When you work with automotive IoT (Internet of Things) projects, pitch decks often look promising. They offer multiple benefits like real-time fleet visibility or predictive maintenance. Then, you move to a connected car pilot project with three vehicles, and it works well on a test track.
But scaling that same system to two thousand trucks across five countries? This is an entirely different story – and one where issues always arise. For example, the 2025 State of IoT research from the global telecommunications company Eseye states that 66% of businesses experience connectivity problems with IoT projects. Moreover, 76% of respondents agreed that a ‘hardware blind spot’ undermined their projects’ success.
In this case, what do you do so your project does not fall into the failed category?
In this guide, our team breaks down everything you need to know about automotive IoT solutions. We look inside prominent use cases and investigate the issues that make projects stall, so you know what to look for when you start one.
What is automotive IoT?
Automotive IoT refers to the network of connected vehicles, sensors, software, and cloud platforms that collect, exchange, and analyse data in real time. The system performs vehicle diagnostics, predictive maintenance, fleet management, remote monitoring, over‑the‑air updates, and improved driver and passenger experiences.
The capabilities that define it include the following:
- Edge layer – wheel-speed sensors, electronic control units (ECUs), OBD-II dongles, telematics units
- Connectivity layer – CAN bus for intra-vehicle messages, BLE for tyre pressure monitors, LTE/5G for backend, V2X for vehicle-to-infrastructure
- Gateway layer – an on-board IoT gateway aggregates data, applies edge rules, and enforces security policies before transmission
- Cloud layer – data lands in Kafka topics, is processed in real-time, stored in a database, and triggers OTA updates
- Application layer – fleet dashboards and driver apps that act on alerts.
Together, these layers form the foundation of modern connected mobility products.
The automotive IoT market
Based on the statistics from the Automotive IoT Market research (2025, updated in May 2026) by Fortune Business Insights, the global automotive Internet of Things market was valued at $32.42 billion in 2025. This number is expected to reach $37.20 billion in 2026. Asia Pacific is holding the biggest market share with 40.78%.
Additionally, Statista estimates that in 2025, there were over 400 million connected cars in operation, up from some 237 million in 2021.
The market growth can be attributed to several factors, such as:
- increasing penetration of vehicle telematics devices
- expansion of fleet digitisation initiatives
- rising demand for vehicle tracking solutions
- growing adoption of connected car technologies
- availability of cloud-based automotive platforms.
Major companies that operate in the automotive IoT market are Texas Instruments, NXP Semiconductors, Intel, TomTom, Cisco, Microsoft, IBM, Google, AT&T, Apple, Bosch, Harman, Infineon Technologies, Octo Group, Huawei, Tesla, Alibaba, Orange, Aeris Communications, KORE Wireless Group, Eurotech, STMicroelectronics, Sierra Wireless, Verizon, Telefónica, Mitsubishi Electric, and Tech Mahindra.
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Core automotive IoT use cases – with implementation notes and real-world examples
The automotive IoT niche itself is complex, with a variety of applications, each having its own benefits and limitations. We look at the six most trending ones that define the sector – and highlight how they work and why you might need them based on the examples of companies you may recognise.
Fleet and asset tracking
When it comes to IoT automotive industry initiatives, fleet and asset management can help you solve some of the most burning issues like rising fuel costs, underutilised assets, unpredictable maintenance, or liability exposure.
Why you need it
Without real-time location and status data, your fleet wastes money. The reasons may vary, for example, engines idle and burn fuel. Moreover, drivers can take unauthorised routes – which increases risk – and manual odometer checks can cause you to miss maintenance windows.
If you want to stop these leaks, you need to implement fleet and asset tracking. This way, you will see a live map of every vehicle – its position, speed, engine hours, and fault codes – as well as your assets’ condition and maintenance needs.
How it works
Modern fleet tracking pulls together data from several sources:
- telematics control units (TCUs)
- OBD-II adapters
- CAN bus interfaces
- GPS modules
- accelerometers
- cloud platforms.
The systems constantly report on vehicle position and asset status. From there, the data moves over cellular networks into cloud infrastructure, then finally appears on dashboards or mobile apps.
Application types
- Fleet management and telematics
A delivery company, for example, might combine GPS data with engine diagnostics and route information. If one vehicle spends too much time in traffic or burns more fuel than others on the same route, managers can check the cause and then adjust operations accordingly.
