. So I think you know for me that's the number we have to drive down. We're not saying that these systems are going to be perfect. I think that's an unrealistic expectation. In fact, I think if we have that expectation, we'll delay launching something that could be saving lives potentially now. and so I think that's the number we should focus on and drive down. we spend a lot of work, you know, Rivian on our active safety systems. so a lot of that is actually fed by the same world model investments on the ML side that are powering sort of the L2 plus features. And so really our goal is for you know Rivian vehicles to be the safest vehicles to be in and around. through those active safety features. So you can imagine almost like a safety bubble where we're preventing the vehicle from getting into these collisions and I think systems like that deployed widely on consumer vehicles will really start to move the needle on these fatality rates.

35:25 Yeah, I think it's a really important question and I think one of the important things here is to make sure that we are being with the government being in government working closely with industry to ensure that safety standards are are top-notch and are are we're aligning on you know making sure that bad actors aren't able to access autonomous vehicles or you know, in cyber security capabilities are are in place. and then making sure that we educate the community that we that the vehicles are in. I think most most communities want to know that the roads have be roads have gotten less and less safe. people, you know, technology has the ability to actually make our make our road safer and the average person is not going to drive as safe as autonomous vehicle. they are you know just to be able to trust the technology and and that's going to take some time and just constant engagement with with communities that the the vehicles are in. I think that's really important. When you talk about trusting the technology, it gets me to this broader question in the industry about what's the better approach to autonomy. Is it, you know, it's the cameras versus the LAR question. Is it using machine learning or a rules-based system? James, what do you think?

36:53 >> Yeah, I mean, I feel like those arguments have sort of largely been been settled. so I think you really want to use and leverage machine learning for as much of the driving task as possible. The reason you you should do that is because specifying driving in rules is actually it's very complicated. You end up with huge you know spaghetti code of heristics. it is actually not well specified in many cases. So you know think of the example of a lot of vehicles entering a a stop intersection.

37:21 You know there are rules on how that's supposed to be handled but that's not how humans actually navigate those things. And so to sort of encode all of that in in a rulesbased system is is almost impossible. So we believe in doing as much in the machine learning model as possible really learning from customer driving data. There's an effort we have ongoing at the moment called the Rivian large driving model and this is supposed to be an offboard huge model that can essentially learn all the nuances of human driving from all the data we receive and then but then I think you have to you have to sort of couch that in a system that provides those those guardrails, right?

37:58 So we we can use the ML as much as possible especially to to have this sort of humanistic and sort of nuanced understanding of how to drive but we can still have guardrails on that system that say okay I don't want to collide with anything I don't want to run a red light I need to be cautious in this situation and so I think it's it's that combination using ML as much as possible because that is the most scalable and sort of powerful approach but then having still a rules-based set of features at the end that you can use to kind of guarantee certain aspects about the driving behavior. And then I think on the when you come to sort of cameras versus versus lighter versus other modalities, I think for us we would say you know more modalities is better and you know Charlie was talking about you know adverse weather you see in Michigan and I think it you know it's exactly those cases where those additional modalities really really can help. so you know for us it's it's really you know can you get the right sensing sort of independence and different views of the scene at an economically you know affordable price point for consumers and that's what we're really focused on.

39:08 Yeah, and I I would say we are here in Michigan trying to to support industry and and getting to to where we don't industry and and fleets autonomous vehicles aren't relying on infrastructure, but at the same time, how do we improve infrastructure to make to in increase redundant redundancy, improve safety? and so that's kind of what the approach we're taking. How do we support machine learning and autonomous vehicles that aren't completely reliable on VTOX or you know vehicle to infrastructure communication but being able to have extra redundancy with roadside roadside units and and infrastructure technology that is able to provide kind of a backbone and additional layers of safety. we for example we have a a corridor an autonomous vehicle corridor being built built out between Detroit and Ann Arbor about a 30 m 39 mile segment of of interstate that will have vehicle to vehicle communication technology technology ve vehicle to infrastructure capabilities that will support the integration of autonomous fleets into our our normal traffic. so I think that's the approach that will will be most su successful down the line and the approach that we're taking here in Michigan.

40:29 >> You know, you've both touched on the weather issue and Charlie specifically, obviously you're sitting in Michigan where you guys have some pretty severe weather there. what I mean, how do you is this the biggest variable to achieving a fully autonomous world? Is this how do you combat severe weather conditions? I would love to hear James' thoughts from a technical perspective, but I I would say you know, Michigan, yes, we have harsh winters, but our summers are absolutely beautiful. Please come visit.

