# Eyes-Off Autonomous Driving Arrives by 2028 | Brandon Basso

Markdown copy of https://realalpha.app/v/XEJtFxyWlXk/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

Brandon Basso argues that end-to-end driving AI can learn safety directly, while diverse data and validation remain essential to autonomy.

- Show: MTS
- Hosts: [Sophia Dew](https://realalpha.app/people/sophia-dew/)
- Guest: [Brandon Basso](https://realalpha.app/people/brandon-basso/) is head of simulation and validation at Wayve.
- Published: Nov 8, 2026
- Length: 22 min

## The best minutes

- [12:42](https://realalpha.app/v/XEJtFxyWlXk/?t=762) [Wayve learns safety instead of relying on checkers](https://realalpha.app/s/wayve-learns-safety-instead-of-relying-on-checkers/) (3 min)
- [8:13](https://realalpha.app/v/XEJtFxyWlXk/?t=493) [Wayve combines road data, OEMs, and simulation](https://realalpha.app/s/wayve-combines-road-data-oems-and-simulation/) (5 min)
- [15:48](https://realalpha.app/v/XEJtFxyWlXk/?t=948) [Wayve expects a shift toward eyes-off driving by 2028](https://realalpha.app/s/wayve-expects-a-shift-toward-eyes-off-driving-by-2028/) (3 min)

## Also worth watching

- [2:35](https://realalpha.app/v/XEJtFxyWlXk/?t=155) [Wayve grades driving across four dimensions](https://realalpha.app/s/wayve-grades-driving-across-four-dimensions/) (3 min)

Skip the opening company overview, the Lovable ad, and the closing sponsor reads.

## Full analysis: Brandon Basso says eyes-off shift may come in 2027 into 2028

- Basso expects a major shift toward eyes-off L3 driving in 2027 into 2028.
- His case rests on Wayve's safety, data and validation approach, but the episode reports no measured safety results.
- Teams should treat the window as a thesis to test, not a product commitment.

Its 6 sections, what it means for you and what to watch are on [the page](https://realalpha.app/v/XEJtFxyWlXk/#analysis), free with an account:

- [17:05](https://realalpha.app/v/XEJtFxyWlXk/?t=1025) Basso expects an eyes-off shift by 2028
- [17:51](https://realalpha.app/v/XEJtFxyWlXk/?t=1071) Eyes-off systems require stricter safety proof
- [4:25](https://realalpha.app/v/XEJtFxyWlXk/?t=265) Four driving dimensions resist one score
- [9:50](https://realalpha.app/v/XEJtFxyWlXk/?t=590) Local variation widens the data burden
- [12:00](https://realalpha.app/v/XEJtFxyWlXk/?t=720) Simulation may scale data, but its share is unknown
- [13:01](https://realalpha.app/v/XEJtFxyWlXk/?t=781) Wayve learns safety alongside lower-level safeguards

## Summary

Brandon Basso leads simulation and validation at Wayve, a company developing end-to-end AI for driving. He describes Wayve as pursuing autonomy across vehicles, from L2/L2+ driver assistance to L4 robotaxis, and says it has a supervised public pilot in London.

Basso says autonomous driving requires both building an AI driver that can outperform humans and proving that it works across varied global conditions. He argues that end-to-end systems should learn safety as part of driving, rather than depend on checker stacks, and that validation must account for safety, comfort, compliance and competence. Wayve gathers data through its own collection, OEM partners and purchased sources, while Basso says simulation should contribute more over time. He expects a major shift toward L3 eyes-off autonomy in 2027 into 2028, which he says could give people back commute time.

