What comes after the software-defined vehicle? | Daily Reports Online
The automotive world doesn’t slow down. But what’s changed most is what we value when we get behind the wheel.
Not long ago, the story of a new car could be told through horsepower, cylinders, torque and exhaust notes. For next-generation platforms, differentiation increasingly comes from driving assistance, electric range, the digital experience and connected features.
It’s about how confidently a car moves through the world. Does it anticipate what’s coming, or second-guess itself?
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Chief Product Officer at TomTom.
The next phase will not be defined by software alone, but by how intelligently vehicles use context. As cars become more capable of learning from data and adapting over time, the question shifts from whether a vehicle is software-defined to whether it can understand the road, the driver and the situation well enough to create a more confident driving experience.
This confidence depends on both the software that drivers interact with and the location intelligence working behind it. Mapping, routing and road context give a vehicle the understanding it needs to look beyond the immediate moment, interpret what it sees and translate raw signals into behavior that feels smooth, safe and more assured.
Shifting from software-defined to AI-defined
Software has rewired the car. Centralized compute, continuous connectivity and software layers that can evolve more independently from the underlying hardware have changed vehicle development. Instead of every capability being tied to a specific electronic control unit, software can be developed and improved across a centralized architecture.
Consequently, cars have become platforms that can keep improving after leaving the factory. Drivers expect the same up-to-date experience they receive from their smartphones, including new features and a continually refined interface.
Now, we’re seeing the next evolution. The AI-defined vehicle.
In an AI-defined vehicle, performance and behavior are shaped by continuously improving machine learned models, as opposed to hand-coded rules. Advances in neural networks and end-to-end architectures are improving the vehicle’s ability to perceive its surrounding environment, predicting what might happen next and then being able to respond appropriately.
What’s more, AI-defined capabilities increasingly touch the full in-vehicle stack. This includes navigation, infotainment, battery and energy management, HVAC and more. This will prove to be the difference between a car that simply follows instructions, and a car that understands behavioral patterns, including how you drive, what you prefer, and how to support you.
Why compute and connectivity change the pace of innovation
The software-defined shift laid the foundations for today’s AI-defined moment. Centralized compute makes it easier to run more advanced models and fuse more inputs. Continuous connectivity means vehicles can improve over time once they’ve left the factory, whether that’s through updates, new services or data-driven learning loops.
You can see the change in the step from smart features to genuinely adaptive systems. For example, we’ve had voice assistants for years. Now we’re moving toward agentic AI-powered assistants that can handle complex, multi-faceted requests across the car’s systems.
On top of that, whilst we’ve had radar-guided cruise control for a long time, we now have end-to-end perception models that can pick up more subtle cues, like when the vehicle ahead is driving slightly erratically, and adjust accordingly by creating more space to increase stopping distance.
In an AI-defined world, the bottleneck moves from can we build a clever model, towards can we feed it with reliable, high-quality context, and do so at scale?
Sensors alone make a car drive like a tourist
Consider one area where AI-driven innovation is already having a significant impact – advanced driver-assistance systems and automated driving.
There is an industry debate about maps versus sensors. Some argue that sufficiently capable sensors remove the need for broader world knowledge. However, trusted assistance comes from combining what a vehicle sees in the moment with what it knows about the road beyond its immediate field of view.
A vehicle relying only on sensors can end up driving like a tourist. It will be cautious and reactive. It will wait to detect the next cue. On the other hand, a vehicle that fuses sensor perception with road intelligence can behave more like a local. It’ll know the road by heart.
It knows where lanes split, where speed limits change, and where closures or hazards are likely to appear. That supports smoother behavior which means, in turn, less surprise, fewer sudden corrections and, importantly, a more comfortable experience.
This is where mapping and road intelligence platforms matter. Autonomy means understanding context in the right detail at the right time.
A shared foundation that still allows differentiation
It’s important to think about this as a shared ‘world model’ foundation. In other words, as a unified view of the road environment created by fusing multiple inputs. That means including road and lane geometry, traffic, road signs, speed limits, closures and other context, and then streaming that intelligence into the car.
The goal isn’t to make every vehicle feel the same. Rather, it’s for the OEMs to differentiate through brand choices, user interface, comfort, and how assistance is communicated. But there’s also an economic reality. The common foundation is expensive to build and maintain at global scale. Duplicating that effort across OEMs slows progress and increases cost.
Where AI is having meaningful impact
AI delivers real value when it changes process and cost structure. One of the most practical impacts today is scaling road intelligence creation. That means using AI to detect lane details, road markings, stop signs and other attributes from sources like aerial imagery, satellite, vehicle signals and probe GPS patterns. Done well, this reduces the cost of building and maintaining rich road models, and improves freshness, which is essential for safety and driver trust.
That’s the heart of the AI-defined shift. It goes beyond smarter features. Instead, it’s a new loop where vehicles improve as they’re exposed to more scenarios and more data. Critically, it’s where the quality of the underlying road intelligence determines how confident, safe and human the experience can be.
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