Automation of freight transport

Trucks in logistics centers are currently moved by drivers or other personnel in a labor-intensive manner. There are primarily two main driving tasks to be performed:

  1. shuttle trips: vehicles shuttle between two closely located sites, e. g., to transport goods between production facilities and/or warehouses. The locations can be connected by private or public roads.
  2. shunting: vehicles shunt in the logistics center to deliver or pick up goods. In most cases, drivers must dock quickly and accurately at loading docks.

If these driving tasks are fully automated, there are clear advantages in terms of transporting goods and merchandise efficiently, initially within the company. Driving personnel can be deployed more economically and break and driving times can be scheduled more flexibly. In addition, transport processes can be  accelerated significantly and made safer.

Drivers are currently deployed for shunting and shuttle services at many production sites and logistics locations. In the 34 locations of the German freight centers (Deutsche GVZ-Gesellschaft (DGG) and GVZ-E Dresden - associated partners) alone, with over 1,400 resident companies and 52,000 employees, approximately 60,000 trips are made by truck every day. In principle, the routes in the logistics centers can be fully automated, since there are no decision-making processes in the vehicle itself that necessarily require a driver. This becomes abundantly clear in large port facilities, where goods are already moved over long distances by fully automated transport systems. Nevertheless, there, as well, automation currently ends after the truck that is approved for road transport and will distribute the goods via the public raod network has been loaded.

Automated trucks for logistics hubs#

© Fraunhofer IVI
AutoTruck: e-truck with steer-by-wire and drive-by-wire systems
© Fraunhofer IVI
helyOS: Control station for automation zones
© Fraunhofer IVI
TruckTrix: Online maneuver planning

The topic of "autonomous driving" has become increasingly important in the recent years. However, before the first fully automated series-produced vehicles can drive on public roads, many technical challenges still need to be solved. In this context, the automation of commercial vehicles in closed areas is an ideal migration path. These so-called automation zones combine areas in which the infrastructure has been developed specifically for autonomous driving. This includes communication technologies, digital maps, monitoring systems and a control center where all data collected in the automation zone converges. Based on this information and the HMI of the control station, an operator will be able to efficiently dispatch and monitor more than ten autonomous vehicles for the execution of work tasks in the future. Drivers are no longer required in these automation zones. Depending on the application, they will take over the vehicles only when the zone needs to be left.

The wide range of applications includes transport tasks in logistics centers, ports, airports, but also uses in agriculture or construction. For example, trucks, swap bodies and trailers in fleets can be moved fully automatically between parking positions and loading ramps, or fields can be worked cooperatively by several machines.

The Commercial Transport WG is investigating technologies for autonomous driving in depots ranging from maneuver planning with specially developed algorithms to testing tracking and sensor systems and conducting driving tests with an autonomous e-truck.

Example projects:

AutoTruck (Fraunhofer IVI): e-truck with steer-by-wire and drive-by-wire systems

helyOS (Fraunhofer IVI): Control center for automation zones

TruckTrix (Fraunhofer IVI): online maneuver planning

AI in transport and mobility: driver assistance systems in rail transport#

Options for reducing energy consumption

© den-belitsky - iStock.com

Traction energy consumption is the most important cost factor in rail transport companis‘ electricity bills. It is largely determined by the way the trains are driven. A significant reduction in energy consumption can be achieved through energy-efficient speed profiles. This includes making as much use as possible of coasting phases, as trains do not consume any energy during them. In addition, the energy-efficient coordination of train traffic is also of crucial importance. This includes avoiding too many simultaneous departures because they cause high load peaks in the power grid, which are billed additionally. Furthermore, the synchronization of arriving and departing trains is relevant in order to be able to use the energy fed back during the braking of one train for the acceleration of another.

Building on this, the aim is to control train departures at stations and journeys on the track in real time through implementing driver assistance systems,  the long-term goal being automatic control of train movements. For this, AI methods are needed that are capable of responding to the usual delays in the operational process so that the optimality of the calculated speed profiles is maintained even under disruptions. 

Example Projects:

Optimizing the energy consumption of the Nuremberg subway
(Project page, Fraunhofer IIS): Reducing the energy consumption of Nuremberg's subway system.

ADA Lovelace Centerapplikation »advanced driver assistance systems in rail transport « (Project page, Fraunhofer IIS): Real-time control of train departures at stations and of journeys on the track.

Integrated Optimization of International Transportation Networks (Project page ROMSOC, FAU): Development of an optimal resource allocation system in rail freight traffic

Testing and validation of ADAS/AD systems#

© Fraunhofer ITWM
© Fraunhofer ITWM
Simulation and virtual scenarios

Advanced driver assistance systems and autonomous driving (ADAS/AD) already play an important role in the development and operation of modern vehicles and are an essential component of economically efficient and sustainable mobility solutions. This is particularly true in the area of commercial vehicles, where suitable assistance systems can substantially increase safety and also make the transport of goods more efficient and resource-conserving at the same time. An important challenge in the development, application and operation of these systems is efficient and realistic testing and validation. Effective testing and hedging concepts must both map a statistically representative customer usage, but at the same time consider all relevant situations up to the point of accidents. This is a correspondingly complex task and requires modern, computer-aided approaches. The development of methods for virtual testing and stochastic variant scenario generation, combined with mathematical methods of artificial intelligence and model-based simulation, therefore represents an important field of research and work at Fraunhofer ITWM.

The Commercial Transport WG has competencies in the application-specific testing and validation of ADAS/AD systems, as well as in the design and optimization of those systems on a hardware and software basis.