### Minute by minute

- [0:51](https://realalpha.app/v/XEJtFxyWlXk/?t=51) **Wayve targets autonomy across vehicles.** Basso says Wayve aims to bring autonomy to all vehicles, not just robotaxis. He describes a path from L2/L2+ driver assistance to L4 robotaxis and says Wayve has a supervised public pilot in London. He frames the challenge as both building a capable AI driver and proving that it works.
- [2:45](https://realalpha.app/v/XEJtFxyWlXk/?t=165) **Safety needs evidence, not claims.** Basso says driving remains risky and costly, making safety improvements a responsibility for engineers and product teams. For end-to-end systems, he says the industry has not yet established rigorous ways to prove they work, much as frontier AI developers face the problem of validating model outputs.
- [4:13](https://realalpha.app/v/XEJtFxyWlXk/?t=253) **Safety has four dimensions.** Basso says Wayve assesses safety for people inside and outside the vehicle, comfort, compliance with local traffic rules, and competence in tasks such as lane changes. He says there is no single metric or benchmark that captures these qualities, so measures should reflect human expectations.
- [5:54](https://realalpha.app/v/XEJtFxyWlXk/?t=354) **Physical AI lacks a broad dataset.** Basso says Wayve recognized driving and validation as data problems early and gathered varied data, including through a roadshow in very cold regions. He argues that physical AI lacks a general dataset comparable to language, so driving systems need diverse examples to improve.
- [8:02](https://realalpha.app/v/XEJtFxyWlXk/?t=482) **Driving behavior depends on context.** Basso uses stoplights and construction scenes to show why fixed rules alone cannot capture driving behavior: signs and stopping conventions vary by place, and construction requires interpreting signs and their implications. He argues that learning from situations in different countries can help produce a driver that works worldwide.
- [11:16](https://realalpha.app/v/XEJtFxyWlXk/?t=676) **Wayve draws on several data sources.** Basso says Wayve's operations team collects geographic and vehicle diversity as well as expert-driver reference data, while the company aims to keep its internal operations footprint limited. He also lists OEM partnerships and purchased or licensed sources, including dashcam footage. He says simulation should generate more data over time.
- [12:42](https://realalpha.app/v/XEJtFxyWlXk/?t=762) **End-to-end AI learns safety intrinsically.** Basso says modular and hybrid systems can rely on maps and other checkers that may fail when signs move or appear temporarily, and he calls such checks a potential performance bottleneck. Wayve trains a safety representation to predict where driving will be unsafe, such as when a bus makes a lane change risky, and uses it to guide driving trajectories.
- [15:48](https://realalpha.app/v/XEJtFxyWlXk/?t=948) **L3 autonomy could give time back.** Basso expects a major shift in 2027 into 2028 from L2+ hands-off assistance toward L3 eyes-off systems, where the vehicle is responsible for safety while occupants do other tasks. He says those systems need more stringent safety guarantees, but could let people use commute time for work, sleep or other activities.
- [18:26](https://realalpha.app/v/XEJtFxyWlXk/?t=1106) **Wayve plans to make its case.** Basso says Wayve's next steps include getting its system out, explaining its approach and building discussion with the public and customers about how it will prove safety. He describes that work as a way to move the industry forward.

## Transcript

MTS, hosted by Sophia Dew, with Brandon Basso.

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[0:00](https://realalpha.app/v/XEJtFxyWlXk/?t=0) And I think there's going to be a big shift in 2027 into 2028 when we move from. So look, Wave is working on these systems that are these L2 plus driver assist systems where you can take your hands off the vehicle. What we need to do is move beyond L2 plus to L3 systems where sometimes those are referred to as not just hands off, but eyes off systems. So those are systems where you can truly check out, do other tasks, and the system is responsible for safety. Hello, everyone, and welcome back to MTS. Today, I am joined by Brandon Basso, who runs the simulation and validation at Wave, the company teaching cars to drive with end-to-end AI. So Brandon, welcome to MTS. Thank you so much for having me. Thank you for joining. This is pretty exciting. I know we often, we're living here in San Francisco, we see things like Waymos and Zooks and all of these existing autonomous cars. But what you guys are doing is you're teaching new cars, how they can, too, be anonymous. Tell us a big picture for people who aren't familiar with Wave. Yeah. So like you mentioned, we're in San Francisco and it's hard to not run into an autonomous car here, whether it's a Waymo or a car from Zoox or any of the many other providers