In fleet management and telematics, data consistency presents a major challenge. Your vehicles frequently report similar parameters, but they do so in different formats or at different update intervals. As a result, development teams spend considerable effort building normalisation layers to convert raw vehicle signals into a common data model.
- Usage-based insurance (UBI)
Usage-based insurance focuses on driving behaviour. Insurers employ it to evaluate how, when, and where customers drive rather than simply calculate premiums from demographic averages. First, connected vehicles supply information from mileage and acceleration to harsh braking and trip duration. Then, scoring algorithms translate these signals into driver risk profiles.
This approach creates more accurate pricing models for insurers. Meanwhile, for drivers, safer habits can lead to lower premiums.
Still, UBI systems face technical hurdles. Sensor accuracy matters, as small errors can distort driver scores. Connectivity interruptions may leave gaps in trip records. Privacy regulations also restrict how companies collect, store, and process location data.
Many successful deployments solve these problems with edge processing. Instead of sending every raw signal to the cloud, software inside the vehicle calculates driving events locally and sends only the information needed for risk assessment.
- Remote access and vehicle control
Remote vehicle functions are now widely available in most fleet operations, not just premium cars. Using connected platforms, operators interact with vehicles from anywhere. In this application type, capabilities vary from remote lock and unlock to climate control activation and geofence alerts.
As an illustration, we may look at any carsharing service, as they work in a similar way. Customers book a car through a mobile app and receive digital credentials. Next, they unlock the vehicle remotely and start the trip. This experience may seem easy, but it depends on reliable communication among cloud services, mobile apps, and vehicle hardware.
Such use cases are based on stricter demands than basic tracking. Commands travel from cloud to vehicle, so developers must address latency and authentication – as well as delivery confirmation and cybersecurity. If a remote unlock arrives minutes late, that frustrates users. At the same time, a command sent to the wrong vehicle becomes a security incident.
Real-life examples of fleet tracking solutions
If you look at real-world examples, the multinational logistics giant DHL has long established itself as an innovator in fleet and asset tracking. In 2020, the company launched its DHL SmarTransport solution in India. This transportation model not only reduces transit time but also prevents fatigue among drivers.
Bamboo Apps’ automotive IoT development services have also covered a number of fleet tracking projects. Among them, we can distinguish an intelligent safety and monitoring system for motorcycle fleets. It is a comprehensive solution that includes two major interrelated systems, namely, AI-powered rider assistance and a cloud-based system for fleet monitoring and analytics. The assistance system uses sensors and computer vision to track the rider’s full surroundings in real time. In turn, the monitoring and analytics system delivers data to fleet operators and regulators.
Additionally, Bamboo Apps has worked on a proprietary digital key solution as an R&D project. The team built a digital key app for both mobile devices and the car’s infotainment screen. Employing proximity technology, drivers open their vehicle with a smart device. Users can share digital keys with other devices and revoke them at any time.
The app runs on an improved BLE communication protocol that adds more data encryption layers and provides a smooth auto‑locking and auto‑unlocking experience.
Last but not least, Bamboo Apps helped Jaguar Land Rover (JLR) redesign and modernise a version of the InControl Remote app suite. We assisted JLR in rebuilding the solution’s architecture, updating the mobile UI, and developing new iOS and Android versions of the app.
V2X communication
Most people describe vehicle-to-everything (V2X) as cars talking to things around them. But the value of V2X is based on how fast software can collect, process, send, and then act on data. If a warning arrives half a second too late, it offers little value to cars travelling at highway speeds. Therefore, software latency often decides whether a V2X system actually works in production.
V2X covers three primary communication models:
- Vehicle-to-vehicle (V2V) – direct communication between vehicles
- Vehicle-to-infrastructure (V2I) – communication between vehicles and roadside infrastructure (e.g., traffic lights, road sensors, toll systems, and traffic management platforms)
- Vehicle-to-pedestrian (V2P) – communication between vehicles and vulnerable road users that carry connected devices, such as smartphones or wearables.
Why companies invest in V2X
In the automotive IoT industry, V2X helps cut accident rates. It makes route timing more predictable and spots road conditions earlier. For example, a connected truck can get braking alerts from vehicles hundreds of meters ahead. Another instance can be a city bus that receives traffic signal timing and adjusts its speed. Or even a passenger car that can detect a pedestrian approaching a blind intersection, even when cameras or LiDAR lack a direct view.
