To reduce the number of traffic accidents and to increase the drivers comfort, the thought of designing driver assistance systems arose in the past years. Fully or partly autonomously guided vehicles, particularly for road traffic, pose high demands on the development of reliable algorithms. Principal problems are caused by having a moving observer in predominantly natural environments. At the Institut fur Neuroinformatik methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We present a solution for a driver assistance system. We concentrate on the aspects of video-based scene analysis and organization of behavior.

In previous years, many methods providing the ability to recognize rigid obstacles-sedans and trucks-have been developed. These methods provide the driver with relevant information. They are able to cope reliably with scenarios on motorways. Nevertheless, not much attention has been given to image processing approaches to increase the safety of pedestrians in urban environments. In the paper, a method for the detection, tracking, and final recognition of pedestrians crossing the moving observer's trajectory is suggested. A combination of data- and model-driven approaches is realized. The initial detection process is based on a fusion of texture analysis, model-based grouping of, most likely, the geometric features of pedestrians, and inverse-perspective mapping (binocular vision). Additionally, motion patterns of limb movements are analyzed to determine initial object-hypotheses. The tracking of the quasirigid part of the body is performed by different algorithms that have been successfully employed for the tracking of sedans, trucks, motorbikes, and pedestrians. The final classification is obtained by a temporal analysis of the walking process.

Sophisticated fuzzy rule systems are supposed to be flexible, complete, consistent and compact (FC(3)). Flexibility, and consistency are essential for fuzzy systems to exhibit an excellent performance and to have a clear physical meaning, while compactness is crucial when the number of the input variables increases. However, the completeness and consistency conditions are often violated if a fuzzy system is generated from data collected from real world applications. A systematic design paradigm is proposed using evolution strategies. The structure of the fuzzy rules, which determines the compactness of the fuzzy systems, is evolved along with the parameters of the fuzzy systems. Special attention has been paid to the completeness and consistency of the rule base. The completeness is guaranteed by checking the completeness of the fuzzy partitioning of input variables and the completeness of the rule structure. An index of inconsistency is suggested with the help of a fuzzy similarity which can prevent the algorithm from generating rules that seriously contradict with each other or with the heuristic knowledge. In addition, soft T-norm and BADD defuzzification are introduced and optimized to increase the flexibility of the fuzzy system. The proposed approach is applied to the design of a distance controller for cars. It is verified that a FC(3) fuzzy system works very well both, for training and test driving situations, especially when the training data are insufficient.

Artificial neural networks have evolved from their biologically inspired roots to a well established means to solve a broad spectrum of engineering problems. Their embedding into modern statistics has provided the necessary theoretical foundation for challenging engineering tasks, such as advanced real time image and signal processing. These are exemplary demonstrations for the applicability of this approach to complex information processing. However, the large number of applications must not obscure the fact that there are some major unsolved problems concerning neural networks. There are still no satisfactorily constructive ways to determine the optimal structure (elements as well as organization) or the learning and evaluation dynamics. Ongoing research addresses these problems. In addition to pursuing this direction, one can ask what other lessons we can learn from biology concerning complex information processing. Our goal is to sketch a possible pathway from neural networks to more comprehensive neural strategies.

Various feature-based object recognition methods make use of similarity measures of features to guide the recognition process. These similarity measures often are only local in nature, meaning that the measures are derived from the local attributes of the features. A similarity measure is presented that takes the form of an object based on the position of the features. A quantity that assesses the similarity of features according to their position among all others, called a context similarity measure, is derived. It is tolerant to missing features or variations in their position. The primary interest is in measuring the similarity between model features and features extracted from an image. The authors consider the use of these measures for object recognition and, as an example, describe their application in a feature-based Hough transform. They show that the combination of local and context similarities considerably improves the recognition performance.>

Proposes a framework of a neural information processing architecture. The authors attempt to extract the basic principles and operations typical for neural information processing and describe a hierarchical extendible system for the problem of exploration and orientation in a natural environment as an example for an ill-posed and highly complex problem. Realisations of the main elements of this processing structure are discussed.>

For image contours approximated by conic curve segments. The authors examine how to detect efficiently, given geometric relationships between conic segments. They present a method that can serve as a general tool for model-driven matching of conic curve segments.>

CARTRACK is a computer vision system that can reliably detect, track, and measure vehicle rears in images from a video camera in a following car. The system exploits the symmetry property typical for the rear of most vehicles on normal roads. The authors present two novel methods for detecting mirror symmetry in images, one based directly on the intensity values and another one based on a discrete representation of local orientation. CARTRACK has been used for realtime experiments with test vehicles of Volkswagen and Daimler-Benz.>

A stereo obstacle detection system has been developed for automatically guided vehicles that operate on flat (factory) floors. The system does not attempt to reconstruct the 3D environment visually but simply tries to detect obstacles on the floor in the vehicle's path. The approach to stereo image processing uses inverse perspective mappings to facilitate matching of the binocular field of vision against the expected 3D structure of the environment. Assuming a known relative camera model, a geometrical image transformation is computed which essentially compensates the stereo disparities for the image points of the floor. After the mapping operation the images are compared and local mismatches are interpreted as possible obstacle locations. The system has been successfully tested in a factory environment. The implementation runs on standard microprocessor hardware in real time.>

Network equations for the cortical area network (CAN) are presented. The nodes are formed by cortical areas with their intrinsic connectivity and the according computational capabilities. Intrinsic processing is modeled by convolutions. The edges are formed by the mappings between the various cortex areas. With respect to the spatial organization, one can distinguish topographic maps (coordinate transforms), patchy maps that occur when multiple input converges to a common target area, and parametric maps (2-D histograms that encode stimulus into a spatial position). Applications include space-variant image processing and visual receptive field organization

The authors describe a vision system, called VISOCAR, for mobile robot guidance in industrial environments. VISOCAR is an optically navigating AGV (automatically guided vehicle) that, owing to its local intelligence, can travel automatically in its natural environment, does not need any dedicated floor installations, and does not impose any restrictions on the route network or factory environment. It can navigate accurately on its track and integrate multisensor information to enhance functional redundancy and system reliability. It is shown that the navigation task can be broken down into a hierarchy of goals, which can be attained by functionally independent modules. The system architecture presented reflects this fact: there is a hierarchy of navigation capabilities, rather than a hierarchy of image processing steps. The successful implementation of this concept for autonomous navigation of AGVs in environments that are well structured but not artificially reduced in visual complexity shows that this modular system architecture is well suited to the specific demands of the application in spite of very limited computational resources.>