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

Abstract

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 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. Moreover, the tool chain considers methods for traffic situation detection and classification on the basis of probabilistic approaches and eventually, traffic event recording. The approach was tested at an ungated level crossing in the small town Bienrode, which is a district of 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).

A review of the technological developments for interlocking at level crossing

“…Some other work proposed the fusion of two techniques such “K-Mean clustering” algorithm on a modified “Lucas-Kanade” method, 57 “Lucas-Kanade” technique with “Haris Corner Points” in literature 58 or “Histogram of Oriented Gradients-(HoG)” algorithm with a Support Vector Machine-SVM in literature. 59…”

“…55,56 Some other work proposed the fusion of two techniques such "K-Mean clustering" algorithm on a modified "Lucas-Kanade" method, 57 "Lucas-Kanade" technique with "Haris Corner Points" in literature 58 or "Histogram of Oriented Gradients-(HoG)" algorithm with a Support Vector Machine-SVM in literature. 59 The discussed algorithms work on a principle of either subtracting the foreground pixel with the background pixel and detect the presence of an object using some threshold value or some supervised programming is required to train from the data for classification. These limitations affect the functionality of the system as the Level Crossing is a dynamic environment and most the time the required data is not sufficient for manual programming.…”