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Speaker 1: This is Dana Perkins and you're listening to Switched on

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Speaker 1: the BNF podcast. Autonomous vehicles might be the cars of

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Speaker 1: the future, but following a flurry of venture capital and

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Speaker 1: private equity funding about a decade ago, investment in the

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Speaker 1: technology is looking a little more tempered this last year,

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Speaker 1: failing to reach four billion US dollars for the first

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Speaker 1: time since twenty seventeen. While Apple has taken a step back,

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Speaker 1: green shoots have come in the form of Tesla's Robotaxi announcement.

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Speaker 1: In such a cash intensive industry, what autonomous vehicle strategies

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Speaker 1: and technologies are appealing to car manufacturers well? To tell

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Speaker 1: us more about this, on today's show, I get to

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Speaker 1: speak with bnaf's head of Intelligent Mobility, Andrew Grant. We

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Speaker 1: talk about the adoption of autonomous vehicles across different regions,

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Speaker 1: such as robotaxis in China. We also review the different

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Speaker 1: levels of automation that are available on the market and

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Speaker 1: discuss Tesla's potentially controversial decision to continue with cameras as

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Speaker 1: sensors while other parts of the market are embracing the

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Speaker 1: more expensive light our technology. B ANDAF subscribers can find

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Speaker 1: Andrew's recent research note at BNAF dot com or at

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Speaker 1: BNF on the Bloomberg terminal. It's titled driving the next

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Speaker 1: phase of electric Mobility in Europe. If you want to

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Speaker 1: receive an update when we publish a future episode of

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Speaker 1: Switched On, make sure to subscribe on Apple Podcasts or

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Speaker 1: Spotify or wherever you get your podcasts and give us

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Speaker 1: a review to share us with others. But right now,

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Speaker 1: let's jump right into our conversation with Andrew about the

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Speaker 1: outlook for the automated vehicle sector. Andrew, thank you for

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Speaker 1: joining on the show today.

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Speaker 2: Nice to be here, Thanks Stanna.

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Speaker 1: We are here to talk about autonomous or automated vehicles,

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Speaker 1: depending upon how you want to refer to them, and

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Speaker 1: some of the developments in that space. Now, before we

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Speaker 1: get to the Jetson's Cars of the future and where

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Speaker 1: everybody's head kind of immediately goes when we think of

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Speaker 1: autonomous vehicles, let's talk about those kind of at the

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Speaker 1: lower level of the computer intervention here. So let's go

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Speaker 1: through level zero through five. Can you give me a

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Speaker 1: bit of a definition first of all, what an autonomous

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Speaker 1: vehicle is and then go through those different numbers of autonomousness.

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Speaker 2: Great way, of putting it down and thanks. Yeah, So

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Speaker 2: definitions are very important in this space. Consumers need to

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Speaker 2: know who's in charge of the vehicle as it's operating

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Speaker 2: on the road. So there's various levels of automated driving

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Speaker 2: that are defined by SAE International. That is the institution

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Speaker 2: that used to be known as the Society for Automotive Engineers,

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Speaker 2: and they have what essentially are six levels of driving automation.

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Speaker 2: I'll simplify that a little bit just for our purposes

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Speaker 2: because it's useful to just bundle these into different categories.

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Speaker 2: So you have your level zero and level one, where

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Speaker 2: there's either no assisted driving or just partially assisted driving

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Speaker 2: that will help the human driver on the road.

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Speaker 1: So is this the old school cruise control that's been

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Speaker 1: around since my mother was driving me around?

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Speaker 2: Yeah, exactly. Get up to a certain speed, lock it

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Speaker 2: in and you should be should be fine. Then you

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Speaker 2: get to your sort of more sophisticated driving, your advanced

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Speaker 2: driver assistant systems that start to come in at what

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Speaker 2: is known as level two and level three partial and

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Speaker 2: conditional automated driving. So in these circumstances, the human driver

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Speaker 2: is still the one that is in control of the vehicle.

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Speaker 2: The computer and the self driving system are really an

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Speaker 2: extension of that human driver. They are meant to help

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Speaker 2: out make the driving a lot safer, but ultimately the

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Speaker 2: onus is on the human driver to be in control. Now,

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Speaker 2: at level three conditional automation, that means at certain stages

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Speaker 2: and in very specific conditions which hopefully the vehicle manufacturer

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Speaker 2: makes very very clear to the human driver, the computer

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Speaker 2: is able to take over and do the self driving

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Speaker 2: or drive the vehicle itself.

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Speaker 1: So what's an example of this is the self parking

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Speaker 1: or is this if you're about to get in an accident,

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Speaker 1: it breaks for you.

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Speaker 2: Yeah, so that parking is part of this, or automating

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Speaker 2: parking where you don't actually have to do anything and

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Speaker 2: the vehicle is going to do the parking for itself.

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Speaker 2: But also it's specific location, so say on a controlled

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Speaker 2: section of highway that's been mapped very thoroughly. Mercedes Benz

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Speaker 2: has come out with a product where it is marketed

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Speaker 2: as a level three self driving system and under those conditions,

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Speaker 2: it's the computer that is under control of the vehicle,

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Speaker 2: but it can still notify the human driver that it

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Speaker 2: needs to come back in and take control of the vehicle.

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Speaker 1: Okay, so I've got an idea of here we are

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Speaker 1: zero to three. Now as we get up to four

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Speaker 1: and five, what starts happening Now.

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Speaker 2: You're talking about a different approach to the automation of

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Speaker 2: the vehicle. What you have here is highly and fully

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Speaker 2: self driving vehicles, and we just tend to bundle four

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Speaker 2: and five together. That's really where it gets really sophisticated

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Speaker 2: self driving technology. The computer is in control, it's doing

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Speaker 2: the driving, and the human can do other things while

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Speaker 2: in the vehicle.

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Speaker 1: And what are some examples of cars on the road

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Speaker 1: right now that are at that level four or five.

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Speaker 2: Well, there's a few cases of this, so probably most prominently,

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Speaker 2: which is the spin off from Alphabet Google their self

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Speaker 2: driving car project. So weimo's operating in Phoenix, Arizona, in

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Speaker 2: San Francisco and testing in a few other places throughout

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Speaker 2: the US. These are level four highly autonomous vehicles that

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Speaker 2: are capable of operating without any human in the vehicle.