[1:10](https://realalpha.app/v/XEJtFxyWlXk/?t=70) out there. But there's also every other car on the road that isn't yet autonomous. And Wave's mission is to have ubiquitous autonomy everywhere for everyone. So all vehicles, not just Tesla's, every other vehicle out there. And this starts with a level of autonomy that we call L2 or L2 plus, which is sort of like, you can take your hands off the steering wheel, it can drive you in urban settings, highway settings, and then we're working on all the way up to L4 Robotaxi, where we have a public pilot in London, currently supervised pilot. What is the hardest part about this? Oh, my gosh, so many things. Well, I am the safety validation person. So for job security the reasons i have to say that part but um there are so many hard aspects i mean as an industry we've been at this problem for two decades going back to foundational research happening at berkeley and stanford and many other institutions around the world and there's so many hard parts i would broadly split it into two big categories and they're very much addressed in some of the content that we put out today so one is how do we make a driver drive better than a human superhuman an ai driver? And then two, how do we validate that it's doing what it's doing? Because it's of course

[2:27](https://realalpha.app/v/XEJtFxyWlXk/?t=147) not good enough to just say, well, we built this great model and here you go. We have to prove that it works. And both of those parts have their own challenges. I personally focus a lot on the proof part, but they're both extremely hard when you think of the breadth of driving that can happen, scenarios, weather conditions throughout the entire world, which is what Wave focuses on literally everywhere. Tell me a little bit more about safety. Why is safety such a difficult engineering problem? Yeah. So, I mean, let's start today with the status quo. The status quo today for driving is not great. It is a very risky thing that we do. It's costly in terms of time, but also just insurance costs for minor accidents. And then, you know, we don't even need to motivate the major safety challenges and the populations affected by vehicular accidents in the United States everywhere throughout the world.

[3:20](https://realalpha.app/v/XEJtFxyWlXk/?t=200) So it is, I think, incumbent upon us as engineers, as scientists, as product managers, researchers to think about how we improve all of those problems. So it kind of is one of those things that really, I think, needs no motivation. and why it's such an intent focus for WAVE. Well, I mean, it's a focus of every self-driving car company out there, whether you're working on L2 or L4. We have this novel approach to self-driving that we refer to as AV2.0 or end-to-end systems. And there hasn't been a lot of rigorous thinking yet about how we prove the safety of those systems. It's exactly the same problem.

[3:59](https://realalpha.app/v/XEJtFxyWlXk/?t=239) that folks at Anthropic and OpenAI think about when they're thinking about frontier safety, how do they prove that what the model is outputting is correct, performance, not going to mislead you, not going to deceive you, aligned with human expectations. It's the same problem, but the physical interpretation of it. So there isn't this golden North Star benchmark for what this means. What does that look like when you're trying to innovate at the Frontier to define what safety means? Yeah, so many things. So we break it down into four core concepts. So safety, of course, has to do with the safety of road users outside of the car, but also the people in the car. There's a comfort element, which is sometimes safety related or adjacent. You know, if you're heartbreaking or swerving or things like that, doing uncomfortable things, sometimes those are precursors of safety. Honestly, equally important is compliance. So there is a dizzying array of traffic rules throughout the world how you behave with certain signs in japan versus in um uh germany is quite different because you're doing london and all across literally everywhere quite literally everywhere when we're operating like we've done with our uh road show last year um in the arctic circle or um just around the world it's

[5:18](https://realalpha.app/v/XEJtFxyWlXk/?t=318) it's crazy how different it is and regulators and our customers expect that we're performing safely, comfortably, compliantly, and competently, meaning that when you ask the car to do a lane change, it does it in a way that people expect. Now, your question is, well, how do we grade all

[5:36](https://realalpha.app/v/XEJtFxyWlXk/?t=336) **Brandon Basso:** those things? And that is the challenge. And there's no one answer. There's no one metric just in the way that you go to any AI leaderboard today. There's a ton of different metrics, but we try

[5:46](https://realalpha.app/v/XEJtFxyWlXk/?t=346) our very best to make sure that those metrics are aligned with human expectation, our expectation when we say what it means for a driver to be safe. Yeah, something you alluded to at the start of our conversation is you mentioned a tough piece is gathering enough data to train these cars to drive autonomously. Famously, even a decade ago, I remember I grew up here in the Bay Area, and you would see the people inside the Waymo cars just gathering data about what's happening on the streets. Where are we now? What's different about 2026 about how we gather all the simulation data? Yeah, absolutely.

