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Going beyond standard lane-departure-avoidance systems, this paper addresses the development of a system that is able to deal with a large set of different traffic situations. Its foundation lies on a thoroughly constituted environment detection through which a decision system is built. From the output of the decision module, the driver is warned or corrected through suited actuators that are coupled to control strategies. The input to the system comes from cameras, which are supplemented by active sensors (such as radar and laser scanners) and vehicle dynamic data, digital road maps, and precise vehicle-positioning data. In this paper, the presented system design is divided into three layers: the perception layer, which is responsible for the environment perception, and the decision and action layers, which are responsible for evaluating and executing actions, respectively.

In this paper the authors present a design approach of a nonlinear controller for steered semi-trailers of heavy commercial vehicles, which is used to improve their maneuverability. The proposed control structure uses the exact input-output linearization. Additionally, a sliding mode control approach (SMC) is used to overcome model uncertainties as well as control errors and increase robustness. The practicability of the control structure is shown with simulation results of a nonlinear three-dimensional multi-body model

This paper presents a novel two DOF control for train-like guidance of general multiple articulated vehicles with all wheel steering. The model based control scheme consists of a feed-forward and a feedback part designed separately. Within the nonlinear feedforward part the steering angles are calculated that theoretically cause every single axle to follow a desired path exactly. Thus in the nonlinear feedback part only model uncertainties and disturbances have to be compensated. Simulations as well as road tests show that the designed control system allows train-like vehicle guidance while it is robust against varying road conditions, payload as well as velocity.

Modern livestock farming follows a trend to higher automation and monitoring standards. Novel systems for a health monitoring of animals like dairy cows are under development. The application of infrared thermography (IRT) for medical diagnostics was suggested long ago, but the lack of suitable technical solutions still prevents an efficient use. Within the R&D project VIONA new solutions are developed to provide veterinary IRT based diagnostic procedures with precise absolute temperature values of the animal surface. Amongst others this requires a reliable object detection and segmentation of the IR images. Due to the significant shape variation of interest objects advanced segmentation methods are necessary. The "active shape" approach introduced by Cootes and Taylor [7] is applied to veterinary IR images for the first time. The special features of the thermal infrared spectrum require a comprehensive adaptation of this approach. The modified algorithm and first results of the successful application on approximately two million IR images of dairy cows are presented.