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Speaker 2: They have a bunch of sensors, very sophisticated computing under

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Speaker 2: the hood of the car, and they do self driving

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Speaker 2: on their own. There's a few other companies that are

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Speaker 2: kind of pushing towards this space cruise. GM owned company

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Speaker 2: was operating at the sort of level before an incident

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Speaker 2: last year. And then there's a variety of companies throughout

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Speaker 2: China that are operating at this level. So companies like

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Speaker 2: Ponyai by do Or also operating these sorts of level

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Speaker 2: four highly autonomous robotaxis that are available for public use.

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Speaker 1: Okay, so we have a lot of different topics to

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Speaker 1: get through today, and everyone hold on to that zero

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Speaker 1: through five. Zero being low, five being high, and the

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Speaker 1: different levels of autonomous vehicle that working to talk about today.

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Speaker 2: But if it helps to simplify it a little bit,

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Speaker 2: we just like to think of it as you've got

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Speaker 2: your partially andconditional automated, that's your aid ass and then

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Speaker 2: you've got your highly and fully automated that's your robotaxi.

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Speaker 2: So those kind of two simple buckets of self driving.

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Speaker 2: They're very different implications. They have different use cases, different

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Speaker 2: financial opportunities. So we kind of simplified into those those buckets.

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Speaker 1: That does simplify it quite a bit. So we've got

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Speaker 1: it in these two categories. Let's talk about the money

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Speaker 1: for a second though. So about a decade ago, there

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Speaker 1: was a flurry of activity and the VC space, and

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Speaker 1: some of the PE companies were as well investing in this,

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Speaker 1: And what I want to know is where does it

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Speaker 1: stand now in terms of investment and kind of have

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Speaker 1: those original VC investments turned into bigger investments. Have they

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Speaker 1: gone through the startup value of death? That sounds like

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Speaker 1: there are a lot of fairly established companies that are

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Speaker 1: actually trialing a lot of this technology right now in

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Speaker 1: different parts of the world. But is it as much

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Speaker 1: in the investor's i as it was, say a decade ago.

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Speaker 2: Well, the simple answer is no. So a lot of

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Speaker 2: money's flowed into this space. You're talking about seventy five

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Speaker 2: billion in private equity and bention capital investment that we've

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Speaker 2: tracked over the last ten years up to the start

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Speaker 2: of twenty twenty four. And that really peaked in sort

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Speaker 2: of twenty nineteen when a lot of investors were getting

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Speaker 2: on the idea of a general self driving computer that

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Speaker 2: could operate multiple applications. It could do a robotaxi and

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Speaker 2: transport people. It could ultimately be applied to trucking. This

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Speaker 2: driver could go anywhere and do anything. And then what

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Speaker 2: we sort of had for the years since then is

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Speaker 2: as there's been slightly less money flowing into the space,

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Speaker 2: companies have had to come up with more specific use

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Speaker 2: cases for their technology. They were pitching for very specific

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Speaker 2: applications of their technology. So while them was less investment

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Speaker 2: flowing into the space in general, it was more targeted

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Speaker 2: and more directed towards companies that actually were developing a

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Speaker 2: business model. They weren't just going to solve self driving

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Speaker 2: in general, they were going to solve a specific pain

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Speaker 2: point for consumers or businesses. So funding's dropped off. It

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Speaker 2: was about four billion dollars that we tracked that went

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Speaker 2: into the space last year. That's not small by any means,

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Speaker 2: but significantly down from what we've seen previously. However, there's

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Speaker 2: been some big announcements this year. Tesla's gone all in

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Speaker 2: on this space. This obviously doesn't count towards venture capital

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Speaker 2: investment because Tesla's are one of the biggest companies in

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Speaker 2: the world, but they're spending about ten billion dollars this

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Speaker 2: year just building out the self driving compute systems to

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Speaker 2: try solve their self driving algorithms. Well, recently there's been

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Speaker 2: some decent sized funding deals. So Applied Intuition raised two

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Speaker 2: hundred and fifty million dollars through one of its funding rounds,

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Speaker 2: and then quite recently Waive their British owned companies raised

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Speaker 2: one billion dollars, which is a funding amount that at

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Speaker 2: a Series C stage is something we haven't seen in

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Speaker 2: this space in a little while. So nice to see

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Speaker 2: those big billion dollar deals coming back.

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Speaker 1: I can certainly see how from the end user standpoint,

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Speaker 1: whether it's in a taxi or in a car you're

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Speaker 1: driving yourself, that if this technology is working effectively, it's

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Speaker 1: safer and you're ultimately in a vehicle that will you know,

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Speaker 1: make fewer human errors assuming all goes right. Some companies

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Speaker 1: have been decreasing their activity, while others have really been

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Speaker 1: dialing up how their focus is on autonomous driving. You'd

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Speaker 1: mentioned Tesla was one of them. Can you talk about

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Speaker 1: some of the other automakers that are in this space

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Speaker 1: who are adopting this technology and really what their strategy

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Speaker 1: is and what the use cases and why they think

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Speaker 1: it's going to give them a competitive edge.

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Speaker 2: Yeah, definitely. So what's really interesting, maybe if I tie

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Speaker 2: this back to why we actually are talking about self

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Speaker 2: driving vehicles in what the listeners are are, we'll well

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Speaker 2: know is very a Decarbonization Focus podcast and all the

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Speaker 2: work we do at B and F is very decarbonization focused.

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Speaker 2: Self driving vehicle technology is key to many automakers strategies.

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Speaker 2: If you are investing in self driving vehicle development, that

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Speaker 2: means you may not be investing in some of the

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Speaker 2: other things that you need to do to become an

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Speaker 2: automaker of the future. In Tessa's case, they have laid

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Speaker 2: off a big chunk of this supercharger division and are

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Speaker 2: devoting more of their resources towards self driving vehicle developments.

