One of the main restrictions of present-day driver assistance systems is the limited temporal and spatial horizon. In order to overcome this limitation, the European integrated project SAFESPOT aims to develop a safety margin assistant, which provides the driver with appropriate recommendations for how to avoid critical situations. For reaching that goal, positioning algorithms with sub-meter accuracy are necessary. In this paper, the SAFESPOT approach for accurate relative positioning of vehicles is presented. The main idea is to combine several sources of information from a cooperative vehicle ad-hoc network using a data fusion module. Thus, the single positioning technologies (e. g. satellite navigation, communication signals, and landmarks) as well as the data fusion algorithms are explained. The results are expected to improve localization accuracy in a way which makes it possible for vehicular safety applications to determine even the lane in which a vehicle is travelling.

The fusion of data from different sensorial sources is nowadays an often-used method to increase robustness and reliability of automatic environmental perception. The project ProFusion2, which is a horizontal subproject in the IP PReVENT (funded by the EC) was created to enhance fusion techniques and algorithms beyond the current state-of-the-art. The enhancement of the algorithms is strongly connected with the creation of a methodology to describe vehicle environments in an adequate manner to meet the requirements of robustness and reliability. In this paper, the definition of such a general environmental description is proposed and an according general data structure is introduced. This data structure is able to handle all kind of information occurring in a data fusion process. Additionally, an output structure of the perception system is proposed to work as an interface to the applications. The ProFusion2 community suggested a model for sensor data fusion in compliance with the Joint Directors of Laboratories (JDL) model which is a widely known model for information fusion systems (Hall and Llinas, 2001)

The paper presents a methodology for using fuzzy operators for the hierarchical fusion of processing results in a multi sensor data processing system. Tracking and fusion of intermediate results is performed in several levels of processing (signal level, several feature levels, object level). To produce higher level hypotheses on the basis of lower level components, grouping rules using certain assignment decisions are used. In this paper this is seen as a classification procedure that is step by step testing and assigning components to a higher level feature or object. For these classifications a suitable combination of a fuzzy operator for fusion and membership functions for classification is proposed to meet the requirements of the hierarchical classification and the necessity to include confidence values for that. Especially the dependencies between the n-fold one-dimensional classification and the n-dimensional classification is addressed. We use a straightforward example to demonstrate the concept of the multi level fusion and classification procedure

In Europe, a considerable part of lives lost in traffic accidents is due to inappropriate vehicle speed or headway. Excessive speed is one of the major causes of accidents on European roads, responsible for one-third of all road accidents. SASPENCE, as part of the EU founded integrated project PReVENT, is developing and evaluating an innovative system able to perform the reliable and comfortable safe speed and safe distance concept, which helps the driver to avoid dangerous situations related to excessive speed or too small headway. In this paper, the high precise reconstruction of the road geometry is discussed. This information about the road geometry is needed in the project to be able to calculate the risk of a given scenario and given speed. A sensor fusion method is proposed to combine information from a vision sensor, a radar system, digital maps, a GPS sensor and odometric sensors to one common description of the road geometry in front of the vehicle. Thereby, the sensor information is used to localize the own vehicle relative to the map data. The system has been tested using real data and the results are shown in the paper

