“Sensor data” is what the car’s cameras and other detectors “see” and measure around it. The episode says maps can work alongside that sensor information to help the car react more reliably.
Orbis maps are a type of detailed digital map. The host explains they’re meant to work together with what the car’s sensors detect to help the car respond more confidently.
The Toyota RAV4 is a popular compact SUV. In this episode, they say the redesigned 2026 hybrid version is starting production in the U.S., which can influence how fast dealers can get enough cars.
FNI revenue is the money a car dealership makes from financing and insurance, not just from selling the car itself. It can include things like loans/leases and coverage you buy with the vehicle.
Concept
bargaining table
In labor negotiations, the “bargaining table” is the formal negotiation process where a union and an employer discuss terms like wages, benefits, and job protections. It’s a shorthand for the structured talks that follow the initial meeting/handshake.
“Super Duty” is Ford’s heavy-duty truck line—built for tougher jobs like towing and hauling. Saying they’ll build Super Duty pickups means Ford plans to ramp up production of those work trucks.
“Lane level” means the map knows which lane you’re in and what’s happening in each lane. That helps driver-assist systems guide you more precisely than just “turn left at the road.”
“Real-time information” means the map updates as conditions change—like traffic, closures, or hazards. Instead of using an old snapshot, the car can react to what’s happening right now.
“Real-time mapping” means the map isn’t fixed; it can refresh very often. For the driver, that translates to guidance that better matches what’s happening on the road right now.
“AI inference on edge” means the car does the AI thinking locally instead of relying on a remote computer. That can make responses quicker when something changes on the road.
High definition maps are very detailed digital maps used for self-driving cars. They can include things like lane layout and where features are, which helps the car understand the road even when cameras or sensors struggle.
“Perception first” means the car should mainly figure things out from what its sensors see right now. Instead of depending on detailed maps, it tries to understand the road directly in real time.
Real-time perception is how the self-driving system keeps “seeing” and understanding what’s around the car, all the time. It needs to be fast so the car can react right away.
In this context, “balance” refers to combining sensor-based perception with map-based knowledge rather than choosing one approach exclusively. The speaker argues that safety-critical scenarios benefit from using maps as a complement to the vehicle’s perception.
“Edge situations” are rare or difficult driving scenarios where normal sensor behavior and typical driving assumptions may break down. In these cases, map data can act as a safety-and-consistency complement to sensor-based perception.
They’re saying maps shouldn’t be used as a fallback that replaces the car’s own sensing. Instead, maps should work alongside the sensors to help the system make better decisions.
An “obstruction” is something that blocks the car’s view—like a barrier or something in the way. The point is that maps can still provide useful context when sensors can’t see clearly.
This is information the car’s sensors notice, then convert into something the navigation/mapping system can use. Think of it as sensor “findings” turned into data.
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This podcast is brought to you by Reynolds and Reynolds.
There are three waves of AI transforming our industry.
Explore what they are, where AI technology is headed,
and what these emerging innovations mean for automotive retailers
at rayray.com slash ai-waves for more info.
Welcome to Daily Drive. For Monday, June 22nd, 2026,
I'm Kellyn Walker in Las Vegas. Today on the show,
Unifor and Ford are at the table, and the stakes are high for Canadian auto workers.
Toyota's RAV4 hits a new production milestone, but dealers still can't keep up with demand.
And the top dealership groups for FNI revenue show what's actually moving the needle.
Plus, Tom Tom's Manuela Locarno Ajai talks about AI-powered maps that update in real time,
down to the lane level. Our Orbis maps provide a complementarity to the sensor data that the
vehicle can capture and allow for a much degree of confidence in the ability to respond to certain
events around the car. Let's run through all the news you need to know to keep up in the auto industry.
Unifor and Ford are kicking off contract talks today. It's the first of three negotiations
the union will hold with Detroit 3 automakers this summer. Current agreements expire in September.
The union represents nearly 19,000 Canadian auto workers. It says it won't make concessions,
and it's pushing hard for job security protections after the sector lost nearly 6,500 jobs since
February 2025. Talks with Stellantis and GM are expected to follow once a Ford deal is reached.