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Speaker 2: Other automakers have gone the other way, where they have

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Speaker 2: kind of cooled it a bit on some of their

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Speaker 2: self driving vehicle ambitions and are focusing more on, say,

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Speaker 2: operating more connectivity services. Maybe they are focusing more on

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Speaker 2: aid asas and developing technologies that they can actually sell

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Speaker 2: to consumers these days. Where this becomes really interesting is

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Speaker 2: are you holding out for kind of a long term

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Speaker 2: revenue and profit potential of some of your highly automated

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Speaker 2: vehicle systems, or are you going to try make most

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Speaker 2: of your money from aid asas and selling partially in

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Speaker 2: traditional automation to consumers today. So I would say the

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Speaker 2: majority of traditional automakers are tending to focus on those

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Speaker 2: level two level three self driving systems and selling the

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Speaker 2: host to consumers because as some of the modeling that

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Speaker 2: we've done shows, that is a big potential revenue driver.

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Speaker 1: How much do these systems cost and are they only

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Speaker 1: found in luxury vehicles, because you know, if it sounds

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Speaker 1: like it's the technology of the future, I'm going to

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Speaker 1: guess that it's actually going to be a real premium

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Speaker 1: and you're actually thinking about a vehicle.

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Speaker 2: I mean, it's a definite added cost. It can be

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Speaker 2: ranging from a couple hundred dollars for your level two

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Speaker 2: systems up to a few thousand dollars for your more

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Speaker 2: sophisticated level two what we can sometimes refer to as

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Speaker 2: level two plus and level three systems. So that's a

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Speaker 2: cost that sometimes the automaker will want to incorporate into

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Speaker 2: the purchase price of their vehicle and sell it to

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Speaker 2: the consumer with that price baked in, and sometimes they'll

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Speaker 2: want to charge a separate amount for that. And we

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Speaker 2: keep talking about Tessa, but there's a really good example

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Speaker 2: of this with the autopilot and the full self driving

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Speaker 2: software package which I'm using inverted commas. There for full

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Speaker 2: self driving because it is not in fact a full

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Speaker 2: self driving software package. It is a level two partial

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Speaker 2: automation package, and they charge between eight and ten thousand

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Speaker 2: dollars for that to purchase that package and use it

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Speaker 2: within their vehicle. They have recently just cut the price

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Speaker 2: on that down to eight thousand dollars, but it's a

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Speaker 2: significant chunk that's added on top of the vehicle that's

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Speaker 2: kind of depending on which type of test that you're buying,

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Speaker 2: that's three to twenty percent of the cost of the vehicle.

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Speaker 1: Given that this is a show that focuses on how

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Speaker 1: decarbonization is changing the industries that we cover, to what

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Speaker 1: extent do you find that autonomous driving is included in

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Speaker 1: with a car that is an electric vehicle? Do you

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Speaker 1: tend to find these together or really is autonomous driving

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Speaker 1: across any number of cars and there's no strong correlation.

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Speaker 2: Or there's definitely a very strong correlation between vehicles that

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Speaker 2: are electric and vehicles that are self driving. So, first off,

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Speaker 2: at the aid as level, a lot of the new

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Speaker 2: vehicles that are being designed and packaged together, those tend

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Speaker 2: to be electric vehicles. And while you're designing a new vehicle,

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Speaker 2: it's much easier to add new technologies to that vehicle

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Speaker 2: than it is to take an existing vehicle and then

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Speaker 2: kind of pile this new tech on top of it.

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Speaker 2: So inherently the newer vehicles out there, the electric vehicles

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Speaker 2: also tend to have aid AAS and partially automated features

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Speaker 2: as well. But then what we've observed with some of

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Speaker 2: the robotaxis there and the testing data that's out there,

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Speaker 2: a really high share of those vehicles are electric, to

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Speaker 2: the degree of about ninety percent of the kilometers traveled

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Speaker 2: in autonomous mode in California. Amongst all the companies that

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Speaker 2: are operating there, and there's a list of about forty

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Speaker 2: companies that are permitted to operate in California, ninety percent

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Speaker 2: of those cometss were done now with electric drive trains.

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Speaker 1: Why do you think that is?

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Speaker 2: I mean a few different pressures. So one, you are

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Speaker 2: going to be bringing in well a taxi service into

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Speaker 2: an urban area, So it kind of makes sense if

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Speaker 2: you are bringing it vehicle into an urban space, you

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Speaker 2: might have some regulators that are looking at you with

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Speaker 2: a close eye. Probably helps a little bit to have

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Speaker 2: an electric vehicle as part of that. There are just

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Speaker 2: going to be a lot more electric vehicles in the future.

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Speaker 2: It makes sense to build on a drive train that's

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Speaker 2: going to be more prevalent. And then there are some

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Speaker 2: technical aspects to this as well. So if you have

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Speaker 2: a big battery, you are running a big computer on

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Speaker 2: these batteries as well, so you're going to have to

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Speaker 2: have a big battery in any case.

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Speaker 1: You've brought up the technology a few times. And then

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Speaker 1: also Tesla, which then brings me to the different competing

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Speaker 1: technologies that are being used at the moment. So Tuessla

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Speaker 1: uses these cameras around the vehicle. And then there's lighter,

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Speaker 1: which can you actually first explain what lightar is compared

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Speaker 1: to a camera based technology, and then can we discuss

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Speaker 1: kind of which ones are being used where and with

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Speaker 1: what types of companies.

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Speaker 2: So LIGHTER is light detection and ranging. Think of it

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Speaker 2: just as a laser, So it's a different type of

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Speaker 2: sensor that's used in these types of applications. Of all

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Speaker 2: the robotaxis that are currently on the roads, they all

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Speaker 2: use LIGHTER. It's a great sensor for practical purposes. It's

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Speaker 2: got great range, it can operate in a lot of

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Speaker 2: different weather conditions. It creates a very reliable three D

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Speaker 2: map of what's happening in and around the vehicle and

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Speaker 2: can really complement some of the other sensors that you're

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Speaker 2: using in this space, so it will complement what the

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Speaker 2: cameras see as well. The disadvantage of it is that

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Speaker 2: it is very expensive. So the initial versions of this

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Speaker 2: were uputs of thirty five thousand dollars for a single sensor.

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Speaker 2: That's come down signif evantly and the technology has changed.