40:59 but I would say that you know how do we support new technology that might be able to help ensure that the sensors are clean if there's if in in a rainstorm for example or how do we make sure how do we support industry in new technology that provides better cameras that cameras that can work in an adver adverse condition. So that's kind of what we're doing here is is providing the the platform throughout the state to be able to test new technology and identify, you know, startups and technology providers that are working on some cutting edge technology that will help AVs work in all types of conditions.

41:39 >> Yeah, I think you know Charlie touched on some of the the things you know I think you'd start with the sensors, right? So yeah, this sensors see clean and unluded. Can they can they see the scene? you know, in some some foggy conditions, some, you know, very heavy snow, you actually can't really rely on cameras. You have to then you know, start using radars. you know, even non-weather related scenarios like, you know, the sun, a low sun, you know, shining directly into the cameras that can be challenging from a vision only. Again, that's where, you know, radar and LAR can really help. So, I think you sort of start start with that. You need to have that that kind of patchwork of sensing capability and sort of different different sensors as well.

42:18 I think then there's then the data piece is very key. So how do people actually drive in snowstorms? It's not the same way that you drive in an unluded, you know, sunny day, right? So do you have the machine learning and do you have the data flywheel that's that's telling you how to handle those situations? I think that's a that's a big advantage that a kind of fully integrated sort of databased OEM like Rivian has over for example like a rower taxi where you have to actually send those fleets to go and gather this specific data in these different conditions. and then finally yeah sort of how how do the rules you know I talked about those guardrails. Do those guardrails need to change? you know, for example, you know, when you when you've got snow on the ground, people often don't follow the lanes. They can't see them. So, you you sort of have these virtual lanes that pop up. Does that need to be taken into account in your in your guardrails? so, it's sort of it's multifaceted. I wouldn't say it's the primary challenge. I still think you know, density and complexity in urban areas is is typically where the those final big challenges are.

43:27 you know James to just reason about you know many many objects in a scene maybe there's a lot of nuance negotiation and things happening those can be tricky to handle in a good way >> on the density issue James you know I I took my first Whimo earlier this year out in San Francisco and like so many other people was just so so fascinated by it and so and it seems like wherever you turn in San Francisco there's another Whimo on every street corner but in terms of New York City it's I I just walk around here and I think to myself how are we going to have these types of cars in New York City and they're Whimo is already testing testing their fleet in in the city here.

44:02 But I mean, how do you how do you adapt to these different densities in different cities and how all the different layouts and how everything is is so different in different places? >> yeah, so I think you know you hope that your that some of your base systems obviously generalize to those places. Now, of course, there's going to be, you know, traffic specific kind of rules of the road almost that exist in that exist in New York but don't exist in San Francisco and things like that. And I think that's where the where that data flywheel really is important. And I mean, you talked about New York City, but you know, if you go outside of the US and you talk about, you know, a country like India where you know, some of the driving's you know, even more, you know, intense I would say and very different again. So you have sort of had to think like how does a system scale and I think that it really sort of tips you in favor of the MLbased approaches right because there you can gather the data you can see how people drive you can you know learn start to learn how to sort of mimic it versus you know rules based systems that can really they can be brittle so you take them to a new place and suddenly those rules don't work anymore.

45:09 James, you're a former way Whimo employee before you joined Rivian. So, I have to ask about the elephant room. I got to ask about Elon Musk and Tesla's approach to autonomous driving right now. How does Tesla compare to Whimo compared to Rivian and others? And who's who's getting it right and perhaps who's not not doing it as well? >> yes, I'm obviously not privy to, you know, what's going on inside Tesla. I think what I would say from the outside is that I think they've you know on the good side they've really sort of pushed the OEMs forwards in the sense that they took a very sort of MLbased approach early on and I think that is the right way to build these systems. Now on the on the counter side, I think they have a sort of very rigid point of view I guess on different sense modalities which I don't think is you know fully explainable just from an engineering point of view. So I would say it's sort of a a mixed bag. I think we're really focused on you know can we can we bring that L4 technology back to consumers in the best way possible. And I think you know sensors are sensors can get you there faster and they can get you there in a more robust way. And I think the the price point of a lot of these sensors is is no longer that you know liars are $10,000 because of the huge scale that you've seen in China.

46:38 Those are those are coming down to you know a few hundred which is very much in the in the envelope of you know consumer vehicles. So, I think, you know, that's that's kind of the approach we're taking and the one that I think is is going to get us there in the most sort of direct way. >> Charlie, what do you think? >> Yeah, you'll have to recap that question one more time for me, Steve. My apologies.

