Richard Matthaei

Map-relative localization in lane-level maps for ADAS and autonomous driving

Future advanced driver assistant systems put high demands on the environmental perception especially in urban environments. Todays on-board sensors and on-board algorithms still do not reach a satisfying level of development from the point of view of robustness and availability. Thus, map data is often used as an additional data input to support the on-board sensor system and algorithms. The usage of map data requires a highly correct pose within the map even in cases of positioning errors by global navigation satellite systems or geometrical errors in the map data. In this paper we propose and compare two approaches for map-relative localization exclusively using a lane-level map. These approaches deliberately avoid the usage of detailed a priori maps containing point-landmarks, grids or road-markings. Additionally, we propose a grid-based on-board fusion of road-marking information and stationary obstacles addressing the problem of missing or incomplete road-markings in urban scenarios.

Autonomous driving – a top-down-approach

Abstract This paper presents a functional system architecture for an “autonomous vehicle” in the sense of a modular building block system. It is developed in a top-down approach based on the definition of the functional requirements for an autonomous vehicle and explicitly combines perception-based and localization-based approaches. Both the definition and the functional system architecture consider the aspects operating by the human being, mission accomplishment, map data, localization, environmental and self-perception as well as cooperation. The functional system architecture is developed in the context of the research project “Stadtpilot” at the Technische Universität Braunschweig.

Consistency-based motion classification for laser sensors dealing with cross traffic in urban environments

In this paper an approach for a consistency-based motion classification for laser sensors is presented which concentrates on urban environments. In these complex environments the algorithm has to match both cross traffic and structures in parallel to the road, as well as objects starting and stopping moving. This leads to a conflict to be solved. For a better understanding we introduce some basic definitions at the beginning. As there are limits due to the sensors properties, the proposed algorithm can be configured. The parameters depend on the special dynamic characteristics of the scenario to be detected on the one hand and on the other hand on the sensors properties. In combination with the resulting speed limit of the ego vehicle, these parameters describe the theoretical limits of this approach in a comprehensible way. This approach runs online and has been validated in crowded urban environment.

Technical evaluation of the Carolo-Cup 2014 - A competition for self-driving miniature cars

The Carolo-Cup competition conducted for the eighth time this year, is an international student competition focusing on autonomous driving scenarios implemented on 1:10 scale car models. Three practical sub-competitions have to be realized in this context and represent a complex, interdisciplinary challenge. Hence, students have to cope with all core topics like mechanical development, electronic design, and programming as addressed usually by robotic applications. In this paper we introduce the competition challenges in detail and evaluate the results of all 13 participating teams from the 2014 competition. For this purpose, we analyze technical as well as non-technical configurations of each student group and derive best practices, lessons learned, and criteria as a precondition for a successful participation. Due to the comprehensive orientation of the Carolo-Cup, this knowledge can be applied on comparable projects and related competitions as well.

Functional System Architecture for an Autonomous on-Road Motor Vehicle

Autonomous driving is a widely discussed field of research with still growing interest. In addition to a lot of technical, legal and social questions to be solved, an immense challenge still remains in mastering the complexity of the resulting system which would eventually replace the driver. A supporting tool for developing complex systems is given by the functional system architecture, which describes the system on an abstract level independent of concrete technical solutions. Functional system architectures published in the context of autonomous driving do not cover all necessary functional requirements. However, they focus on different sub-aspects and functional mechanisms within this context.