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Speaker 2: It's gone from being one very pricey sensor that would

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Speaker 2: kind of sit on top of the vehicle, it would spin,

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Speaker 2: it would do a lot of different things. Now light

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Speaker 2: or developers have kind of broken that up into a

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Speaker 2: bunch of smaller versions that are positioned throughout the car

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Speaker 2: or around the car, and incollective do the same thing

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Speaker 2: but for a fraction of the cost. Now, for some developers,

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Speaker 2: and Tesla being one of them that I would mention,

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Speaker 2: are in the minority of the autonomous vehicle space, the

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Speaker 2: price hasn't come down quick enough, and they don't see

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Speaker 2: it coming down quick enough. You're talking still even at

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Speaker 2: the kind of the best end, it's about five hundred

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Speaker 2: dollars for one of these sensors, you need a couple

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Speaker 2: of them on a vehicle. Some developers use up to

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Speaker 2: eight of those, so it's still an added cost on

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Speaker 2: the vehicle that pushes up the price. And on top

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Speaker 2: of that, you still need the cameras to be doing

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Speaker 2: a good job. So the approach of Tesla and one

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Speaker 2: or two other developers in the space is to rely

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Speaker 2: on the cameras to create this version of what's around

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Speaker 2: the vehicle and what's ahead of the vehicle, and not

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Speaker 2: rely on these sensors that can push up the price

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Speaker 2: of the vehicle.

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Speaker 1: So, given that Tesla's continued with the less expensive but

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Speaker 1: the technology that's traditionally been working for them, is there

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Speaker 1: a risk is light er costs continue to decline and

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Speaker 1: the adoption becomes more prevalent, that Tesla could get left

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Speaker 1: behind in this technology or at some point need to

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Speaker 1: adopt it themselves.

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Speaker 2: I mean, Elon Musk has been very clear that he

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Speaker 2: has no intention of allowing the incorporation of lidar, in

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Speaker 2: fact any other sensors long range radar for dy imaging

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Speaker 2: radar within the Tesla self driving ecosystem. So it doesn't

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Speaker 2: seem likely that Tesla would be doing that they are

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Speaker 2: taking a gamble that they will solve self driving with

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Speaker 2: their advanced computing that they're building out and spending all

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Speaker 2: that money on. There is the potential that while they

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Speaker 2: are working on that algorithm and pushing it ahead, they

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Speaker 2: do just get beaten out by companies that are willing

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Speaker 2: to lean on some of these other sensors and maybe

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Speaker 2: take the additional cost of pushing up the hardware price

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Speaker 2: of their vehicles. But there's also the option of, at

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Speaker 2: some state which I would never discount, that Tesla does

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Speaker 2: a bit of a U turn and ultimately starts to

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Speaker 2: incorporate some of these sensors as the costic lines. There's

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Speaker 2: a report that's just come out recently that Tesla purchased

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Speaker 2: two million dollars worth of LDAR from Luminar Prominent Ladder

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Speaker 2: supply last year. So this is something that they are

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Speaker 2: testing with, but all statements coming out of the company

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Speaker 2: are that they have no plans to put this into

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Speaker 2: their vehicles. So waiting with beta breadth for August eighth,

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Speaker 2: which is the announced dates of the Tesla Robotaxi being unveiled.

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Speaker 1: Great. So, now that I have a good feel for

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Speaker 1: the technology, I want to pivot a little bit to

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Speaker 1: the regional dynamics. You know, we do this event in

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Speaker 1: San Francisco every year, which is the BNEF Summit actually

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Speaker 1: that takes place there and it's very focused on transportation.

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Speaker 1: A lot of the people at the summit, both our

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Speaker 1: team and the different clients that we have at the event,

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Speaker 1: we're definitely in a lot of these robotaxis. So I

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Speaker 1: saw it as a very real thing. There Where is

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Speaker 1: the adoption? Am I right in thinking that the US

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Speaker 1: is a big adopter of the autonomous driving technology? And

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Speaker 1: outside of the US, also where else do we find it?

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Speaker 2: I mean two definite leaders in the space, the US,

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Speaker 2: with some states in particular being well ahead of others,

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Speaker 2: and in China. So different approaches to how these vehicles

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Speaker 2: are being deployed, and most of that's coming from a

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Speaker 2: regulatory front, but you are looking at places like kind

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Speaker 2: of southern states across the US where there are robotaxis

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Speaker 2: being deployed, not just robotaxis, also some self driving trucks

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Speaker 2: that are being tested to a fairly significant degree. And

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Speaker 2: then California with San Francisco and the Bay Area being

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Speaker 2: the tech hub that it is, it's a prominent area

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Speaker 2: for robotaxi testing and deployment, and then across China. So

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Speaker 2: a lot of the major cities in China, ranging from Beijing, Shanghai, Muhan,

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Speaker 2: are significant autonomous vehicle test hubs, but working quite closely

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Speaker 2: with the regulators to deploy some of these vehicles.

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Speaker 1: So talk to me a little bit about the testing.

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Speaker 1: How does that work as a test hub? You have

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Speaker 1: this geo fenced area that the vehicles then ultimately operate

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Speaker 1: in and then they collapse to a certain number of

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Speaker 1: miles or people inside or people not inside. How do

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Speaker 1: you go about testing the technology?

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Speaker 2: For the most part, that comes down to the relationship

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Speaker 2: with the regulators. This is a new space. There's a

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Speaker 2: lot of back and forth between companies that are looking

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Speaker 2: to deploy these types of technologies and the regulators. They

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Speaker 2: end up having to go through what we call a

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Speaker 2: bit of a policy gauntlet. You I'll use California for

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Speaker 2: an example. You end up having to get a first

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Speaker 2: a testing permit from the California Department of Motive Vehicles

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Speaker 2: that allows you to drive around with a human driver

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Speaker 2: supervising in the vehicle. Then you can get a driverless

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Speaker 2: testing permit where you are allowed to take the driver

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Speaker 2: out the vehicle. Then you are ultimately allowed a deployment

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Speaker 2: permit from the California Department of Motive Vehicles and that

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Speaker 2: lets you put your vehicles into into operation. But then

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Speaker 2: you also need the California Public Utilities Commission to give

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Speaker 2: you permission to transport passengers, either at a testing phase

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Speaker 2: or for for commercial reasons, so you need a bunch

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Speaker 2: of different permits. Then you might need to go to

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Speaker 2: the federal level and get permission. If you are using

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Speaker 2: a custom designed vehicle as your robotaxi, you need the

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Speaker 2: federal government to sign off on being able to operate

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Speaker 2: that vehicle on public roads. So once you've got this

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Speaker 2: collection of permits, then you need to go to the

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Speaker 2: local governments and work with them to make sure you're

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Speaker 2: not disrupting things that they're trying to do. And they

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Speaker 2: have ways to limit and control what robotaxes are deployed

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Speaker 2: and how they're deployed. So these companies are building up

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Speaker 2: big policy teams and regulatory teams that are working with

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Speaker 2: regulators to design the right type of regulation. But it's

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Speaker 2: really kind of iterative and step wise, and you kind

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Speaker 2: of can't really go from Hey, I've built this technology

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Speaker 2: in a lab and then two weeks later, I've deployed

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Speaker 2: it on streets and people are using those robotaxes. It's

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Speaker 2: very iterative and it's very building up the process over time.