47:02 >> Just comparing the different approaches that automakers are taking from Whimo to Tesla. Whimo I has this reputation in the industry for taking a slower perhaps more more cautious approach to how they go about things whereas Tesla is more of a move fast and break things kind of approach here and obviously they have the robo taxi fleet that's starting to be rolled out. So just curious your thoughts on the different approaches and the pros and cons of both.

47:31 >> Yeah, you know I think we don't really have like a reference per se on on what a company's approach would be. Being with the state of Michigan's economic development department, we we want to support all companies that want to grow here in Michigan, hire here in Michigan. I think the big thing is deploying in a safe way. That's that's the most important thing. whether you're taking a a fast approach with using machine learning and and or you're taking a slower approach with a certain use case and and leveraging infrastructure. we don't really have a preference per se as I as I mentioned. It's just we are trying to to make Michigan a great place for companies to thrive and to to to deploy their technology in a safe way within communities. And so you know we are we think that we have a really strong obviously automotive industry here but how do we make sure that we we remain competitive and how do we kind of bridge the gap between legacy automotive and and the startup world in in the on the west coast and and bring the minds together to ensure that we're you know we see this widespread widespread adoption over time.

48:41 James, what what do you think is the biggest bottleneck for the robo taxi industry right now? >> I I mean I'm no longer in, you know, the robo taxi industry. So I I would say that it just takes time to scale these things. They're, you know, fleets are physical things. You have to charge them somewhere. You have to clean them somewhere. you have to you know think about power and the operational aspects and so that just takes time.

49:13 so I think and and then of course there's you know there could also be you know further engineering development that has to happen to you know maybe cover specific cases that you see in new places. So I don't see a fundamental issue but I think you know this isn't I don't know Charlie mentioned social media right it's not like just downloading an app someone has to go and build these vehicles put all the sensors on they have to be kept somewhere they have to be you know kept clean operationalized and that takes time it's like a the physical investment aspects are you know just take take time >> Charlie what do you think >> yeah you know I think identif Identifying a clear use case is important for for fleet operations.

49:57 deploying them in in a certain scenario. at least right now I think that's an important way to continue momentum and continue adoption. you know deploying them in a highly urban complex environment may not be the best best option right now. So can we identify fixed routes or can we identify certain use cases that fleets will thrive in and and it's more feasible you know at this time and for example looking at how can we partner with large employers to provide you know shuttle services between their facilities or can we deploy autonomous vehicles at airports from the parking to the terminals.

50:37 looking at deploying autonomous vehicles on at universities to get students engaged in to experience that technology. I think that's the that's a really critical way at this juncture to continue, you know, supporting fleet operations before we see them in in highly complex multimodal environments. >> I'd like to shift gears a little bit. we could do a little bit of a a lightning round here on some of your predictions for what you guys think is going to come true in this industry in the coming years. So, I have a my older son is seven years old. In 10 years, he'll be eligible to get his driver's license here in New York. Will he need it and will he even want one? James, what do you think?

51:22 >> so, I I grew up in London and even without autonomous vehicles, you know, London has a fantastic, you know, public transport system. So when I was young and sticking in the city you know I didn't need to drive and actually I took my driver's license quite late. I would say once you have a family and you start needing to get from A to B that's when the real value of you know personally owned vehicle comes. So I I suspect your son will actually still get a driver's license even if his early years are you know in in a rubber taxi.

51:55 But I think the world in which the vehicle he'll be driving in I think will be much safer. It will have you know many more autonomy modes. you as a parent, this is actually you know feature I'm excited about you know may be able to engage a teenager mode right which puts the vehicle in a in kind of like a extra safe state. so that you know your son can't can't speed or you know do donuts in the parking lot or whatever else he wants to do. and so, yeah, I think that's probably most likely. I I don't see a huge shift away, from the automobile, at least in the US, just because of, you would also need a a commensurate shift in, you know, where houses are built and how people are living and everything else. but I do think that, consumer autonomy will will become, essentially a, you know, a must have on every vehicle.

52:52 >> I would say must have on every vehicle. >> Your son will. >> Yeah, I would say your son will probably need to get a driver's license. And I think that's okay. I think we we're going to continue to see more AVs on the roads, but it's going to be a little bit longer down the line until you you don't need a a driver's license. I actually p personally enjoy driving from time to time. there's also times where I I I would love to get an autonomous vehicle and not drive to get work done or to to get some rest, for example. but I think that's a little ways out and it really depends on where you live your lifestyle as as James alluded to. If you're if you live in a rural community, most likely you're going to, you know, at least near-term over the next 5 10 years still want need a driver's license, want a driver's license. If you live in a highly urban area, maybe that's not the case.