We'll have more on this in a minute with automotive news Canada's David Kennedy,
who's on the scene in Toronto as the two sides begin talks.
Toyota's hottest vehicle just got a new production home. The redesigned All Hybrid 2026
RAV4 is now rolling off the line in Georgetown, Kentucky. It's Toyota's first US production
of the model. Three plants across Japan, Canada, and now the US are ramping up, but demand
is outrunning supply. US sales are down 40% through May, and Toyota estimates nearly 55,000
lost sales this year. That's more than $1.8 billion in revenue. Georgetown is expected to
add 40,000 units in 2026, with output set to climb another 50% next year.
And the top dealership groups in finance and insurance are finding that standardized training,
not just star performers, is the key to growing revenue. Automotive news's 2026 list of the top
100 dealership groups ranked by FNI revenue shows some groups posting gains of more than 80% year
over year. Bert Ogden Auto Group led the way. It hired a dedicated platform director to keep
finance managers accountable across more than 20 stores. Ken Gainley Automotive and Bayway Auto
Group leaned on consistent customer processes and tailored product offerings to drive similar
results. You can find our list of the top 100 dealership groups based on FNI revenue at AutoNews.com.
And those are today's headlines. You can find more details on all those stories at AutoNews.com.
Joining me now is David Kennedy, reporter at our sibling publication Automotive News Canada,
who's on the scene in Toronto as Unifor and Ford sit down to begin contract talks. Now David,
what's the scene like there so far? We're relatively busy morning here. We had the official handshake
a few minutes ago. So, you know, things are underway, bargaining is going to happen,
and we'll see where things are, you know, in seven or 10 days time when radio silence gets broken.
Unifor says it won't make concessions, but with nearly 6,500 jobs already lost and tariffs still
in place, what leverage does the union actually have at the bargaining table? It's definitely
going to be a tough year for the union on the bargaining side. I think the reality is, you know,
there's two idle assembly plants here here in Canada this year, some downs down a shift at GM
Oshawa as well. And Ford hasn't built anything in Canada for a couple years. They're going to
gear back up, build some super duty pickups. And what it comes down to it, that's probably the
leverage that we'll see from the union, you know, Ford wants to start building those pickups. They
want to build the engines. So the thing is, you know, that's always the union's power in this,
that they can take their labor away if they want to, whether or not the, you know, it matters from
the company side, we'll wait and see. But, you know, they've picked Ford for a reason. And it's
because they have a relatively good relationship up here in Canada with Ford. I think GM and
Stellantis are going to be much tougher negotiations than the one we see off the bat with Ford.
Now, Ford is the traditional pattern setter in these talks. What does a deal here signal for
the Stellantis and GM negotiations to follow? Uniform has always had a good relationship with
Ford. For years, you know, they've let off these negotiations by talking to Ford. And GM and
Stellantis have proved tougher talks. And that's going to be the case this year as well. Ford,
you know, wants to start building super duties in Canada. They've got everything in place to
do so. And they're planning to this fall. So the both the union and the automaker want this to
happen. GM and Stellantis are a little bit tougher to say, you know, GM's been sort of
dialing out some production in Canada on the pickups that they build. And, you know, could do
that probably without hurting the bottom line, at least, you know, in the short term. And Stellantis
is really the same case, you know, they've been building full bore in Windsor for a while now.
And many events have been selling well, which is good for everyone. But at the same time, you
know, that Brampton plant has sat idle for a long time. And Uniform wants to see it filled.
So we'll just have to see what happens when it comes to those talks. But I think, you know,
we'll see what the pattern is that the Uniform can get set and then all bets are off with the other
two. David Kennedy of Automotive News Canada. Thank you so much for joining me.
Thanks for having me. Coming up, Tom Tom's Manuela Locarno Ajai on how AI is rewriting what
maps can do inside your vehicle. That's next on Daily Drive. Three waves of AI are transforming
our industry, as we know it today. Here from AJ McGowan, Vice President of Research and Development
at Reynolds and Reynolds on all three ways of AI and automotive, where we're heading,
and what the latest cutting edge innovations mean for automotive retailers.