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Speaker 1: So let's stay on the robot taxis for a minute.

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Speaker 1: One of the things when we think about decarbonization is

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Speaker 1: the fact that autonomous vehicles could drive down the need

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Speaker 1: for car ownership because you can have more vehicles available

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Speaker 1: when you need them. And also these robotaxis, well the

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Speaker 1: driver doesn't need to sleep. They maybe need to charge

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Speaker 1: at their battery yet, but they don't need to sleep,

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Speaker 1: so you have much longer use of the vehicle throughout

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Speaker 1: the day. What impact does the autonomous driving technology have

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Speaker 1: on the number of miles traveled for some of these vehicles.

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Speaker 2: That's a great question and something that we put into

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Speaker 2: our long term electric vehicle outlook. This can be a

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Speaker 2: kind of a foundational element in the number of vehicles

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Speaker 2: that you need on public roads in the long run.

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Speaker 2: So since the vast majority of robotaxis in our view,

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Speaker 2: will end up being shared vehicles, they will either be

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Speaker 2: owned by a company or a central entity and shared

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Speaker 2: amongst many users. That means that they will be traveling

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Speaker 2: more than those privately owned passenger vehicles that will sit

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Speaker 2: in driveways or parking lots most of the day. Our

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Speaker 2: estimate is that it's between three and five times the

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Speaker 2: annual mileage of your private passenger vehicle. That means you

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Speaker 2: need few of them to provide the same amount of

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Speaker 2: mobility to consumers. So there are some benefits to that.

438
00:21:56,359 --> 00:21:59,159
Speaker 2: If it's managed properly, you don't have to kind of

439
00:21:59,160 --> 00:22:01,120
Speaker 2: have so many vehicles on the streets. You maybe don't

440
00:22:01,160 --> 00:22:03,359
Speaker 2: need so much parking that you can use for for

441
00:22:03,400 --> 00:22:06,120
Speaker 2: other applications. There is a bit of a risk that

442
00:22:06,160 --> 00:22:08,840
Speaker 2: some people are concerned about in that you would have

443
00:22:08,920 --> 00:22:11,800
Speaker 2: a lot of what they call deadhead miles, which would

444
00:22:11,800 --> 00:22:16,240
Speaker 2: be vehicles that are circulating looking for passengers, not parked,

445
00:22:16,280 --> 00:22:18,840
Speaker 2: but just driving around and nobody's using them. So that's

446
00:22:18,880 --> 00:22:20,880
Speaker 2: something that's worth paying attention to.

447
00:22:21,160 --> 00:22:23,160
Speaker 1: Which you do end up seeing with taxis and cities

448
00:22:23,200 --> 00:22:23,840
Speaker 1: all over the world.

449
00:22:24,000 --> 00:22:26,600
Speaker 2: Yeah, exactly. And there's a lot of different ways to

450
00:22:26,720 --> 00:22:28,800
Speaker 2: drive down those numbers, and you can kind of look

451
00:22:28,800 --> 00:22:31,119
Speaker 2: at some of the databack solutions that some of the

452
00:22:31,200 --> 00:22:33,600
Speaker 2: ride hailing companies are working with and they've had some

453
00:22:33,640 --> 00:22:37,120
Speaker 2: success in driving down deadhead because ultimately nobody wants dead head.

454
00:22:37,320 --> 00:22:39,720
Speaker 2: The companies that are operating these vehicles, the drivers that

455
00:22:39,720 --> 00:22:42,479
Speaker 2: are operating these vehicles don't want that as well. They

456
00:22:42,480 --> 00:22:45,919
Speaker 2: are not earning while they are driving without anybody in

457
00:22:45,960 --> 00:22:48,000
Speaker 2: the vehicle, so they're not trying to do that, and

458
00:22:48,000 --> 00:22:50,119
Speaker 2: they want to limit those those types of miles.

459
00:22:50,280 --> 00:22:52,680
Speaker 1: I can't help but have a chuckle because my father

460
00:22:52,800 --> 00:22:55,719
Speaker 1: is a grateful dead fan, and they are referred to

461
00:22:55,760 --> 00:22:58,879
Speaker 1: as dead heads. So it is a sign of the

462
00:22:58,920 --> 00:23:01,440
Speaker 1: times that we are using that term in a very

463
00:23:01,480 --> 00:23:03,919
Speaker 1: different way. Right now, let's talk about some of the

464
00:23:03,960 --> 00:23:06,160
Speaker 1: growth markets. So you had mentioned that adoption is quite

465
00:23:06,240 --> 00:23:08,919
Speaker 1: high in the US, China coming is in a close second.

466
00:23:09,240 --> 00:23:11,720
Speaker 1: Where are we also seeing kind of the frontier markets

467
00:23:11,760 --> 00:23:14,080
Speaker 1: for this technology, and where do you think we'll see

468
00:23:14,080 --> 00:23:15,160
Speaker 1: growth in the medium term.

469
00:23:15,480 --> 00:23:20,159
Speaker 2: I think anyway where there's a dense urban environments that

470
00:23:20,240 --> 00:23:24,520
Speaker 2: meets a few criterias, so well marked streets, decent weather

471
00:23:24,640 --> 00:23:27,159
Speaker 2: is a big help for some of these robotaxi applications.