53:42 What percentage of cars on the road will be autonomous versus not in let's say 10 years from now? >> 20%. >> What are we at right now? >> And what are we at right now? >> That's a good question. James, do you know? >> I would say sort of define autonomous. So, do you mean like a full robo taxi level autonomy or do you mean vehicles that for example could provide an L3 capability that gives you your time back on the road and makes makes driving safer? So, I think I think that's where there's actually a big spectrum of autonomy here and >> I I think we're I think people focus on the endpoint. but I think there's actually many other customer societal benefits you get on the way there.

54:30 so I think, you know, to be honest, I think every vehicle, new vehicle sold in 10 years time to be competitive will have to have, you know, close to best-in-class autonomy. I think it's becoming more and more of a consumer preference. We we see actually much higher conversion rates when people try our autonomy features in the in the stores. I think it's something like a 3x conversion rate. And so I think I think that that shift is really happening. And I think as you see, you know, robo taxis roll out, people will just expect and demand more and more. So I think I think this this tide is coming and OEMs need to be ready.

55:06 >> What what matters more better data or the algorithm? >> I think if you had to I think if you had to pick one, you would pick the data. but to make the best use of the data you need, you know, excellent, you know, machine learning engineers and approaches to to understand that data and to learn you know, how to drive from it essentially.

55:39 But the data is the most important. If you don't have the data, I think you you there's no getting around it really. So, you know, if you have if you don't have the Michigan snowstorm, I don't think there's any feasible way you could build an autonomy system that then can handle those Michigan snowtorrms. You have to go there and see the data. >> And actually, just sticking on that, James, Rivian is doing a lot in the Gen AI space. And so, do you want to talk just a little bit about how are you using Gen AI to, improve the autonomy that you guys are offering in your vehicles?

56:10 >> Yeah. Yeah. So I think here's maybe two sens in which you know we we use genai. So I think one is in this like large driving model that I alluded to earlier. So that is actually a very large transformer-based model. It looks a lot a large language model in the sense that you have you know data coming in in the LLM space. It's it's text in our in our side. It's really sensor data raw sensor data. And then you have these large transformers that that kind of chew on that data. And then at the end we we generate tokens. And now these tokens are not words or or you know letters in the LLM case. They're actually little snippets of trajectories and we kind of we piece them together and that gives you the the best the model's best interpretation of the future driving path. so I think you know that that model is very heavily inspired by a lot of the work that's happening on LMS and of course we we kind of slipstream on the work that's happening there. I think that's one sense. The other sense is we're also you know seeing a big sort of productivity boost from using some of these genai tools. I would say not replacing people but really making them more effective. So you know improving the velocity that you can write code that you can test code that you can do things like code review and and you know interface with APIs and things like that. So I I think yeah it it is an accelerant and and definitely on both sides. I think very essential to the work we're doing on autonomy.

57:40 >> Charlie, what's the biggest myth that you'd like to debunk about self-driving? >> That the techn is not ready. I think the technology industry has been incredible. At least I think in the United States, you know, the technology being developed by industry, by academia is has gotten us to a point where it's ready. Let's let's get these techn let's get these vehicles out there and get let's allow people to experience them. but I that's the big thing. I think too many people think that the technology is not ready, it's not safe, but I think that's just the opposite.

58:16 >> James, what do you think? >> Yeah, I you know, plus plus one to Charlie. I think yeah I I think we we we just need to get more people experiencing what is out there and I think we you know we need to push US OEMs to to be more tech forward you know you see how the level of autonomy features that are present in Chinese OEMs and I think you know I kind of feel like OEM's got a little bit complacent in in that regard and I think we have to push everyone forwards so that's what we're, you know, we're trying to do here at Rivian.

58:55 >> And finally for you, James, what's one word to describe the state of self-driving technology today? >> I think on the cusp, I know it's not one word, but it's, you know, yeah, one idea. So, I think it's really on the cusp where you you're seeing significant deployments in certain cities and I think in in dense metros, I think that will happen quite quickly. And then I think you'll see a trickle down into consumer vehicles for most other trips >> on the cusp. I like that. Charlie, what do you think? One word to describe the state of self-driving technology today >> here. I think it's here and we're going to see it more and more every day.

59:36 >> Well, thank you James and Charlie for these insights. and to everyone who joined us today for inside self-driving. I also want to thank our partner MobileI for their support of today's program. I'm Steve Russell from Business Insider. Thank you for being with us. We'll see you next time.