Iris Leefken

Image processing and behavior planning for intelligent vehicles

Since the potential of soft computing for driver assistance systems has been recognized, much effort has been spent in the development of appropriate techniques for robust lane detection, object classification, tracking, and representation of task relevant objects. For such systems in order to be able to perform their tasks the environment must be sensed by one or more sensors. Usually a complex processing, fusion, and interpretation of the sensor data is required and imposes a modular architecture for the overall system. In this paper, we present specific approaches considering the main components of such systems. We concentrate on image processing as the main source of relevant object information, representation and fusion of data that might arise from different sensors, and behavior planning and generation as a basis for autonomous driving. Within our system components most paradigms of soft computing are employed; in this article we focus on Kalman filtering for sensor fusion, neural field dynamics for behavior generation, and evolutionary algorithms for optimization of parts of the system.

A flexible architecture for intelligent cruise control

We present a concept of a flexible and modular architecture for intelligent cruise control (ICC). The architecture can be subdivided into three different processing steps: object-related analysis of sensor data, behavior-based scene interpretation and behavior planning. Each step works on collected sensor information as well as on a knowledge base, which can be broadened by external knowledge like GPS and street maps. An intelligent car following system is described in the paper as a spin-off for behavior planning.

Application and optimization of neural field dynamics for driver assistance

Behavior planning of a vehicle in real-world traffic is a difficult problem. Complex systems have to be build to accomplish the projection of tasks, environmental constraints, and purposes of the driver to the dynamics of two controlled variables: steering angle and velocity. This paper comprises two parts. First, the behavior planning for the task of intelligent cruise control is proposed. The controlled variables are determined by evaluating the dynamics of two one-dimensional neural fields. The information concerning the actual situation and driver preferences is coupled additively into the field. Second, the parameters of the dynamics for the steering angle are adjusted by a state-of-the-art evolution strategy in order to achieve a smooth, comfortable trajectory. The behavior of the vehicle is successfully controlled by the neural field dynamics in the testbed of a simulation environment.

SCENE INTERPRETATION AND BEHAVIOR PLANNING FOR DRIVER ASSISTANCE

The scene interpretation and the behavior planning of a vehicle in real world traffic is a difficult problem to be solved. If different hierarchies of tasks and purposes are built to structure the behavior of a driver, complex systems can be designed. But finally behavior planning in vehicles can only influence the controlled variables: steering, angle and velocity. In this paper a scene interpretation and a behavior planning for a driver assistance system aiming on cruise control is proposed. In this system the controlled variables are determined by an evaluation of the dynamics of a two-dimensional neural field for scene interpretation and two one-dimensional neural fields controlling steering angle and velocity. The stimuli of the fields are determined according to the sensor information.

Flexible architecture for driver assistance

To reduce the number of traffic accidents and to increase the drivers comfort, the thought of designing driver assistance systems rose in the past years. Principal problems are caused by having a moving observer (ego motion) in predominantly natural surroundings. In this paper we present a solution for a flexible architecture for a driver assistance system. The architecture can be subdivided into four different parts: the object-related analysis, the knowledge base, the behavior-based scene interpretation, and the behavior planning unit. The object-related analysis is fed with data by the sensors (e.g., vision, radar). The sensor data are preprocessed (flexible sensor fusion) and evaluated (saliency map) searching for object-related information (positions, types of objects, etc.). The knowledge base is represented by static and dynamic knowledge. It consists of a set of rules (e.g. , traffic rules, physical laws), additional information (i.e., GPS, lane-information) and it is implicitly used by algorithms in the system. The scene interpretation combines the information extracted by the object related analysis and inspects the information for contradictions. It is strongly connected to the behavior planning using only information needed for the actual task. In the scene interpretation consistent representations (i.e., birds eye view) are organized and interpreted as well as a scene analysis is performed. The results of the scene interpretation are used for decision making in behavior planning, which is controlled by the actual task. The influence of behavior planning on the behavior of the guided vehicle is limited to advices as no mechanical control (e.g. , control of the steering angle) was implemented. An Intelligent Cruise Control (ICC) is shown as a spin-off for using this architecture.