472
00:23:27,400 --> 00:23:30,600
Speaker 2: And then governments that are I wouldn't say necessarily willing

473
00:23:30,640 --> 00:23:34,600
Speaker 2: to be flexible, but are thinking about regulations in this

474
00:23:34,680 --> 00:23:37,640
Speaker 2: space in a thoughtful way. That's kind of the minimum

475
00:23:37,640 --> 00:23:42,520
Speaker 2: criteria for this technology to accelerate and be deployed. So

476
00:23:42,560 --> 00:23:45,960
Speaker 2: there's a few places where that is true, but yet

477
00:23:46,040 --> 00:23:49,199
Speaker 2: more kind of regulatory developments is needed.

478
00:23:49,640 --> 00:23:51,800
Speaker 1: Well. And then let's talk about a growth market for

479
00:23:52,040 --> 00:23:55,600
Speaker 1: Tesla specifically, So they have set up a pretty large

480
00:23:55,680 --> 00:23:59,360
Speaker 1: project in China. Can you comment on that a little bit, Yeah.

481
00:23:59,160 --> 00:24:03,680
Speaker 2: Sure, So, currently Tesla's FSD, their full self driving name

482
00:24:03,720 --> 00:24:06,360
Speaker 2: brand product which I mentioned earlier, is not a full

483
00:24:06,400 --> 00:24:09,680
Speaker 2: self driving system, is not available in China right now.

484
00:24:09,800 --> 00:24:13,840
Speaker 2: It's something that they've recently received permission or at least

485
00:24:13,880 --> 00:24:17,400
Speaker 2: passed too critical milestones to be able to introduce their

486
00:24:17,440 --> 00:24:20,879
Speaker 2: technology in the market. Now. China is the world's biggest

487
00:24:20,880 --> 00:24:23,840
Speaker 2: auto market, big potential for them to introduce this type

488
00:24:23,840 --> 00:24:26,640
Speaker 2: of technology there, particularly when you consider this is kind

489
00:24:26,640 --> 00:24:29,520
Speaker 2: of eight thousand dollars or whatever they are able to

490
00:24:29,640 --> 00:24:31,280
Speaker 2: charge for it on top of the purchase price of

491
00:24:31,320 --> 00:24:33,320
Speaker 2: the vehicle. So good a little earner for them. And

492
00:24:33,640 --> 00:24:35,879
Speaker 2: since they've got these types of permissions, they can possibly

493
00:24:35,920 --> 00:24:38,760
Speaker 2: start introducing this technology. But they're not the only game

494
00:24:38,800 --> 00:24:42,159
Speaker 2: in town. A lot of the local companies, particularly this

495
00:24:42,359 --> 00:24:45,600
Speaker 2: new crop of electric vehicle automakers that China has are

496
00:24:45,640 --> 00:24:50,840
Speaker 2: offering at least at an SAE level Driving Automation classification level.

497
00:24:51,040 --> 00:24:54,560
Speaker 2: It's a similar type of technology. And from what we've tracked,

498
00:24:54,880 --> 00:24:57,320
Speaker 2: those technologies are being offered at a much lower price

499
00:24:57,440 --> 00:25:00,600
Speaker 2: than what Tesla would be potentially introducing this vehicle, so

500
00:25:00,720 --> 00:25:03,480
Speaker 2: potential for them there but decent amount of competition.

501
00:25:03,800 --> 00:25:08,040
Speaker 1: And then just going back to this convergence between the

502
00:25:08,080 --> 00:25:11,840
Speaker 1: self driving technology and electric vehicles and the battery packs

503
00:25:11,840 --> 00:25:14,920
Speaker 1: that are in the electric vehicles themselves, how much of

504
00:25:15,000 --> 00:25:18,840
Speaker 1: the battery does this technology really draw upon and does

505
00:25:18,880 --> 00:25:22,560
Speaker 1: it really dramatically reduce the number of miles traveled by

506
00:25:22,600 --> 00:25:25,000
Speaker 1: some of these vehicles, and is that really a concern?

507
00:25:25,119 --> 00:25:28,080
Speaker 1: And really are there any other drawbacks to this technology

508
00:25:28,080 --> 00:25:29,359
Speaker 1: that maybe I'm not seeing?

509
00:25:29,720 --> 00:25:32,000
Speaker 2: Yeah, I mean in some cases, so you have a

510
00:25:32,040 --> 00:25:36,000
Speaker 2: few different providers of self driving computers rending from video

511
00:25:36,080 --> 00:25:39,320
Speaker 2: to mobile eye that are all providing different compositions of

512
00:25:40,040 --> 00:25:42,080
Speaker 2: computing power that end up under the hood of the

513
00:25:42,119 --> 00:25:45,760
Speaker 2: vehicle and do the self driving. Now, the power consumption

514
00:25:45,800 --> 00:25:49,000
Speaker 2: of these can vary, but we ransom some numbers and

515
00:25:49,040 --> 00:25:51,840
Speaker 2: in kind of a worst case scenario, nearly half of

516
00:25:51,920 --> 00:25:54,320
Speaker 2: the battery or the capacity of the battery would be

517
00:25:54,400 --> 00:25:58,199
Speaker 2: drained by some of these self driving systems. That's a

518
00:25:58,240 --> 00:26:01,600
Speaker 2: lot that is kind of a worst case scenario number,

519
00:26:01,720 --> 00:26:05,600
Speaker 2: and I would mention that as these different computers get iterated,

520
00:26:05,800 --> 00:26:09,560
Speaker 2: it's something that is declining rapidly, So we don't expect

521
00:26:09,560 --> 00:26:12,520
Speaker 2: that type of number in many real world applications as

522
00:26:12,560 --> 00:26:15,760
Speaker 2: we progress. But also, if you're operating in a robotaxic capacity,

523
00:26:15,800 --> 00:26:18,680
Speaker 2: this is something that you can design around. So if

524
00:26:18,720 --> 00:26:22,240
Speaker 2: you are getting to say a three hundred mile range vehicle,

525
00:26:22,320 --> 00:26:25,040
Speaker 2: and you might be only getting two hundred miles out

526
00:26:25,080 --> 00:26:27,320
Speaker 2: of that, that's something that you can design your usage

527
00:26:27,359 --> 00:26:28,080
Speaker 2: patterns around.

