From manual processes to automated operations
Industrial and logistics sites are undergoing a fundamental digital transformation. Rising transport volumes, labour shortages, cost pressure, and limited space are colliding with ever-increasing demands for transparency, safety, and efficiency. Many processes are still manual, fragmented, and heavily dependent on human drivers – with clear limits in terms of scalability and stability.
This is exactly where Industrial Automated Driving (IAD) comes in. It is now possible to coordinate up to 100 vehicles on an industrial site, each driving without any human drivers. In addition to labour savings, IAD also creates space and time savings. In harbour environments, for example, vehicles are currently driven from parking areas onto ships by human drivers. This process is limited by the number of drivers that can be transported to the parking area by shuttle bus, as well as by the time needed for each driver to return after loading. IAD removes these constraints, reducing loading and unloading times and, in turn, shortening the time transport ships spend at the dock.
Autonomous Driving vs. Automated Driving vs. Teleoperated Driving
The main difference between these technologies is where the sensors and trajectory planning logic happens. The other difference is the environment in which they operate.
In autonomous driving, sensors are integrated into the vehicle, and trajectory planning and driving decisions are processed on board. While network connectivity may support services such as map updates or traffic information, the vehicle remains the primary decision-making unit. This increases vehicle complexity and cost.
By contrast, Automated Driving relies on external sensors, typically LiDAR, to detect vehicle position as well as surrounding obstacles and traffic. Trajectory planning and driving commands are generated in an external cloud environment and transmitted to the vehicle via mobile networks. This approach enables newer vehicle classes to operate with minimal modification.
The third type of service is Teleoperated driving. This sharply contrasts with the other two types of service, already described above, in that a human operator is involved. Sensors and cameras on the vehicle feed live information back to a remotely located human driver using a car simulator. The simulator is essentially a digital twin of the car. The human driver is actually steering and controlling the vehicle so that minimum computation is required. The greatest challenge here is the connectivity required over mobile networks. Cameras and other sensors are generating large amounts of uplink data from the vehicles and very low latency is required to allow the human drivers responses to be received back at the vehicle with fast enough reaction time.
Autonomous, automated, and teleoperated.
Autonomous Driving & ADAS
– Public areas
– All sensors are inside the verhicle, e.g. cameras, LiDARs
– The driving logic (software) is inside the vehicle
Automated Driving
– Hubs/Ports/Non-public areas
– All sensors are outside, e.g., LiDARs on poles or buildings
– The driving logic (software) resides outside the vehicle
Teleoperated Services
– Overarching services on all areas
– Focus on teleoperation services by a remote driver (driving logic)
– The sensors (cameras) are on bord and inside the vehicle
Automated driving enables series standard vehicles to be manoeuvred and parked in controlled environments such as logistics hubs, harbours or in factories. Autonomous driving enables specially designed vehicles to drive on public roads even in cases where there is no network connection.
More than autonomous vehicles: a systems approach
Industrial Automated Driving does not mean simply “letting vehicles drive autonomously”. The real added value comes from end-to-end automation: from mission planning and fleet control to integration with existing production, warehouse, or yard management systems.
High-performance 4G and 5G networks, combined with low-latency edge and cloud infrastructure, provide the foundation for safe, scalable automated driving across multiple sites. Standardized reference architectures help ensure these functions can be deployed and operated consistently in different locations and countries.
What matters most is not the individual vehicle, but the interaction between vehicle, software, infrastructure, and connectivity.
Automation where it already works today
Automated vehicles and transport systems are already in productive use and are delivering efficiencies today in many processes. In controlled industrial environments such as plants, warehouses, yards, and farms, they support the driverless movement of goods, components, and vehicles. These solutions often rely on specialised vehicles, including automated yard tractors and agricultural or mining machines.
Increasingly, however, standard production cars, lorries, and buses can also be automated. Many premium vehicles already offer assisted driving functions such as parking assistance, lane keeping, and automatic braking, and can also be controlled remotely. Automotive manufacturers are using these capabilities to move vehicles through factories and logistic hubs without drivers.
Modern AI algorithms, together with sensors such as LiDAR and cameras, enable reliable perception, safe navigation, and dynamic obstacle detection. In some applications, RTK-GPS is combined with on-board sensor data to guide and control vehicles with high precision.
Typical use cases
The range of applications is broad and extends across industries:
– automated pallet and material transport in warehouses or outside
– just-in-time supply of production lines
– autonomous manoeuvring of vehicles, trailers, or swap bodies in plant and logistics yards
What all these scenarios have in common is that they reduce manual tasks, increase process stability, and create new transparency regarding workflows, timing, and capacity utilization.
Across all these environments, Industrial Automated Driving represents far more than the automation of isolated transport processes. It forms a foundational element of end-to-end digitalization strategies within manufacturing and logistics ecosystems. Starting at OEM production facilities, extending through logistics hubs such as ports and distribution centers, and continuing to downstream operations including depots and dealerships.
Therefore, Industrial Automated Driving enables connected, data-driven, and highly efficient material flows across the entire value chain. By integrating automation, real-time operational transparency, and intelligent orchestration of assets and processes, companies can significantly enhance productivity, resilience, and scalability in increasingly complex industrial environments.
Why now is the right time
The current maturity of technology, network infrastructure, and AI-based perception enables solutions that are realistically implementable today – not as experimental pilots, but as productive components of industrial value creation. Logistics and manufacturing firms are reporting challenges with shortages of skilled workers, especially for nightshifts. At the same time, these companies are under growing pressure to make processes more resilient, efficient, and sustainable.
Industrial Automated Driving is therefore less an innovation project and more a strategic lever for making industrial mobility fit for the future.
The next step
Which use cases make sense? What technical and organizational prerequisites must be in place? And how can the economic benefits be assessed realistically? Are there any additional benefits for my overall digitalization strategy by adding a cellular infrastructure and Lidar sensors in my sites?
The answers depend on each site’s specific requirements – and that is where the real discussion begins. Detecon is well positioned to support this dialogue. We combine expertise in communications networks, AI, and cloud computing to deliver end-to-end consultation and implementation planning. In addition to providing neutral consultancy services, we work closely with the organizations of Deutsche Telekom and T-Systems to deliver tangible industrial automated driving end-to-end solutions.
Get to know us.
Dimitri Jungblut
Manager
