Andreas Leich

COLOMBO: Exploiting Vehicular Communications at Low Equipment Rates for Traffic Management Purposes

While most standardized vehicular communication applications aim on increasing traffic safety, the exchange of messages between vehicles and the environment may be used for other purposes at no additional hardware costs as well. One possible area of such applications is traffic management. Traffic management requires data about the state of the road network before being able to predict or control traffic. The COLOMBO project, co-funded by the European Commission, examines the possibilities to use data gained via vehicular communications for traffic management purposes.

Road user tracker based on robust regression with GNC and preconditioning

In this paper an early vision tracking algorithm particularly adapted to the tracking of road users in video image sequences is presented. The algorithm is an enhanced version of the regression based motion estimator in Lucas-Kanade style. Robust regression algorithms work in the presence of outliers, while one distinct property of the proposed algorithm is that it can handle with datasets including 90% outliers. Robust regression involves finding the global minimum of a cost function, where the cost function measures if the motion model is conform with the measured data. The minimization task can be addressed with the graduated non convexity (GNC) heuristics. GNC is a scale space analysis of the cost function in parameter space. Although the approach is elegant and reasonable, several attempts to use GNC for solving robust regression tasks known from literature failed in the past. The main improvement of the proposed method compared with prior approaches is the use of a preconditioning technique to avoid GNC from getting stuck in a local minimum.

Wide-area based traffic situation detection at an ungated level crossing

The automated detection of atypical and critical traffic situations is essentially important to help to understand driver behaviour, to find functional correlations between traffic conflicts and real accidents, and eventually, to prevent, particularly severe accidents. In this paper a tool chain is introduced that enables a fully automated traffic situation detection in wide-area traffic on the basis of a single camera. The tool chain takes into account novel powerful methods for object detection, classification and tracking on the basis of robust regression with preconditioning on the one hand as well as traffic situation detection and classification on the basis of probabilistic approaches on the other hand and eventually, traffic event recording. The approach was tested at an ungated level crossing in the small town Bienrode, which is situated near Brunswick, Germany. It is shown that atypical situations, e.g. overtaking, braking, stopping, inadequate speeds and accelerations, as well as critical situations, e.g. tailgating, can be detected within a range of up to 120 m distance of the camera automatically. The approach enables new ways of analysing traffic areas with regard to traffic safety and performance. The results shown in this paper were obtained in the project OptiSiLK, whose abbreviation means “Optimisation of the safety and the performance at intersections of different traffic modes”. OptiSiLK was funded by the Ministry for Science and Culture of the State of Lower Saxony (MWK).