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After analysing the major causes of injuries and death on roads, it is understandable that one of the main goals in the automotive industry is to increase vehicle safety. The European project SPARC (Secure Propulsion using Advanced Redundant Control) is developing the next generation of trucks that will fulfil these aims. The main technologies that will be used in the SPARC project to achieve the desiderated level of safety will be presented. In order to avoid accidents in critical situations, it is necessary to have a representation of the environment of the vehicle. Thus, several solutions using different sensors will be described and analysed. Particularly, a division of this project aims to integrate cameras in automotive vehicles to increase security and prevent driver's mistakes. Indeed, with this vision platform it would be possible to extract the position of the lane with respect to the vehicle, and thus, help the driver to follow the optimal trajectory. A definition of lane is proposed, and a lane detection algorithm is presented. In order to improve the detection, several criteria are explained and detailed. Regrettably, such an embedded camera is subject to the vibration of the truck, and the resulting sequence of images is difficult to analyse. Thus, we present different solutions to stabilize the images and particularly a new approach developed by the "Laboratoire de Production Microtechnique". Indeed, it was demonstrated in previous works that the presence of noise can be used, through a phenomenon called Stochastic Resonance. Thus, instead of decreasing the influence of noise in industrial applications, which has non negligible costs, it is perhaps interesting to use this phenomenon to reveal some useful information, such as for example the contour of the objects and lanes.

Abstract-One of the main technical goals in the actual automotive industry is to increase vehicle safety. The European project SPARC (Secure Propulsion using Advanced Redundant Control) is developing the next generation of trucks towards this aim. The SPARC consortium intends to do so by providing the truck with active security systems. Specifically, by equipping the vehicle with different sensors, it can be made aware of its environment, such as other vehicles, pedestrians, etc. By combining all sensor data and processing it with internal proprioceptive information 1 , the truck can advice, warn or even override the driver in case of non-response.Camera systems are particularly advantageous for sensing purposes, because they are passive sensors and provide very rich information. Moreover, they can easily be software-reconfigured to extract new or additional data from the input-image. Typical information that SPARC aims to extract is the position of the vehicle within the lane, the presence and distance of other vehicles or obstacles, and the identification of roadsigns. In this paper, a lane-detection algorithm will be presented and discussed.Some of the resulting information needs to be given in world coordinates, as opposed to image coordinates. To carry out the necessary conversion, a previous calibration is needed. The challenge is to determine a procedure to calibrate a camera mounted on a truck to precisely determine the position of obstacles situated in a 100 meter range. The two-step calibration procedure presented here has been designed to simplify the calibration of the mounted cameras in the truck production line.

In video surveillance and sports analysis applications, object trajectories offer the possibility of extracting rich information on the underlying behavior of the moving targets. To this end we introduce an extension of Point Distribution Models (PDM) to analyze the object motion in their spatial, temporal and spatiotemporal dimensions. These trajectory models represent object paths as an average trajectory and a set of deformation modes, in the spatial, temporal and spatiotemporal domains. Thus any given motion can be expressed in terms of its modes, which in turn can be ascribed to a particular behavior.The proposed analysis tool has been tested on motion data extracted from a vision system that was tracking radio-guided cars running inside a circuit. This affords an easier interpretation of results, because the shortest lap provides a reference behavior. Besides showing an actual analysis we discuss how to normalize trajectories to have a meaningful analysis.

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