Auswirkungen des automatisierten und vernetzten Fahrens auf die Fahrzeugsicherheit

Abstract

One of the key challenges for transforming the automotive sector towards connected and automated vehicles (CAV) in road transport and gaining widespread acceptance is ensuring that vehicles reliably perceive their environment. A variety of sensors are currently used for this purpose, including cameras, Radar-, LiDAR- and ultrasonic sensors. Each sensor type has its own strengths and weaknesses, and there is often a need to combine them to ensure the most accurate and reliable perception of complex traffic situations. For the necessary environment perception, the approach of an on-board sensor system is currently primarily used. In contrast, the project "Managed Automated Driving Urban" (MAD Urban), which is funded by the BMWK, addresses the concept of infrastructure-based cooperative environment perception (Collective Perception) by means of Road Capturing Units (RCU). The positioning of these RCUs can be adapted to the complex environmental situations on site in such a way that the relevant detection areas are covered in the best possible way at all times. Traffic objects and traffic flow are detected early and critical traffic situations are detected on the infrastructure side, in some cases before they can be recognized by the driver or a vehicle sensor system. All data is analyzed and sent to the road users via communication units. The sensors installed on the infrastructure side thus increase traffic safety, especially for Vulnerable Road Users (VRU), enable maneuver planning for CAV and optimize the overall traffic flow. The paper reflects the state of the art regarding MAD and provides an overview of the activities and first results of the MAD Urban project. The focus is on the presentation of the metrological infrastructure, its integration into the information technology architecture, and the design in the context of the specific use cases of CAV at complex urban intersections.