The payoff goes beyond safety. Fleets see fewer sudden stops and gather richer operational data to plan ahead. At the same time, vehicles burn less fuel and arrive more reliably.
How V2X works
Most production-grade V2X systems use either dedicated short-range communications (DSRC) or cellular vehicle-to-everything (C-V2X) standards.
The vehicle continuously broadcasts and receives safety messages with data, from location and speed to acceleration. Software then compares incoming messages against local sensor readings and decides whether to act.
While this may sound simple, any V2X project comes with strict latency requirements. A V2X platform must process messages within milliseconds and juggle fluctuating network quality and cybersecurity demands. For this reason, decision-making rests closer to the edge rather than routes every event through a central cloud platform. Critical collision warnings usually stay inside the vehicle or roadside infrastructure, where software can react instantly.
Real-life examples
V2X projects are becoming more and more widespread across countries and city municipalities, and it shows that the demand for automotive IoT connectivity is growing. In 2025, the Automotive Technology Centre of Galicia started testing an autonomous shuttle bus that uses V2X communication along a cooperative intelligent transport systems (or C‑ITS) corridor. On this connected route, the shuttle talks to traffic lights and also detects pedestrians.
One year prior, Oakland County in Michigan, United States, prototyped a connected vehicle (CV) system. It was deployed at five intersections and in ten vehicles to test traffic signal priority and vulnerable road user detection. Field testing demonstrated that the traffic signal priority feature reduced intersection travel time by an average of 10 seconds per vehicle pass.
Bamboo Apps’ portfolio of automotive IoT projects includes Parkyy, a V2I parking management solution. Built for a major service provider in Saudi Arabia, the Parkyy app facilitates parking in congested cities and includes multiple additional features, such as a tenant portal and scooter integration.
Vehicle health monitoring and predictive maintenance
Long before a vehicle component stops working, sensors detect subtle shifts in temperature or vibration – or maybe voltage or fluid levels. Vehicle health monitoring helps organisations catch these patterns early and act before a minor issue turns into an expensive repair.
Why you need continuous vehicle health monitoring
When you manage a vehicle fleet, predictive maintenance results in a smaller number of unexpected breakdowns and higher vehicle availability. With this approach, maintenance schedules depend on actual component conditions instead of fixed service intervals. For example, a highway truck experiences different wear than a vehicle stuck in stop-and-go city traffic. Condition-based maintenance accounts for those differences.
Continuous health monitoring supports remote diagnostics and lowers warranty costs. As connected fleets grow, these systems create opportunities for subscription-based maintenance and uptime services.
How it works
The process starts with sensor fusion, as one sensor never tells the full story. Let us say, a rise in engine temperature could reflect normal operation. When that same signal appears with unusual vibration and increased engine load, however, it may point to an emerging mechanical issue. Sensor fusion combines multiple data streams to produce a more accurate picture of vehicle health.
Once data leaves the vehicle systems, a machine learning-based pipeline turns raw telemetry into maintenance recommendations.
In this case, data quality often becomes the major issue. Sensors may drift over time, or communication links may fail. Moreover, environmental conditions add noise to telemetry streams. Machine learning models also need substantial historical data before they can confidently separate normal behaviour from genuine faults.
The architecture itself raises another decision point – where to process data.
Edge processing performs analytics inside the vehicle through telematics control units or onboard compute platforms. This approach supports near real‑time responses and reduces the amount of data sent over cellular networks.
Cloud processing centralises analytics across large vehicle populations. Cloud platforms can compare behaviour across thousands of vehicles and train more advanced machine learning models.
Here is a comparison of edge processing versus cloud processing.
| Edge processing | Cloud processing |
|---|---|
| Runs inside the vehicle | Runs in centralised infrastructure |
| Low latency for time‑sensitive alerts | Higher latency due to data transmission |
| Lower bandwidth consumption | Higher data transfer requirements |
| Works even with limited connectivity | Depends on network availability |
| Supports immediate anomaly detection | Supports fleet‑wide analytics |
| Limited by onboard computing resources | Scales to complex ML workloads |
| Best for operational decisions | Best for long‑term insights and model training |
In IoT, automotive industry professionals often combine both approaches.
Real-life examples
A recent (2025) Road Intelligence for London Mobility project by Compass IoT and Transport for London (TfL) applies a predictive maintenance framework to road infrastructure. It uses connected vehicle data to monitor the ‘health’ of London’s transport network. Instead of waiting for crashes to occur, the platform continuously analyses telemetry data to identify high-risk junctions and collision clusters before serious incidents happen. The project fuses real-time vehicle data with Transport for London’s historical crash records and bus telemetry.