528
00:26:28,400 --> 00:26:30,120
Speaker 1: We've talked a bit about the companies that are really

529
00:26:30,119 --> 00:26:32,919
Speaker 1: going all in on this technology and prioritizing it as

530
00:26:32,920 --> 00:26:35,119
Speaker 1: a part of their strategy. But can you give me

531
00:26:35,160 --> 00:26:37,720
Speaker 1: some notable examples of those who have really backed away

532
00:26:37,720 --> 00:26:38,040
Speaker 1: from it.

533
00:26:38,280 --> 00:26:41,560
Speaker 2: Well, one of the most high profile that's kind of

534
00:26:41,680 --> 00:26:44,679
Speaker 2: recently cooled on self driving and in fact vehicles in general,

535
00:26:44,800 --> 00:26:47,160
Speaker 2: is Apple up until the end of twenty twenty three,

536
00:26:47,240 --> 00:26:49,639
Speaker 2: or we have data that shows that they were testing

537
00:26:49,640 --> 00:26:53,320
Speaker 2: this self driving vehicle technology in California right up until

538
00:26:53,320 --> 00:26:55,119
Speaker 2: the end of last year, so this has been a

539
00:26:55,160 --> 00:26:58,199
Speaker 2: pretty recent decision they and then they have decided to

540
00:26:58,480 --> 00:27:01,160
Speaker 2: pull back on self drive VIA and that after years

541
00:27:01,200 --> 00:27:03,479
Speaker 2: of trying different strategies of they're going to be a

542
00:27:03,520 --> 00:27:06,760
Speaker 2: fully self driving robotaxi service, they're going to be a

543
00:27:06,800 --> 00:27:10,239
Speaker 2: partially automated vehicle that will kind of compete with your

544
00:27:10,240 --> 00:27:13,480
Speaker 2: traditional automakers. Eventually they've decided that they are going to

545
00:27:13,480 --> 00:27:15,919
Speaker 2: pull back from that. And there's probably a few different

546
00:27:15,920 --> 00:27:18,359
Speaker 2: reasons for that, one of them being the competition in

547
00:27:18,400 --> 00:27:21,679
Speaker 2: the space, but also the complexity of this environment and

548
00:27:21,720 --> 00:27:25,400
Speaker 2: balancing the need for an electric vehicle and a self

549
00:27:25,440 --> 00:27:28,600
Speaker 2: driving vehicle and all the other technology that goes into

550
00:27:28,680 --> 00:27:29,800
Speaker 2: these applications.

551
00:27:30,280 --> 00:27:31,960
Speaker 1: Well, and actually you've mentioned there were a lot of

552
00:27:31,960 --> 00:27:34,320
Speaker 1: companies and a lot of activity about a decade ago.

553
00:27:34,640 --> 00:27:37,359
Speaker 1: Presumably there are a few companies that are now left

554
00:27:37,440 --> 00:27:39,919
Speaker 1: is emerging as the leaders in this space as the

555
00:27:39,960 --> 00:27:42,800
Speaker 1: industry I mean, actually, has there been consolidation or has

556
00:27:42,840 --> 00:27:46,480
Speaker 1: it essentially been that just some companies have really pulled

557
00:27:46,480 --> 00:27:47,080
Speaker 1: out in the lead.

558
00:27:47,359 --> 00:27:49,880
Speaker 2: There's definitely been a lot of consolidation and a lot

559
00:27:49,920 --> 00:27:53,159
Speaker 2: of companies that have pivoted to specific applications. So a

560
00:27:53,240 --> 00:27:56,960
Speaker 2: company like Aurora, for instance, started off as a robotaxi developer,

561
00:27:57,080 --> 00:28:01,359
Speaker 2: they now focus almost exclusively one large application, heavy duty

562
00:28:01,400 --> 00:28:04,960
Speaker 2: trucks and doing long haul driving. And then there's companies

563
00:28:05,040 --> 00:28:07,080
Speaker 2: that have been bought up and are now part of

564
00:28:07,320 --> 00:28:10,520
Speaker 2: much larger entities. Zoos, for instance, one of the more

565
00:28:10,560 --> 00:28:14,000
Speaker 2: prominent companies and one of the more active robotaxi testers

566
00:28:14,119 --> 00:28:17,480
Speaker 2: within the US and within California, is now a subsidiary

567
00:28:17,480 --> 00:28:19,800
Speaker 2: of Amazon. So a lot of the tech companies are

568
00:28:19,920 --> 00:28:22,639
Speaker 2: very interested in the space and have some interest or

569
00:28:22,640 --> 00:28:26,960
Speaker 2: some venture. Microsoft placed quite recently a big investment in Wave,

570
00:28:27,320 --> 00:28:30,200
Speaker 2: so the tech companies are monitoring this closely. So it's

571
00:28:30,200 --> 00:28:32,440
Speaker 2: interesting to see Apple pull out while a lot of

572
00:28:32,480 --> 00:28:35,359
Speaker 2: the other tech companies are still actively involved and funding

573
00:28:35,400 --> 00:28:35,960
Speaker 2: their adventures.

574
00:28:36,080 --> 00:28:38,240
Speaker 1: And you bring up an important part of miles traveled

575
00:28:38,280 --> 00:28:40,320
Speaker 1: and really how goods get from place to place in

576
00:28:40,400 --> 00:28:45,000
Speaker 1: supply chains, which are trucks and delivery vehicles. Beyond the

577
00:28:45,080 --> 00:28:49,719
Speaker 1: robotaxis and the consumer vehicles that end up having all

578
00:28:49,760 --> 00:28:52,480
Speaker 1: these safety features added, are the delivery trucks in our

579
00:28:52,520 --> 00:28:55,560
Speaker 1: supply chain vehicles. A huge part of this market or

580
00:28:55,600 --> 00:28:57,320
Speaker 1: is it kind of a little bit slower to react

581
00:28:57,320 --> 00:28:57,560
Speaker 1: to it.

