RoadGraph - Graph based environmental modelling and function independent situation analysis for driver assistance systems

Knaup, Jorn; Homeier, Kai

doi:10.1109/itsc.2010.5625016

Cited by 29 publications

(8 citation statements)

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“…In this step for example other traffic participants are associated to a certain lane (see e.g. Knaup and Homeier [26]). Hence, on this level of abstraction, the semantic information is the most relevant one besides the geometric and topologic information.…”

Section: Processed Datamentioning

confidence: 99%

Autonomous driving – a top-down-approach

Matthaei

Maurer

2015

At - Automatisierungstechnik

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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 topdown 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. Zusammenfassung:In diesem Artikel stellen wir eine funktionale Systemarchitektur für ein "autonom fahrendes Straßenfahrzeug" vor, die im Sinne eines modularen Baukastensystems entworfen ist. Sie wurde in einem Top-Down-Ansatz ausgehend von einer Definition des Funktionsumfangs eines "autonom fahrenden Straßen-fahrzeugs" entwickelt und führt explizit wahrnehmungsbasierte und lokalisierungsbasierte Ansätze zusammen. Sowohl die Definition des Funktionsumfanges als auch die funktionale Systemarchitektur berücksichtigen die Aspekte Bedienung, Missionsumsetzung, Karten, Lokalisierung, Umfeld-und Selbstwahrnehmung sowie Kooperation. Die Ergebnisse basieren unter anderem auf Erkenntnissen aus dem Projekt "Stadtpilot" der Technischen Universität Braunschweig.

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Section: Processed Datamentioning

confidence: 99%

Autonomous driving – a top-down-approach

Matthaei

Maurer

2015

At - Automatisierungstechnik

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“…A pure geometric representation for example as presented in [3] models road markings using arc splines. In RoadGraphs [4] streets are represented by a graph structure. Lane segments are represented by straight edges that are connected by nodes.…”

Section: Related Workmentioning

confidence: 99%

Semantic state space for high-level maneuver planning in structured traffic scenes

Kohlhaas

Bittner

Schamm

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Originating from simple cruise control systems that monitor and control the speed of the vehicle, driver assistance systems have evolved into intelligent systems. Future assistance systems will combine information from different sensors and data sources to build up a model of the current traffic scene. This way they will be able to assist in challenging tasks in complex situations. Towards this goal, we present a semantic scene representation for modeling traffic scenes. Based on a geometric representation a semantic representation is defined using an ontology to model relevant traffic elements and relations. Considering potential relations of the ego vehicle, a semantic state space of the ego vehicle is derived. Transitions are defined that model state changes (maneuvers). The model can be used for example for situation analysis and high level planning for driving hint generation or automated driving. The method is evaluated in different traffic situations and on real sensor data. It is going to be applied to (semi-)automated driving in a real test vehicle.

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“…One possibility of scalable environment representations is given by tree based methods, for example [6], which are on the downside faced with rather high computational effort. Besides that, there are several feature and graph based approaches, for example [7], which have shortcomings due to restricting model assumptions about the environment. Another category of publications deals with the further processing of the information represented in occupancy grids, e.g.…”

Section: B Related Workmentioning

confidence: 99%

An interval based representation of occupancy information for driver assistance systems

Weiherer

Bouzouraa

Hofmann

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-Future Advanced Driver Assistance Systems (ADAS) require detailed information about occupancy states in the vehicle's local environment. In contrast to widespread occupancy grids, this information should be represented in a compact, scalable and easy-to-interpret data structure. In this paper, we show how occupancy probabilities can efficiently be represented in our 2D Interval Map framework. The basic idea of this approach is to discretize the vehicle's environment only in longitudinal direction and to avoid quantization errors in lateral direction by storing continuous values. In order to correctly deal with dynamic obstacles in ADAS scenarios, the map also interacts with a model based object tracking.The comparison of our experimental results to a ground truth illustrates the differences of grid and interval based environment representations. A tested collision avoidance function yields similar results for both representations, while computation times and memory requirements are substantially improved by the application of the 2D Interval Map.

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RoadGraph - Graph based environmental modelling and function independent situation analysis for driver assistance systems

Cited by 29 publications

References 0 publications

Autonomous driving – a top-down-approach

Autonomous driving – a top-down-approach

Semantic state space for high-level maneuver planning in structured traffic scenes

An interval based representation of occupancy information for driver assistance systems

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