When it comes to Bamboo Apps’ experience with IoT in automotive applications for health monitoring, our team has built a proprietary solution to monitor and manage remote autonomous vehicles – in this particular case, shuttles. Using the system, the dispatcher receives an instant notification for every emergency. An alert goes out for a slippery road, a major obstacle, a passenger falling, someone pressing the emergency stop button, a low battery, low tyre pressure, or other incidents.
EV charging management
EV charging management relies on software and hardware that monitor and improve charging operations. Fleets and charge point operators use these systems to reduce electrical upgrade costs and avoid overloading the power grid.
Why you need it
A well-designed system can deliver multiple advantages:
- Charging schedules coordinate with vehicle availability and grid tariffs to lower peak-hour costs
- Fleet operators distribute load across depots to prevent transformer overloads
- Charge point operators manage pricing and utilisation from one interface
- Maintenance teams detect faulty chargers early through telemetry.
Without coordinated control, energy spikes lead to expensive grid upgrades or forced load shedding.
How EV charging management works
An EV charging management platform links vehicles and chargers – as well as cloud services and energy providers – into one system.
Chargers provide telemetry and control signals through protocols such as Open Charge Point Protocol (OCPP). This standard defines how a backend adjusts charging sessions and reads real-time status. On the vehicle side, ISO 15118 supports smarter interactions like plug-and-charge and battery-aware charging decisions.
In an automotive IoT platform for EV charging management, bottlenecks can appear due to inconsistent charger behaviour. Engineering teams spend a large share of effort on reconciliation layers – adapters that translate this behaviour into a unified control model.
Real-life examples
In 2026, Iberdrola-BP Pulse and GaleoTech built EVBrain, a cloud-based EV charging monitoring platform on AWS. The platform processes OCPP data from hundreds of charging points across Spain in real time. It detects 26 types of incidents – from connector faults to anomalous energy delivery – before customers notice a problem.
Six dashboards give operators network-wide visibility, while an AI assistant answers natural language queries about recharges and incidents. The system also monitors low-voltage cabinets and permits remote circuit breaker management. Field testing showed that proactive detection and sub-minute latency reduced charger downtime and prevented customer complaints.
Driver monitoring systems
Driver monitoring systems (DMS) have transitioned from a nice-to-have feature into a product requirement that demands careful engineering. Under the EU’s General Safety Regulation (GSR 2022), new vehicle models must include drowsiness and attention warnings. Also, advanced distraction detection now applies to more vehicle categories.
Why you need it
Driver distraction and fatigue remain major causes of road accidents. A driver who checks a smartphone for three seconds at highway speed travels more than 100 meters without watching the road. In this case, traditional safety systems react only after a dangerous situation occurs. DMS tries to spot the problem before that point.
For fleet operators, DMS can lower accident rates, insurance claims, vehicle downtime, and liability costs. OEMs use DMS as part of broader advanced driver assistance systems (ADAS) and automated driving strategies, where the vehicle needs to decide whether the driver can safely take over control.
How driver behaviour monitoring works
Most modern DMS platforms rely on one of two architectures – camera-based or sensor-based monitoring.
Camera-based systems use infrared cameras or RGB cameras, sometimes both. Computer vision models track facial landmarks, eye openness, blink frequency, gaze direction, head position, and facial expressions.
Both types of architecture come with their cons. In camera-based DMS, sunglasses can reduce eye-tracking accuracy – and poor cabin lighting adds complexity. Drivers with different facial features, seating positions, and body proportions also introduce edge cases that require additional model training and validation.
Sensor-based approaches usually consume less computing power and avoid some privacy concerns tied to in-cabin cameras. On the other hand, they often struggle to tell distraction apart from other driving conditions. A driver might make fewer steering corrections simply because the road stays straight, not because fatigue has set in.
As a result, many modern vehicle platforms combine both approaches. Camera data supplies direct information about driver attention, while vehicle telemetry provides extra context that improves decision-making.
Real-life examples
In 2025, Mercedes Trucks launched a Safety Truck demonstration vehicle based on the battery-electric eActros 600. The truck targets typical commercial vehicle accidents such as rear-end collisions, lane departures, side impacts, and blind spot incidents with vulnerable road users. Its enhanced electronics platform delivers 270-degree sensor coverage with 20 times higher data processing power through fusion of radar and camera data.