So I want you to picture what it would be like on that same Tuesday morning that we talked about
earlier with a full agenic infrastructure in place. Your service manager wouldn't start the day by
reading reports. Ray hands them three ROs that need attention and tells them why. Your sales
manager wouldn't spot check calls. Ray already routed the ones that mattered. The GM doesn't
reconstruct yesterday. Ray reconstructed it overnight and gave them a summary. And your
controller doesn't have to pull the deal level analysis every Friday. Ray runs it on Thursday
and flags anything that's worth a conversation. Decisions can get made earlier in the day
with better information by fewer people. That's what the agenic wave actually feels like in your
store. It feels and sounds a little bit like science fiction, but at the end of the day,
it's not. It's just a faster Tuesday. If you're interested in exploring how AI can improve operations
at your dealership, visit rayray.com.ai-waves. That's r-e-y r-e-y.com.ai-waves. Welcome back to
Daily Drive. I'm Kellan Walker. When we talk about self-driving tech, we're often talking about
sensors and the computers needed to organize all of that data. But that's only one piece of the AV
puzzle. Vehicles also use maps, which are constantly updated to make sense of the physical world around
them. Tom Tom, Senior Vice President of Product Engineering, Manuela Locarno Ajai, sat down with
her own Hannah Lutz at Tom Tom's Discover Conference in Detroit. They talked on the Automotive News
Shift podcast about how AI is powering a new generation of maps. Ones that update in near
real time and deliver lane-level precision that traditional navigation systems never could. Here's
a piece of that conversation. So we are at Tom Tom's Discover Conference in Detroit today,
and we've heard experts from Tom Tom, Bosch, Amazon, Deloitte, Vistion, and Qualcomm and others talk
about AI, vehicle technology, all connected to mapping, which I know is Tom Tom's specialty.
So Manuela, when we think about mapping in cars, I think most of us still think about maps as pure
navigation systems, at least from a consumer point of view. How has the role of maps changed over the
past decade and continues to change? Well, it's very true that navigation is the most familiar
use case that everybody has exposure to. The reality is that maps, especially in automotive,
have increasingly got a role in assisted driving and now more recently in autonomous driving.
And at Tom Tom, we have built what we call the Orbis map platform very much in anticipation
of the evolution of the industry with the introduction of AI and capabilities that will
require mapping beyond the traditional navigation use case. So tell me more about that platform.
How is it different from previous iterations? So we built Orbis as an AI native platform that brings
mapping capabilities at a global scale, accurate, precise, with the ability to provide
information and details not just at the road level, but also at the lane level to enable
these assisted driving use cases. And we're building in a way that brings freshness and
real-time information, which is very much critical in more specific critical use cases.
So what are kind of the table stakes now in mapping versus the differentiators? You talked about
using AI really to build this. Is that something that really has to be done or is that something
that is is setting some companies apart while others take different strategies?
Well, the key differentiator in mapping these days is the ability to have fresh and real-time
information. It's not just about the attributes of the road geometry. It's about having real-time
information about traffic situation, road closures, hazards on the road. And it's true that the
advancements in AI are allowing us to have a much faster update cycle and the ability to
bring fresh information to the vehicle faster and also generate insights with that information that
really helps the decision systems to make up the automotive mobility platform of today.
So you talked a lot about real-time maps and that's what the company's talked about,
real-time mapping of the entire world. What does that actually mean in practice? What would a
consumer, a driver see as a real-time map that I think in the presentation one of your colleagues
said is updated every five minutes? Yeah, well we have elements of our map that update every
so every few seconds. So it's the ability for us to have a platform that can update as frequently
as needed and then of course it's the ability of the mobility system to ingest that real-time
information as frequently as they're capable of. And what's the next step if we're already at this
real-time vision? What more can be done to improve that driving experience? So we also are discussing
of course the ability to bring the AI inference on edge in the vehicle. You've heard also today
Tom Tom discovered how we said that the ability to react quickly to use cases on the road that
might be edge cases or unpredictable situations require the system to have the ability to either
decide right there and then with on-edge infrastructure or using online services the
traditional way that we have been used in navigation solutions. And that's what truly
makes the difference. There is also you see in some more advanced capabilities where you have
lane level information where it's not just knowing the traffic on the road but you know the traffic
per lane for example or if you know there is a hazard on the road you know in which lane the
hazard is. Those are capabilities that are really influencing the ability to differentiate what
location technology providers can offer. So something I really wanted to ask you there's a
debate in the AV industry the autonomous vehicle industry about whether high definition maps are
essential. So autonomous driving developers argue that they are critical to understanding
the driving environment beyond what the sensors can tell. Others say perception first since
systems with the sensors can scale faster and that the autonomous systems should understand
roads directly from the cameras and the sensors. What is your take in this debate?