582
00:28:57,920 --> 00:29:00,880
Speaker 2: Trucking is a really interesting use case because if you

583
00:29:00,920 --> 00:29:03,640
Speaker 2: really think about it, a lot of the applications seem

584
00:29:03,720 --> 00:29:05,880
Speaker 2: like there might be some of the lower hanging fruit

585
00:29:05,960 --> 00:29:08,160
Speaker 2: of the space. If you are driving from one point

586
00:29:08,200 --> 00:29:10,000
Speaker 2: to another, you know a start points, you know an

587
00:29:10,120 --> 00:29:12,760
Speaker 2: end points. If you are doing long haul trucking, particularly

588
00:29:12,760 --> 00:29:15,160
Speaker 2: across the southern US where a lot of these companies

589
00:29:15,160 --> 00:29:18,360
Speaker 2: are located, you're dealing with pretty good road conditions. It's

590
00:29:18,600 --> 00:29:21,840
Speaker 2: pretty decent and consistent weather the whole time, and for

591
00:29:21,880 --> 00:29:24,480
Speaker 2: the most part, it seems like regulators are fairly open

592
00:29:24,520 --> 00:29:27,960
Speaker 2: to some of these solutions that seems like the perfect combination.

593
00:29:28,800 --> 00:29:30,960
Speaker 2: A lot of the trucking companies haven't been able to

594
00:29:31,000 --> 00:29:33,760
Speaker 2: reach the stage where they are removing drivers fully from

595
00:29:33,760 --> 00:29:36,400
Speaker 2: the vehicles for those fixed journeys, but there's a number

596
00:29:36,440 --> 00:29:39,440
Speaker 2: of promises that that will happen this year. So Aurora

597
00:29:39,600 --> 00:29:44,000
Speaker 2: plus AI and Kodiacrobotics are all targeting to remove the

598
00:29:44,080 --> 00:29:46,320
Speaker 2: driver from the vehicle during the course of this year

599
00:29:46,400 --> 00:29:49,080
Speaker 2: and run some of those operations. So big things ahead

600
00:29:49,080 --> 00:29:50,200
Speaker 2: for the self driving.

601
00:29:49,960 --> 00:29:53,000
Speaker 1: Truck space certainly something to watch. So we talked a

602
00:29:53,040 --> 00:29:55,040
Speaker 1: little bit about how it changes the number of miles

603
00:29:55,080 --> 00:29:58,040
Speaker 1: traveled for the vehicles themselves. But can you actually talk

604
00:29:58,080 --> 00:29:59,680
Speaker 1: to me a little bit about how this changes the

605
00:29:59,680 --> 00:30:01,720
Speaker 1: way they we interact with the vehicles and the way

606
00:30:01,720 --> 00:30:04,560
Speaker 1: that people essentially drive, get around, receive their goods. What

607
00:30:04,600 --> 00:30:07,400
Speaker 1: are some of the overarching themes and takeaways in particular

608
00:30:07,480 --> 00:30:10,920
Speaker 1: given that we're BNF and we talk about decarbonization here, well.

609
00:30:10,800 --> 00:30:12,720
Speaker 2: I mean one of the main ways we think about

610
00:30:12,720 --> 00:30:15,920
Speaker 2: this is just how it's changing how people would react

611
00:30:16,120 --> 00:30:18,760
Speaker 2: in the vehicle and how the driving patterns that they

612
00:30:18,800 --> 00:30:21,720
Speaker 2: would have if some of the burden of driving that

613
00:30:21,840 --> 00:30:25,160
Speaker 2: vehicle is taken away or lightened. So there's some early

614
00:30:25,200 --> 00:30:27,680
Speaker 2: studies out there, and this is kind of early data

615
00:30:27,920 --> 00:30:30,680
Speaker 2: that show that people are more willing to drive longer

616
00:30:30,680 --> 00:30:34,840
Speaker 2: distances if they have a reliable advanced driver's sistance system

617
00:30:35,120 --> 00:30:38,080
Speaker 2: in the vehicle that is helping them drive. Now, that's

618
00:30:38,120 --> 00:30:41,400
Speaker 2: really interesting because it means that maybe people will be

619
00:30:42,000 --> 00:30:45,880
Speaker 2: more willing to drive or prefer to drive longer distances

620
00:30:46,040 --> 00:30:49,000
Speaker 2: versus taking a train or maybe a short hall flight

621
00:30:49,320 --> 00:30:52,160
Speaker 2: or things like that. So it could really change the

622
00:30:52,240 --> 00:30:55,120
Speaker 2: number of miles that are traveled on roads that obviously

623
00:30:55,120 --> 00:30:58,440
Speaker 2: has a big impact on electric vehicle batteries. It has

624
00:30:58,480 --> 00:31:01,239
Speaker 2: a big change on that number of vehicles that are

625
00:31:01,240 --> 00:31:03,040
Speaker 2: on the roads. So it's something that we're paying a

626
00:31:03,080 --> 00:31:04,000
Speaker 2: lot of close attention to.

627
00:31:04,320 --> 00:31:06,520
Speaker 1: Yeah there, I mean, just the couple of examples you

628
00:31:06,600 --> 00:31:10,960
Speaker 1: gave have huge implications. So fewer rail journeys may or

629
00:31:11,000 --> 00:31:14,479
Speaker 1: may not end up increasing emissions overall, but actually fewer

630
00:31:14,560 --> 00:31:18,640
Speaker 1: flights would certainly reduce emissions. So it really depends upon

631
00:31:18,680 --> 00:31:22,280
Speaker 1: I guess what it's replacing and how it changes actually

632
00:31:22,280 --> 00:31:24,560
Speaker 1: even where people live and how they commute to work

633
00:31:24,600 --> 00:31:26,520
Speaker 1: and how they go about their lives. So we'll watch

634
00:31:26,560 --> 00:31:29,120
Speaker 1: this space and I look forward to seeing what analysis

635
00:31:29,160 --> 00:31:31,440
Speaker 1: comes out of your team on how the future of

636
00:31:31,520 --> 00:31:33,840
Speaker 1: vehicles and how we get around comes about.

637
00:31:34,200 --> 00:31:41,560
Speaker 2: Thanks for having me, Dana.

638
00:31:44,120 --> 00:31:47,240
Speaker 1: Today's episode of Switched On was produced by Cam Gray

639
00:31:47,480 --> 00:31:51,040
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00:31:51,080 --> 00:31:54,160
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641
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642
00:31:56,960 --> 00:32:00,880
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643
00:32:00,920 --> 00:32:03,760
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