Bamboo Apps can add its own experience to these automotive IoT use cases. One of our internal R&D projects – Self-Drive – is an AI vehicle acquisition application PoC. The software guides the driver along a set course and gives context about the vehicle’s capabilities. Additionally, the AI acts as a voice assistant that uses car data, computer vision, and machine learning. Drivers can operate it with voice commands or by touch.
We also worked on a mobile prototype for a behaviour tracking app that scores driver performance on several factors – speeding, braking, phone use, swerving, cornering, and jerking. The app monitors driving style and records important events during each trip. Then, it turns that data into easy-to-read analytics and reports.
Over-the-air (OTA) software updates
Vehicle software grows more complex by the day. A modern connected vehicle can pack over 100 ECUs running millions of lines of code. Without OTA capabilities, every security patch or new feature forces a dealership visit, which costs manufacturers money and takes vehicles off the road for owners.
Why OTA is a must
OTA updates let automotive companies maintain vehicles throughout their life. Teams can fix software defects faster, close newly discovered security gaps, add features after delivery, and support subscription services. Fleet operators gain another advantage – they can update hundreds or thousands of vehicles remotely instead of scheduling service appointments across multiple locations.
For software-defined vehicles, OTA updates also allow continuous product improvement. Manufacturers can introduce new capabilities through software releases instead of waiting for the next vehicle generation, and they can respond faster to customer feedback.
How OTA works
An OTA platform connects cloud infrastructure with software components inside the vehicle. First, engineers package a software update and upload it to a cloud-based management platform. The system then distributes the update to selected vehicles over cellular or Wi-Fi networks.
After a vehicle receives an update notification, it downloads the package, verifies the digital signature, installs the software, and reboots any affected systems. Cloud services track deployment progress, software versions, update campaigns, and installation results across the whole vehicle fleet.
In OTA updates, cybersecurity demands constant attention. An attacker who compromises the update infrastructure could distribute malicious software across an entire vehicle fleet. For that reason, OTA architectures require strict authentication and end-to-end deployment monitoring.
Real-life examples
As a major player in the automotive IoT market, Tesla has made OTA updates a standard feature in connected vehicles. The company regularly delivers performance improvements, new user interface features, bug fixes, and functionality updates remotely. Owners often receive capabilities that did not exist when they bought the car – all without a trip to the service centre.
Where IoT automotive projects struggle
We asked Maxim Leykin, CTO at Bamboo Apps, to share his view on challenges in automotive IoT. Here is what he has to say.
In my experience, Automotive IoT is one of the most demanding IoT environments, because cars are mobile, safety-critical, long-lived, and operate under harsh real-world conditions. Speaking in more detail, I can outline the following limitations:
Automotive is a highly regulated domain, and requirements substantially vary across countries and even regions (e.g., in one of our projects, we faced different safety regulations for different American states).
Modern vehicles contain dozens of ECUs and multiple internal networks. Data comes from everything, from engine systems to engineering modules. The challenge is to normalise and interpret it consistently across different vehicle models and manufacturers.
The vehicle is constantly moving; thus, network coverage and connectivity costs change every minute. Also, GPS signals can be degraded in tunnels or parking garages. Despite all this, data must continue flowing even with intermittent connectivity.
Cars are, obviously, ‘safety-first’ devices, which means automotive systems require redundancy, security, extensive testing, fail-safe behaviour, and careful separation between infotainment and critical vehicle systems.
Considering all this, to make automotive IoT actually work, we need to provide:
Maxim Leykin, Chief Technology Officer at Bamboo Apps
- Edge-first architecture
- Strong observability
- Security by design
- Data standardisation
- Reliable device management
- Solid OTA updates.