So our take it's ultimately it's about balance right. You need to have real-time perception
of the vehicle for autonomous driving to be a possibility but you also want the system to
have knowledge about what the road and the environment around the car looks like. And
it's about having the ability to balance those two elements in all use cases in particular those
very edge situations where the map as a complement to the system is required for safety and consistency
and and we truly believe a map shouldn't be looked as a crutch for the autonomous system
but really very much as a complement to the overall platform that the AV are built on.
What does a map know that sensors have no way of knowing?
Well you have very simple situations where there is an obstruction. The vehicle sensors
just are not able to see because maybe there is a large tracks in front of them or maybe because
there is just weather situations that obfuscate the sensors and having that knowledge built in
in the car side by side with what the car can sense through the sensors on the vehicle is what
allows the vehicle to be able to respond to those edge cases where obstructions allow the system to
see but there is also the effect the sensors see just so far there is a horizon element to sensors
the map can complement by having the ability to offer to the vehicle information about what's
happening farther ahead or around the vehicle that the vehicle itself could not see with sensors alone.
And you're gathering this information about obstructions and things that sensors are not
able to see for whatever reason from vehicle data from vehicles that have already passed through.
Our Orbis platform is ingesting tens of thousands of data sources it is a combination of probe data
sensor derived observations data but also a variety of data sources that we acquire through
different areas and also for different geographies those data sources might differ and that's what
allows us to have the high confidence that our Orbis maps provide a complementarity to the sensor
data that the vehicle can capture and allow for a much degree of confidence in the ability to
respond to certain events around the car. Tom Tom senior vice president of product engineering
Manuela Locarno Ajai spoke with our own Hannah Lutz you can hear the full conversation on the
episode of shift available now wherever you get your podcasts that's daily drive for today
i'm kellen walker thanks to automotive news executive producer jake neer as well as our own
Larry bellaquette and gail howe for their reporting for today's podcast we also had
reporting from david kennedy of our sibling publication automotive news canada you can
get the latest news on tech and innovation the uniform ford contract talks and everything
happening in the auto industry at autonews.com come back tomorrow for a conversation with
john bozella CEO of the alliance for automotive innovation in more recent election cycles is
that the game is now from one end zone all the way to the other we're not just making adjustments
to regulations we're tearing up the playbook and starting completely over again that's very
challenging to the auto industry we'd love to hear from you let us know you think of the show
and the topics we covered today send us an email at daily drive at auto news dot com
or leave us a voicemail at 313-444-2774 and if you enjoy the podcast remember to like leave a
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About this episode
Unifor’s contract talks with Ford kick off in Toronto, with Ford aiming to restart Super Duty production in Canada and the union weighing how that posture compares with GM and Stellantis. The show then pivots to Toyota’s redesigned All Hybrid 2026 RAV4 ramp in Georgetown, Kentucky. Later, TomTom’s team discusses AI-powered, lane-level mapping—updating every few seconds—and how Orbis complements vehicle sensors for traffic, closures, hazards, and situations sensors can’t see.
Unifor and Ford kick off contract talks in Toronto today, the first of three negotiations with the Detroit 3 automakers this summer. Toyota’s RAV4 finds a new home in Kentucky. Plus, TomTom’s Senior Vice President Manuela Locarno Ajayi explains how artificial intelligence is delivering real-time, lane-level mapping and why it matters for the future of autonomous driving.