[6:22](https://realalpha.app/v/XEJtFxyWlXk/?t=382) So one of the core advantages and I would say core aspects of WAVE's approach going back to a decade ago is we very much recognized both the driving problem and the validation problem as a data problem. And we secured all sorts of interesting diverse data early on. We did this roadshow campaign where we drove well north of, I forget if it's the Arctic Circle or not, sorry, I'm not a geography expert, where it was very cold. And collected diverse data. and that is so critical because our system is fundamentally one that gets better with data just like any frontier llm today and people in robotics general robotics will tell you this there is no general data set for robotics today for physical ai there's no general data set like there is for language you know humans have developed all this language data throughout the years books and you know videos and stuff and we don't have um a corpus yet of um driving data so we work very hard on collecting that that's both our first party collection purchasing data going back to 10 years ago and then partnering with our fantastic oem partners and working with them on getting the data so that we can deliver back to them the best um the best products i want to shout

[7:35](https://realalpha.app/v/XEJtFxyWlXk/?t=455) out our sponsor lovable you know the app you've been meaning to build the internal tool side project or product you'd otherwise lose a weekend to, with Lovable, you get software you can ship today. That includes an editable code base you can inspect and change, plus two-way GitHub sync. Lovable also handles the backend and infrastructure, including managed Postgres, auth, storage, hosting, payments, and more, so you don't have to wire it all up yourself. And through Lovable's MCP server, you can create and deploy your project directly from the agents you already use. Turn ideas into software people love with Lovable. Lovable.dev. Now, on to the episode. So I know today, during our interview, you brought along a demo.

[8:18](https://realalpha.app/v/XEJtFxyWlXk/?t=498) Tell us a little bit about what this demo is supposed to be. Yeah, let's talk about demos. Let's talk about data. So I'm going to jump to one to just highlight that last point. This is in the center blog, the validation blog. So we'll come back to some of these videos. But just speaking to the diversity, here is a very simple scene that you can see anywhere in the world. And it's stopping at a stoplight. Now, the way that those stoplights look in the US and Germany is slightly different. The nuanced behavior for how far you might stop behind a stop line is not only geography dependent, but it's also local rules dependent, style dependent.

[9:00](https://realalpha.app/v/XEJtFxyWlXk/?t=540) And culturally, you know, they say it's a California stop compared to... Yeah. And this really gets back to sort of the overall thrust of what we're putting out there and we're trying to say, which is it is literally impossible to write rules for how this should work. We didn't learn to drive by digesting. We digested, well, it depends on where you grew up, but like we digested a rules book that said okay here's you know the driver test and we took it but ever since then when we were 16 or 14 or whatever we've learned we've extrapolated from those rules we've taken trips to countries where you drive on the other side of the road and we've become better drivers and that's exactly how we view the driving problem um for uh wave i want to um highlight one other great example in here where um i think it might be in the first post here we go okay so um So this example is about many things, but I just want to highlight one thing that is kind of a little bit different, but also a little bit the same throughout the world, which is construction scenes.

[10:02](https://realalpha.app/v/XEJtFxyWlXk/?t=602) So I think when you look at these scenes, you probably have a sense of what to do from just looking at the image. Okay, it's like there's a light. I'm not that great of a driver, so honestly, I look at these scenes, I'm like, thank God I'm not driving in London or Japan right now. well this is this is why we say that the the driver should be better than better than humans because i i was actually looking at this image on the left i'm like do i do i actually know what to do here so um the point is that construction scenes are all a little bit different you have to understand language you have to understand that language both from a system one thinking level um reactionary but also a system two thinking level which is like long-term strategy so like it's not just enough to parse the sign but you also need to understand like what are the consequences of parsing that sign how do i make sure i'm not driving over here into this like left region um and the and then if you look at the right you know there's there's signs in um in uh in different characters uh there's a person there that's like maybe it doesn't even look like he's necessarily trying to tell you what to do so this is an example of the diversity of data that

