Networking on the Road
Author: Achim Geiger
In personal transportation, V2X (vehicle-to-everything) communication is seen as the technology of the future for smoother traffic and reduced CO2 emissions. At the same time, connected driving will likely raise the capabilities of automated vehicles to a higher level in terms of safety, efficiency, and autonomy.
Efficient networking will likely become a career booster for automated vehicles in the near future. Today, individual models from manufacturers such as Volkswagen, Ford, Volvo as well as Audi, BMW, and Mercedes can already network with each other via mobile communications or pWLAN in order to exchange information about problematic weather conditions, road works, or other sources of danger. However, in a manufacturer survey in May 2023, the ADAC found out that the potential for communication across brand boundaries is far from exhausted. Direct connection between different vehicles (V2V/vehicle-to-vehicle) would certainly be desirable in the interest of road safety. Until now, even cars with highly developed sensor systems have to rely on their on-board technology to constantly re-evaluate the immediate surroundings with every driven meter. With the help of real time information on traffic signals, road conditions, and traffic density, the range of vision could be significantly extended. The means to this end is V2X (vehicle-to-everything) communication, which establishes a comprehensive exchange of information between the vehicle and its surroundings.
More visibility for automated cars by networking with infrastructure
One possibility is connecting with the internet (V2N/vehicle-to-network) in order to use weather reports or navigation information. Communication with pedestrians (V2P/vehicle-to-pedestrian) could provide real time information on the presence and movement of vehicles by involving smartphones. However, the central role will be played by networking with infrastructure (V2I/vehicle-to-infrastructure). For example, when approaching an intersection, an automated vehicle could receive data on the intersection topology, including information on number of lanes and positions of stop lines from a traffic signal system or a roadside unit (RSU) equipped with radio modules. In addition, it could retrieve information on the current status of all signals at the intersection, as well as a forecast of the remaining time for green or red traffic lights.
In many places, digital ecosystems are part of the political agenda
More and more countries, cities, and municipalities are adding infrastructure management to their political agendas. However, setting up a digital ecosystem with sensors and cameras to record and analyze the outside world is no easy feat, especially since the individual systems also have to be perfectly networked with each other. Tasks such as object detection and sensor data fusion are usually handled by artificial intelligence (AI) systems. There are now around 20 active digital test fields for automated and connected driving in Germany in urban, regional, and supraregional areas, which add up to a total length of around 1,400 kilometers. In most of these test fields, the focus lies on realistic testing of vehicles for private transport and local public transport. In the fall of 2022, the German states of Lower Saxony, Baden-Württemberg, Hamburg, and North Rhine-Westphalia jointly launched a project on automated and connected mobility that will collect and evaluate the research findings from the various test fields and bring them to the road as quickly as possible.
Düsseldorf is already rehearsing emergencies with emergency vehicles in the test fields
A tangible contribution to this cooperation will come from Düsseldorf. The metropolis on the Rhine maintains a state-of-the-art, approximately 20 kilometer long digital test track that includes several highway sections, two highway interchanges, and city streets with tunnels, bridges, and intersections with and without traffic lights. Most recently, in the project “KoMoDnext – Cooperative Mobility in the Düsseldorf Test Field II”, which ended in March 2022, the Rhinelanders examined a scenario in which a police patrol car is prioritized for emergency travel on a stretch of road. As soon as the emergency vehicle activates its blue lights, it transmits data in quick succession on its position and speed to a central service (V2I platform/vehicle-to-infrastructure platform). The system evaluates the position reports and transfers them to the traffic signal’s control system located in the section of roadway, each of which is switched to green approximately 60 seconds before the police vehicle is expected to pass, while cross traffic is blocked by a red light. By the time the emergency vehicle reaches the intersection, the area has already been largely cleared. It can therefore drive through this danger zone at a comparatively high speed. During the emergency drive, automated and networked vehicles also receive a warning message about the emergency drive in an area defined by the police car’s respective position data. Based on this message, a highly automated vehicle could in future decide whether to hand the task of driving over to the driver.
With V2X-optimized mobility, one cog meshes with the other
The great hope associated with V2X is safe, efficient, and sustainable mobility in which one cog meshes smoothly with the next. In this context, the commitment to an intelligent infrastructure is already paying dividends today – initial findings from the test fields indicate that automated vehicles make significant improvements in traffic management possible, even with a low level of equipment. The Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB in Karlsruhe, Germany, has indicated the magnitude of potential efficiency improvements in the research project “KI4LSA (Artificial Intelligence for LightSignal Systems)”, which ended in August 2022. In Lemgo, North Rhine-Westphalia, they investigated the use of AI for predictive traffic light control in conjunction with an intelligent infrastructure. The result speaks for the self-learning algorithm: In real world operation, the average travel time of vehicles was reduced by around ten percent. Simulations have also shown that the system has enabled an emissions reduction between 15 and 20 percent.