If we sum up Maxim’s point of view with the factors we have already mentioned in the article, we can create a clear picture of the challenges faced by automotive IoT projects:
| Challenge area | Why it becomes difficult in automotive IoT | What teams face in practice |
|---|---|---|
| Connectivity fragmentation | Vehicles combine multiple communication technologies | Engineers must bridge CAN, LIN, Automotive Ethernet, BLE, LTE, and 5G networks, as well as translate proprietary signal definitions, synchronise timestamps, and maintain data consistency across systems. |
| Cybersecurity at ECU level | Every external connection creates another path that attackers may target. | Teams must implement secure boot, Hardware Security Modules (HSMs), cryptographic key management, OTA package signing, secure diagnostics, and intrusion detection mechanisms – as well as align development processes with ISO/SAE 21434 and UN ECE WP.29. |
| Real-time limitations | Automotive software operates under deterministic timing requirements that differ from traditional cloud-based IoT architectures. | Vehicle functions running on AUTOSAR platforms cannot compete for resources with telemetry collection or cloud synchronisation tasks. Engineering teams must carefully separate safety-critical workloads from analytics pipelines. |
| Data volume versus transmission cost | Vehicles generate far more data than organisations can economically transmit, store, and process. | Teams must decide which information belongs in the cloud and which should remain at the edge. |
| Regulatory patchwork | Automotive IoT solutions operate across markets that follow different rules. | Data retention policies, consent management, encryption strategies, and audit capabilities should define architecture decisions long before deployment. |
| OEM and Tier-1 integration barriers | Access to vehicle data rarely comes automatically. | Suppliers often need approvals for network access, signal documentation, cybersecurity reviews, validation activities, and multi-vendor testing. |
What to look for in an IoT development partner
Choosing an IoT partner for automotive projects often comes down to risk management, not feature checklists. The wrong choice can force a full redesign once real vehicles and real regulations enter the picture. To help you make the right decision, here is a list of questions you are free to ask your service provider before you commit to a project.
Domain experience
- Does the team work with CAN, LIN, and Automotive Ethernet?
- Do the engineers understand how these protocols interact inside real vehicle architectures?
- Have they handled production exposure, not just prototypes?
Automotive IoT security (ISO/SAE 21434)
- Does the team perform threat analysis and risk assessment early and map attack paths from BLE pairing to cloud APIs?
- Have your developers implemented secure boot, certificate rotation, and OTA integrity?
- Do OTA update flows include rollback strategies and cryptographic verification from day one?
HMI/UX
- Does the team treat HMI as part of the system architecture?
- Do your professionals have experience with automotive-grade UX constraints like limited driver attention and multi-screen consistency between mobile and in-car interfaces?
- Can you show examples of interaction flows designed under real driving conditions?
Connectivity
- Does the team understand how BLE, cellular, Wi-Fi, and V2X interact rather than treating them as separate modules?
- Have you built systems across cellular telematics stacks, MQTT cloud messaging, and in-vehicle edge computing?
- Do they have field-tested experience with network transitions (e.g., cellular to Wi-Fi during garage service)?
Production references
- Can the team show production deployments that have run across thousands of vehicles over the years?
- Do they provide OTA update history, device failure rates, and post-software-evolution behaviour?
Bamboo Apps’ automotive IoT development services
If you are looking for an automotive IoT company with real-world experience, Bamboo Apps can be your trusted tech partner. Over the years, we have worked with the biggest names in the industry – like Mitsubishi Electric and Jaguar Land Rover – to deliver successful projects in multiple domains, from car connectivity to fleet management. We help you choose the right stack based on your exact needs, not industry hype, and can support your project at any stage of the development process.
Let’s discuss your IoT automotive hurdles and select the perfect solution for them
FAQ
How does IoT improve vehicle safety?
IoT connects vehicles through sensors, telematics units, and ADAS modules. It sends live data to cloud platforms. Fleet systems detect hard braking, driver fatigue, or engine problems, then alert drivers or operators. Vehicle-to-everything communication adds awareness of road conditions. This helps reduce crashes and supports faster response in critical situations.
What connectivity technologies are used in automotive IoT?
Automotive IoT depends on LTE and 5G cellular networks for telemetry and over-the-air updates. For V2X communication, both C-V2X and DSRC provide support. Inside the cabin, Wi-Fi and Bluetooth handle connections. For low-power tracking, NB-IoT and LTE‑M serve the role. GNSS delivers positioning, whereas Ethernet and the CAN bus connect in‑vehicle systems.
How does OTA update work in connected cars?
For a connected car to receive an over‑the‑air update, new firmware travels from a cloud platform to the vehicle’s electronic control units by way of the telematics gateway. First, engineers prepare the update packages and add cryptographic signatures. Then, they distribute the packages through MQTT or HTTPS, using services such as AWS IoT Core or Azure IoT Hub. After the vehicle receives the files, its systems validate the update, install it, and prepare a rollback option in case the installation fails.