[11:06](https://realalpha.app/v/XEJtFxyWlXk/?t=666) we have. And the point is that if we learn how to drive in situations like this in Japan on the right and this in the UK on the left, we're going to make a generally better driver that works worldwide. How do you gather all of this data? We have a fantastic operations team that thinks about that night and day, 24-7, 365 globally. So that's like pillar one is our operations team. Now, Wave is decidedly not a hardware and operations business. It's a very hard business to be in. People who have been working on self-driving, especially RoboTaxi Forever, have massive operations footprints. Kudos to them for how they've built that out.

[11:47](https://realalpha.app/v/XEJtFxyWlXk/?t=707) But that's not how Wave is planning on solving the problem. We're planning on solving the problem by having a minimal internal operations footprint purely for the purpose of geographic and vehicle diversity data collection. and also... High-fidelity human reference data. So we have expert drivers. We see how they drive. However, we want to primarily generate data with simulation over time. Maybe not primarily, but over time. That's one aspect of getting data. Two is partnering with our OEMs, who have a much richer set of data than we do. And three is purchasing data. So there's a rich set of data out there that we can go license or purchase from a variety of vendors. Dashcam data, for example.

[12:28](https://realalpha.app/v/XEJtFxyWlXk/?t=748) We have a huge corpus of crazy stuff that's happened on highways, right?

[12:33](https://realalpha.app/v/XEJtFxyWlXk/?t=753) **Brandon Basso:** So if we can get that and we can incorporate something that happened on a highway 10 years ago into our corpus, then we can build a better driver.

[12:40](https://realalpha.app/v/XEJtFxyWlXk/?t=760) So multi-prong. All right. With that said, I do want to see the other demos that you have on here. Yes. So we kind of jumped into the part that's near and dear to my heart, which is the validation part, how we prove safety, how we think about data in different continents.

[12:54](https://realalpha.app/v/XEJtFxyWlXk/?t=774) **Brandon Basso:** but i want to touch briefly on the ai driver itself and the main message here is that the way that we've built the driving stack is with safety as an intrinsic property versus an extrinsic bolt on property lots of systems today are either hybrid or compound av or they're purely modular

[13:18](https://realalpha.app/v/XEJtFxyWlXk/?t=798) sorry ai not av where they have different subsystems that are responsible for different aspects of the task and one feature of these systems it turns out is there's lots of checking yeah the canonical example i'll give you and then i'll come to this example that's on the screen is um maps lots of systems have high definition maps that are really like checkers like they say well look we think that we've mapped this before there's a stop sign right here we really need to check uh that there's a stop sign here and stop for it and there's all sorts of challenges you can get into with systems like that like what if the stop sign isn't there what if it moved what if there's a temporary stop sign what if the stop sign is connected to a bus that's moving and you see all of these different failure modes and the the reality is that the checker systems tend to be the ultimate bottleneck and limit of system performance it's not impossible there are some fantastic systems out there today that you can get in and we can go take a we can go take a ride right now you know hundreds of miles around the bay area on on systems like these that are just absolutely fantastic they were built um at great expense and they require great operational overhead

[14:28](https://realalpha.app/v/XEJtFxyWlXk/?t=868) to maintain um and uh like those systems but especially a way if we're working on making that a much more sustainable um system so uh net net we try to learn intrinsically what is safe behavior What you see on the screen here is a representation, a learned representation of safety. You can think of this as like a future occupancy map. The red areas are the don't drive in areas and then sort of a gradient. And you might have seen representations like this before. Those familiar with AVs or robotics where it just sort of looks like an occupancy map, a future occupancy map. But really what this representation is saying is predicting where is and isn't safe to drive based on you see this bus coming up on the right.

