We propose a lane keeping assistance system which warns the driver on unintended lane departures. Based on an existing robust video-based lane detection algoritlim we compare different methods to detect lane departure. A number of assumptions on driver behaviour in certain situations have been integrated to distinguish between intended and unintended lane departures. We integrated our lane keeping assistant in an experimental car and performed systematic experiments in real trafJic situations.
We propose a system to estimate the occupancy of a car-park using a single image of a single camera. Very often car-parks are already equipped with CCTV-cameras for surveillance purposes which may be used for automatic detection systems as well. Our system is targeted on cases where occupancy values are sought, but exact solutions like automatic gates or induction loops are too costly and where estimate values are acceptable for the operator. The image processing for the vehicle classification basically works by constructing a reference image of the empty car-park given in the input image and then comparing those two. The occupancy estimate is determined by the vehicle to car-park pixel area ratio, where perspective distortion and occlusion is compensated
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.
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