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 j l t e r based Data Fusion handles the conzbination of the sensor infomation of the infrared camera and of the laser scanner. Arranging a set of Kalman jilters in parallel, a multi sensor / multi target tracking system was created. The usage of suitable movement models has a great injuence on the pelfomzance of the tracking system. Severul types of models are discussed focussing 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 will be discussed in this article too. 0-7803-831

This paper treats an important problem concerning driver assistance systems: the detection and tracking of road borders. The detection of the road boarders are createdfrom the signals of a laser Scanner system. Two different information can be taken from the laser signal: range and reflectivity. The mnge signal deliwers the road edges at the basis of single scans.In opposite to that, to detect edges in the reflectiuity, the mflectivity signals are arranged as images and a special image pmcessing algorithm is developed. The fusion and tmcking of this information is performed using an Eztended Kalman filter where a circular curue is wed as mouement model.

Abstract-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 networks 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.

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.

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