[15:11](https://realalpha.app/v/XEJtFxyWlXk/?t=911) So that's a rear bus coming up fast, and it's telling you in the future it's not going to be safe or it's going to be marginally less safe to do a lane change into this lane. So we use this system not as a bolt-on checker but as an explicit head, a task that we learn on how to drive safely, and we use this to guide our ultimate driving trajectories. Now, we, of course, have all of the lower-level systems that you might expect from a car today that you need to do to be compliant. these active safety systems, automatic emergency braking, lane-keeping assist, emergency lane-keeping assist is a soup of TLA's, three-letter acronyms I won't bore you with, but the main AI driver learns safety as an intrinsic property.

[15:54](https://realalpha.app/v/XEJtFxyWlXk/?t=954) So here's a representation. All right. I have some larger macro questions I wanted to ask. just about autonomous driving as a whole. This is an area that I'm particularly excited about. If you look at just the incidents of people who die from car crashes and thinking about, hey, maybe in the next generation, that number could, in theory, be zero. That is insane. And so if we could just zoom out, let's talk a little bit about autonomous driving in general. What does that enable for society at large? So many things. And to just emphasize your point more um those those stats are always very salient they always um hit me hard especially as someone who has young kids but um uh they disproportionately affect people who are younger who are less experienced drivers disproportionately affect different people in different ways throughout the globe and i think it's really incumbent upon us to to think very deeply about how we um upset the status quo here because the status quo isn't great um but beyond the safety argument which I think most people like sort of intuitively get to your question like what what is what's

[16:58](https://realalpha.app/v/XEJtFxyWlXk/?t=1018) **Brandon Basso:** different about society what changes about society and I think there's going to be a big shift in 2027 into 2028 when we move from so look Wave is working on these systems that are these L2 plus

[17:12](https://realalpha.app/v/XEJtFxyWlXk/?t=1032) driver assist systems where you can take your hands off the vehicle you know you can get in vehicles today and experience this like in like in Teslas and customers in general love them and WAVE is working very hard to build credible evidence to show that that kind of driver can be safer than even expert humans so that's really important that's just the the foothold though what we need to do is move beyond L2 plus to L3 systems where sometimes those are referred to as not just hands-off but eyes-off systems so those are systems where you can truly check out do other tasks and the system is responsible for safety now of course to get to those level of systems you need to have much, much more strict, stringent safety guarantees, higher bar, because, you know, can you imagine hopping in your car and saying, you know, take me to the South Bay and taking a nap?

[18:03](https://realalpha.app/v/XEJtFxyWlXk/?t=1083) Yeah. That's a very different thing. It's, you know, almost at the level of what we consider to be safe for robo-taxis today. And I think when we get there, what happens for people is they get their most valuable commodity back, which is time. Their time. their time. There is this really interesting stat where people's happiness levels are almost directly correlated to their commute time. Imagine instead of being stuck in traffic from Sunnyvale to San Francisco for two hours a day, you spend that time working or napping or catching whatever you can do. Really exciting stuff. I know we're kind of at time. I wanted to ask right before we go, what is next for Wave? What can people expect to see over the next few months?

[18:46](https://realalpha.app/v/XEJtFxyWlXk/?t=1126) get the system out there autonomy for everywhere for everyone it's to tell our story it's to represent why we think that this is the right way to solve the problem it's it's honestly to do posts like this and talks like this to sort of um move the industry forward um and um be a thought leader in the space uh both with regard to the safety of the driver itself but also how we plan on doing validation um so that we can start having a conversation with the public and with our customers about how we plan on proving that these systems are going to make our lives better. Very exciting. Brandon, really wanted to thank you for joining us today on MTS and for bringing your demo as well. We got to show it. The whole audience got to see what it actually looks like.

[19:25](https://realalpha.app/v/XEJtFxyWlXk/?t=1165) I am so excited for the future of autonomous driving to have a world where people can get their time back, where transportation becomes a lot easier and more efficient. Very exciting. And this is at the frontier of what is possible. So thank you so much for joining us today on MTS. A huge thanks to MTS sponsors. Credits for your startup at neon.com slash MTS. Special thanks to our sponsor, Kong, the AI connectivity platform. Connect APIs, LLMs, agents, and systems with serious security and governance. KongHQ.com. Finally prove what your AI investment is worth with Lairdyn. Head to Lairdyn.com today to start measuring your AI's bottom line.

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