A Look at the Hidden Technology Behind Google Maps

This is the next article in my series, “AI in Everyday Apps.” This one took a little longer than I had promised. But I guess it was worth the wait. 🙂

There was a time when we actually remembered roads. Our local taxi and rickshaw drivers seemed to know every street, every shortcut and every landmark by heart. It always amazed me.

London takes this to another level. To get a taxi driver’s licence, applicants have to pass an exceptionally rigorous test known as “The Knowledge.” It can take three to four years of study, during which they learn around 25,000 streets and 20,000 landmarks so they can navigate the city without a map or GPS.

Today, that seems almost unimaginable.

We hardly think twice before opening Google Maps. Enter a destination, tap a button, and there it is, the route, the turns, the traffic, the estimated time of arrival. We share our locations through it, and entire businesses, from cab aggregators to delivery services, depend on maps to function.

But have you ever wondered what is actually happening behind that blue line?

Today, I want to look at the AI behind Google Maps. There are many mapping apps, but we'll use Google Maps to understand what goes on behind the scenes.

BTW, a little side recommendation: If you're curious about how Google Maps came to be, Never Lost Again by Bill Kilday is a fascinating behind-the-scenes story of the creation of one of the most essential applications ever devised and the team that built it and changed how we navigate the world.

One thing to keep in mind as we go along: what makes Google Maps work is a combination of engineering and AI. Not everything happening behind the scenes is AI, but AI is increasingly making that engineering smarter. Let's start the journey!

Step 1: Google Maps knows your current location

As you open Google Maps, a blue dot instantly appears to mark your position. The first thing Maps has to do is figure out where you are. This isn't primarily AI; it's a clever combination of GPS, sensor fusion, signal matching and other algorithms. While it’s easy to assume this relies solely on GPS, real life is rarely that simple. Satellite signals bounce off tall buildings, get blocked indoors, and update slowly. If Google Maps relied on GPS alone, your blue dot would constantly jump around. When relaxing at home, for instance, satellite signals rarely penetrate walls and roofs. This is where Google's Fused Location Provider comes in. It intelligently combines signals from GPS, Wi-Fi, cell towers and other sensors to produce the best estimate of where you are. Several things are happening in the background to work out where you are:

• GPS gives the starting point: Your phone receives signals from GPS satellites and calculates your approximate location. Outdoors, this can be quite accurate, but it isn't always reliable, especially indoors or between tall buildings.

• Wi-Fi and cell towers add another clue: When GPS is weak, your phone can look for nearby Wi-Fi networks and cell towers. Google has information about the approximate locations of many of these signals, gathered from repeated observations. Matching the signals your phone sees with these known locations, help narrow down where you are.

• The signals are combined: No single source is perfect. The system combines GPS, Wi-Fi, cellular networks and other available signals to arrive at the most likely location. If GPS is strong, it may be given more weight; if you're indoors and GPS is poor, Wi-Fi or cellular signals may become more useful.

• Your phone's sensors fill in the gaps: The accelerometer and gyroscope tell your phone that you're moving, how fast or in which direction you're moving, and whether you've turned or stopped. This helps the system keep track of your movement between location updates.

• The map itself provides another check: Once Maps has an approximate location, it knows where the roads, buildings and pathways are. If your location estimate puts you in the middle of a building but you're clearly moving along a road, the system can use that information to refine where it thinks you are. This process is often called map matching.

All of this happens continuously as you move. The blue dot you see isn't simply your GPS location. It is the system's best estimate of where you are, calculated from several imperfect sources that are constantly being updated and checked against one another.

Step 2: You enter your destination

Let's say you type: Mumbai Airport

Seems simple. But people rarely search perfectly.

One person might type BOM, another T2, someone else Mumbai International Airport, while a tourist may simply type airport near me. They may all be looking for the same place.

So Google Maps first has to understand what you mean, not just what you typed. This is where AI plays a big role.

When you enter a destination, Maps uses Natural Language Processing (NLP) and increasingly Large Language Models (LLMs) to understand the meaning behind your words. It isn't simply matching keywords against a digital phonebook. It is trying to understand your intent, even when your query is incomplete, vague, misspelled or conversational.

There are several things happening here:

• Understanding your intent (Technology: Natural Language Processing and Large Language Models): If you type "quiet café with Wi-Fi" or "cozy place for dinner near me," Maps has to understand the kind of place you're looking for. NLP helps break down the meaning of the words, while newer AI models can understand more complex, conversational requests and connect them with information about places, reviews and attributes.

• Figuring out the exact place (Technology: Entity Resolution and Geocoding): Suppose you type "eifel towr" or "McDonald's near Bandra station." Maps has to work out which specific place you mean. Entity resolution helps identify the place, while geocoding converts a place or address into geographic coordinates, its exact location on the map.

• Guessing what you might want (Technology: Contextual Prediction): Even before you finish typing, Maps is trying to be helpful. It can use context such as your current location, the time and day, and sometimes your previous activity to suggest likely destinations. A coffee shop might be more relevant in the morning; a petrol station might be more useful when you're already driving.

• Deciding what to show first (Technology: Ranking): Finally, Maps has to choose which results should appear at the top. Ranking algorithms consider factors such as distance, opening hours, ratings, popularity, current busyness and other relevant information to decide which results are most useful to you.

So that one little search box is doing quite a bit of work. 

You have told Maps where you want to go. Now Maps has to figure out how to get you there.

Step 3: Google gives you the best route

Now Google Maps knows two things: where you are and where you want to go.

The obvious question is, which road should you take?