Dieser Artikel befaßt sich mit einem Teilgebiet der Szenenerkennung für Fahrerassistenzsysteme. Multi-Sensor-Daten-Fusion wird für die Detektion und zeitliche Verfolgung von Personen im Fahrzeugumfeld eingesetzt. Ein System, bestehend aus einem Laserscanner und einer Infrarot-Kamera wird verwendet, um Personen sicher zu detektieren und genau zu lokalisieren. Diese beiden Sensoren sind in gewissem Maße komplementär hinsichtlich der Information, die sie über eine beobachtete Szene liefern. Der Laserscanner liefert eine exakte Position, hat dabei jedoch eine schlechte Leistung hinsichtlich der Unterscheidung zwischen Personen und anderen Objekten. Im Gegensatz dazu erlaubt die Kamera nur eine schlechte Lokalisierung, ist aber für die Extraktion wichtiger Merkmale geeignet, insbesondere, wenn es sich – wie bei Personen – um räumlich ausgedehnte Objekte handelt. Um die Verknüpfung der Sensor-Informationen zu erreichen, wird ein Extended Kalman-Filter-Ansatz verwendet, in welchem die spezifischen räumlichen Transformationen als nichtlineare Meßfunktionen eingehen. Um die räumliche Ausdehnung der Personen zu berücksichtigen, wird ein spezielles Merkmal-Modell für die Personenbeschreibung verwendet. Das wird kombiniert mit einem auf diese Anforderungen angepaßten Fuzzy-Gating-Verfahren. Das Personenerkennungssystem ist in Betrieb auf dem elektrogetriebenen Testfahrzeug der Professur für Nachrichtentechnik der TUC.

This paper presents a multi sensor approach for tracking the road borders and lanes in highway scenarios. It is based on extended Kalman filtering and estimates the parameters of circle segments. In addition to a greyscale camera, our approach uses digital map information in combination with GPS, yaw and velocity measurements. The solution combines the localization task with the task of fusing white lines in the image with data from maps. Therefore, the state space contains movement parameters as well as the parameters of more than one circle segment. Simulations show that the algorithm is also very successful in situations when the vehicle makes rapid lane changes or the white lines in front of the car are temporarily not visible, because they are hidden by another object. Also in situations when the highway goes over the top of a hill, a precise estimation of the road course in front of the car is not possible. Simulated and real tests show the improvement reached by the approach. Results are compared with others which are known from the literature.

This article presents a multi sensor approach for driver assistance systems: the detection and tracking of pedestrians in a road environment. A multi sensor system consisting of a far infrared camera and a laser scanning device is used for the detection and precise localization of pedestrians. Kalman filter based data fusion handles the combination of the sensor information of the infrared camera and of the laser scanner. Arranging a set of Kalman filters in parallel, a multi sensor/multi target tracking system was created. The usage of suitable movement models has a great influence on the performance of the tracking system. Several types of models are discussed focussing on the typical behavior of pedestrians in road environments. The multi sensor/multi target tracking system is installed on a test vehicle to obtain practical results which is discussed in this article too.

The paper describes the usage of the generalized feature model in a multi sensor tracking system. The generalized feature model is based on the assumption that an object can be modelled by an individual set of features. This approach takes into account that objects have an actual size and can be measured by several different sensors. In this article different types of 3D road objects are considered and feature models for these types of object are introduced. The focus is mainly on the detection and tracking of pedestrians and vehicles in road environments. The particular problem of feature uncertainties in the generalized feature model is discussed using several sensor types. Also, different suitable dynamic models are applied to the feature model. The results are presented on the basis of simulations and real measurements.

This paper treats an important problem concerning driver assistance systems: the detection and tracking of road borders. The detection of the road boarders are created from the signals of a laser scanner system. Two different information can be taken from the laser signal: range and reflectivity. The range signal delivers the road edges at the basis of single scans. In opposite to that, to detect edges in the reflectivity, the reflectivity signals are arranged as images and a special image processing algorithm is developed. The fusion and tracking of this information is performed using an Extended Kalman filter where a circular curve is used as movement model.

In automotive applications, especially for driver support systems or autonomous transportation sys- tems, the precise knowledge of the road border in front of the vehicle is fundamental. The basic idea presented in this paper is to detect and track the road border in the difficult case of non cooperative areas (no white lines, etc.) with a laser radar system. A two step approach has been developed: algorithms for the detection of the road border in a single scan based on statistical approaches, and tracking algorithms to estimate the road border from the generated detections. The presented results show that the solution works in several different road scenarios. For the development and verification of the road border signal processing, a specially equipped electro mobile is used.