Henning Mosebach

Automated valet parking as part of an integrated travel assistance

The integration of automated valet parking (automated search of a parking space and execution of the parking maneuver) in a comprehensive travel assistance approach promises great benefits for a traveler. In particular, it aims at increasing comfort, optimizing travel time and improving energy efficiency for changing means of transport (where one of them is a car) within a whole multimodal travel chain. In this paper an automated valet parking system as part of a travel assistant is presented. Besides giving an overview of the overall system, the main components (namely the environment perception and automation modules of the fully automated vehicle, a mobile phone application as human machine interface and a parking space occupancy detection camera as part of the parking area infrastructure) are described. The system was successfully tested on three different parking areas, where one of these areas was located at Braunschweig main station.

Generating high precision maps for advanced guidance support

Modern driver assistance systems increasingly support safer and more fuel-efficient driving. To fulfill these tasks information about the vehicle environment is essential. Besides extracting this information from sensors to perceive the environment, it is also possible to use stored static data. These models allow a reliable and fast access to the environmental data without the typical problems faced with the recognition by sensors, like noise or glitches. Nowadays street maps produced for navigation purposes reach a precision of several meters. To support the driver during manoeuvres however a precision one magnitude better is necessary. Positioning technology has steadily improved over the years and will further improve in the near future. Alongside this development it can be expected, that more precise digital maps will gain more importance. In this paper a method is presented to create these maps mainly automatically. The primary goal for this method is to achieve the best precision possible investing a very small effort. Data is recorded using a test vehicle equipped with a navigation system featuring high precision DGPS and inertial sensors. Lateral deviations are compensated by using a image processing lane-detection sensor. Several measurement iterations are made and merged into a digital map of the street using statistical methods. In order to show the suitability of the generated map for all kinds of ADAS, a test track was surveyed and the digital map was used for automatic guidance of another test vehicle. It could be shown that the map generation algorithm is generally able to produce high precision maps even in challenging environments, however ADAS demanding precise lateral and longitudinal position information can only rely on digital maps and GNSS in environments with little or no obstruction to the sky.