At first, this sounds like a simple problem. Maps could simply look at the roads on its map and find the shortest path between two points. But the real world is constantly changing. A road that is fastest right now may be crawling with traffic 20 minutes from now. An accident may block a road, rain may slow down traffic, or a sudden rush of vehicles may create a new bottleneck. Maps therefore has to do much more than find a path. It has to predict what the roads will look like by the time you get there. 

This is where the AI comes in. 

1. Predicting what traffic will look like

Google, working with DeepMind, developed Graph Neural Networks (GNNs) that look at the road network as a connected system. Instead of looking only at traffic right now, these models learn how traffic moves from one road to another and predict how congestion is likely to develop in the next few minutes.

To make these predictions, Maps combines live traffic data from vehicles and smartphones with years of historical traffic patterns. Historical data helps it understand what usually happens on a particular road at a particular time and day, while live data tells it what is happening right now.

So Maps isn't just asking, "What's the traffic like now?" It is asking, "What is the traffic likely to be when you get there?"

2. Estimating your arrival time

Once Maps has an idea of how traffic is likely to behave, it can estimate how long each part of your journey will take. Put all those estimates together and you get your ETA, or estimated time of arrival.

This is why your ETA isn't simply based on the distance you have to travel. It is a prediction of how long the journey is likely to take under the conditions Maps expects you to encounter.

3. Choosing the best route

Now Maps has to choose among the possible routes.

And "best" doesn't necessarily mean the shortest.

Recommendation and optimization algorithms can consider travel time along with things such as tolls, fuel efficiency and road conditions. This is also where Maps can offer an eco-friendly route that may use less fuel even if it isn't the absolute fastest.

So Maps is essentially balancing several competing factors to find the route that makes the most sense for you.

4. Detecting what has suddenly changed

But what if something unexpected happens?

If vehicles that were moving normally suddenly slow down in the same location, Maps can detect this unusual change in traffic. It may indicate an accident, road closure or some other disruption.

That information can then be fed back into the system, changing the estimated travel times and potentially changing the route Maps recommends.

So the route you see isn't simply the shortest path between two points. It is the result of Maps combining huge amounts of live and historical data, predicting what is likely to happen on the roads, and then choosing the route it believes will get you there most efficiently given what it knows right now.

Step 4: Rerouting and suggesting alternate routes

You’re on your way when suddenly traffic slows down. An accident? A road closure? A bottleneck?

Maps gets much of this information through crowdsourcing. Millions of phones and vehicles on the road continuously provide aggregated location and speed data. Driver reports add another layer of information.

When several vehicles suddenly slow down at the same spot, AI-based anomaly detection can recognise that something unusual is happening. Maps can then combine these signals to understand the disruption.

But detecting a problem is only half the job. Maps now has to decide: should you take another route?

Its traffic prediction and routing models compare alternatives and estimate how long they are likely to take, including how traffic may build up on those roads. If another route is genuinely faster, Maps may simply tell you: “5 minutes faster.”

And the process continues throughout the journey. As new information comes in, Maps can recalculate your route and change the plan.

Other AI features in Google Maps

The navigation journey is perhaps the most obvious place where AI works behind the scenes. But Google Maps uses AI in many other ways too.

1. Google Maps talks to you and you can talk to Maps while you drive

Google Maps doesn't just show you the route. It talks you through it.

“Turn left onto LBS Road.” “In 500 metres, take the exit.” “Keep right.”

Behind this seemingly simple voice guidance are several AI capabilities. Text-to-Speech (TTS) converts navigation instructions and road names into natural-sounding speech, while Natural Language Processing (NLP) helps generate instructions that are clear and relevant to your position on the route.

Maps also has to decide when to speak. It continuously tracks your location and movement and triggers the appropriate instruction at the right moment, so that you hear the next turn when you actually need it.

And this is now becoming conversational. With Gemini multimodal AI models, you can also talk to Maps while driving, ask for places along your route, get information about them, or ask follow-up questions through Ask Maps.

So AI is doing two things at once: Maps can talk to you, and you can talk to Maps.

2. Understanding places and reviews

Google Maps contains an enormous amount of information about places: reviews, photos, opening hours, menus, services and more. AI helps turn all of this information into something useful.

Large Language Models (LLMs) can analyse large volumes of reviews and summarise what people are saying about a place. Instead of reading hundreds of reviews, you might get a quick sense of what visitors like, what they don't, or what the place is particularly known for.

3. Seeing the world through your camera

Maps can also use AI to connect what your camera sees with places around you.

With Google Lens in Maps, you can point your camera at your surroundings and ask what is nearby. Computer vision identifies objects, buildings and landmarks, while AI connects what the camera sees with Google's geographic and place data.

Your camera effectively becomes another way of searching the map.

And we hardly think about any of this

Google Maps is not perfect. It does make mistakes, particularly in those last few metres when you are almost at your destination and somehow Maps seems to have its own idea of where the entrance is!

But that hardly diminishes what it has become.

Something that once required years of memorising roads and landmarks is now something we take completely for granted. We simply enter a destination and expect Maps to know where we are, find the best way to get there, anticipate traffic, reroute us when things change, and increasingly even talk to us along the way.

And behind that deceptively simple experience is a remarkable amount of AI: machine learning, computer vision, natural language processing, speech recognition, predictive models and now large language models.

Most of the time, we don't notice any of it. And perhaps that is the real achievement.

AI is quietly working behind the scenes, making an already indispensable part of our daily lives a little more useful, a little more intelligent, and hopefully, a little easier.