Decoded: How does your car know it’s found the right charging station?

How CARIAD turns fragmented data into reliable destination information for drivers. Discover how POI data fusion combines multiple sources with real-world vehicle insights to keep information accurate and up to date.

AI image showing a car riding along a street with several points of interest

A long car journey. The battery is running low and you search for a charging station. You check different sources, such as the IONITY app and Google Maps, and the same place can look quite different: one result is called "IONITY A8 North", another appears as "IONITY München Nord." A third is shown slightly farther down the road on the map. One source reports available chargers, another includes photos and payment information. How to turn those fragmented descriptions into a single place your car can recognize? The point of interest (POI) data fusion function developed by CARIAD does this – and it adds millions of data points from Volkswagen Group vehicles.

The hidden puzzle behind every destination

Managing point of interest (POI) data sounds straightforward: collect information and display it. In practice, the details behind a single destination are scattered across different providers, databases, and systems. Drivers could end up seeing duplicate entries, inconsistent names or incomplete information. The goal is to merge information from third-party providers to create one accurate destination profile.

The POI data fusion function developed by CARIAD brings these different pieces together with insights from vehicles and customer feedback. It enriches and validates the information to ensure drivers see the most complete and up-to-date representation of a place. In other words, the right data at the right time.

How scattered data becomes one clear result

Before a charging station can appear once on your screen, it may exist many times across different datasets such as ELLI, IONITY, Dow Jones and Plugshare. The process begins with raw information. Before it reaches drivers, the data goes through extensive processing.

Information is brought together, duplicates are removed, details are standardized and additional context is added to create a clearer and more complete view of a place.

The process follows four stages:

Extraction – Fresh data enters the process and is checked for inconsistencies. Invalid entries are removed.
Transformation – Information from different sources is organized into a common format. Individual details or attributes are corrected as needed.
Enrichment – Data from different sources is merged, duplicate listings are removed, and information from the vehicle fleet or customer feedback is used to improve details.
Release – The data is validated, final checks are performed, and the cleaned-up version is made available.

Live information follows a separate continuous flow. Updates such as charger availability can be processed and distributed, helping keep information current.

The result is a single, reliable destination record that can be used across current and future infotainment platforms, including SDV, to brand websites and apps worldwide.

Process image with four stages of the data process behind the point of interest function

Why are charging stations so complicated?

The POI data fusion function supports 170 other types of destinations beyond charging. But charging stations pose a particular challenge because the information around them is constantly changing. New chargers are added, charging speeds increase, payment options evolve and stations can become temporarily unavailable.

For drivers, finding a charging station is only the starting point. They may also want to know its charging power, opening hours, Plug & Charge compatibility, nearby amenities or customer feedback. Some stations can even provide insights such as charger occupancy, charging success rates, or the last successful charging session.

These factors help answer practical questions: Will the charger be available? What payment options are supported? Is this the right stop for my journey?

By combining static information with real-time updates, POI data fusion helps create a more complete and current picture of each charging station.

Vehicles help improve the map

Vehicles themselves can also help improve what we know about a place. Fleet data can help confirm or correct a charging station’s position. Your own charging session can add information about typical or maximum charging speed, the last successful session and the proportion of successful charging attempts. Your customer feedback can contribute ratings and reports on problems.

This real-world input helps close gaps. If charging power is missing, fleet data may provide additional insights. If a location is inaccurate, information from vehicles can help correct or confirm it.

"Traditionally, maps were quite static and only updated periodically. Today, data is updated in real time and vehicles themselves help verify locations, identify inconsistencies and add context," says Alexander Schedel, Product Owner at CARIAD.

More than a pin on a map

The next time you search for a charging station, the result may appear almost instantly. Behind it is a continuous process of combining, checking and improving millions of data points. POI data fusion turns that complexity into something drivers never have to think about. And that is exactly the point.