Computational counting for a quantitative analysis of cells in histologically prepared brain sections

Abstract We propose a reliable method for automatic counting of cells in brain sections for different antibodies (NeuN, Parvalbumin, GABA and c-Fos) and Nissl-staining. The images of stained sections are binarized by thresholding. Then regions are clustered using a common clustering algorithm. After choosing only proper sized clusters the detected cell-bodies are counted. The parameters of the algorithm are adjusted manually and remain constant for different probes. The computational cell counting method provides correct counting results, shown by a comparison of computational results and counts gained by human experimenters.

Scene representation for driver assistance by means of neural fields

In this paper a method for scene representation in driver assistance applications is proposed. To gain noise reduction and unique environmental information detection object-hypotheses are evaluated. For each object an object-dynamic is built. The Object-dynamic consists of four one-dimensional neural fields for information evaluation of relative position, size and relative velocity. The chosen formulation enables fusion and separation of object-hypotheses gained from different sensors. Due to the dynamic character of the representation a reduction of noise and a prediction over short time periods is possible. The advantages of the representation are shown by inspecting real world sensor data.

Generating Complex Driving Behavior by Means of Neural Fields

We have developed a neural field based architecture to generate complete behavioral sequences for autonomous driving. On the basis of a user defined desired speed, the system can autonomously decide between the different behavioral alternatives lane following, lane change, acceleration and deceleration. Depending on the current traffic situation, the system can autonomously organize reactive behavioral sequences such as overtaking. Basically, the system consists of coupled one-dimensional neural fields: two fields for a desired longitudinal position and one field for the lateral position. All field dynamics are driven in a parameter regime which guarantees the existence of a monomodal self stabilizing peak. Both, the longitudinal and the lateral control consist of two dynamics: one dynamics represents a planning component (“motivation”) and one determining the action component. The action level generates the desired position of movement which determines the corresponding controlled variables: steering angle and velocity.

Eine flexible Architektur für Fahrerassistenzsysteme

In diesem Artikel wird eine flexible Architektur vorgestellt, mit deren Hilfe eine modulare Losung von Fahrerassistenzaufgaben in Kraftfahrzeugen gezeigt werden kann. Es wird eine Objektbezogene Analyse von Sensordaten, eine Verhaltensbasierte Szeneninterpretation und eine Verhaltensplanung vorgestellt. Eine globale Wissensbasis, auf der jedes einzelne Modul arbeitet, beinhaltet die Beschreibung physikalischer Zusammenhange, Verhaltensregeln fur den Strasenverkehr, sowie Objekt- und Szenenwissen. Externes Wissen (z.B. GPS - Global Positioning System) kann ebenfalls in die Wissensbasis eingebunden werden. Als Anwendungsbeispiel der Verhaltensplanung wird ein intelligenter Tempomat vorgestellt.

A Flexible Architecture for Driver Assistance Systems

The problems encountered in building a driver assistance system are numerous. The collection of information about real environment by sensors is erroneous and incomplete. When the sensors are mounted on a moving observer it is difficult to find out whether a detected motion was caused by ego-motion or by an independent object moving. The collected data can be analyzed by several algorithms with different features designed for different tasks. To gain the demanded information their results have to be integrated and interpreted. In order to achieve an increase in reliability of information a stabilization over time and knowledge about important features have to be applied. Different solutions for driver assistance systems have been published. An approach proposed by Rossi et al. [8] showed an application for a security system. An application being tested on highways has been presented by Bertozzi and Broggi [1]. Dickmanns et al. presented a driving assistance system based on a 4D-approach [2]. Those systems were mainly designed for highway scenarios, while the architecture presented by Franke and Gorzig [3] has been tested in urban environment.