Marek Junghans

Vehicle environment detection by a combined low and mid level fusion of a laser scanner and stereo vision

A sensor system consisting of a multi layer laser scanner and a stereo camera is used to observe the surrounding environment of a vehicle. By a novel multi level fusion framework the heterogeneous sensor data can be merged on different processing levels. The following paper gives a short introduction to the processing steps of the sensor data and focuses on the lower raw level and middle object level fusion. Here, the low level fusion obtains a more precise object discrimination, whereas the mid level fusion is able to solve conflicting sensor statements with competing object information. A combination of both, obviously results in a more accurate environmental description. The effectiveness of both fusion approaches is tested and compared with real road reference data, obtained from highly precise GPS data.

Analysis of traffic safety for cyclists: the automatic detection of critical traffic situations for cyclists

Over the past years, the bicycle has gained importance as a means of transportation in big cities. The use and acceptance of a bicycle as being an evolving means of transportation is highly linked to its transportation safety. Still, the risk of accidents is a dominant barrier. Even though the Federal Ministry of Transport, Building and Urban Development established a National Cycling Plan to enhance cycling and improve safety aspects, serious accidents still occur. Even if the number of traffic accidents is declining in Berlin, the consequences of bicycle accidents with physical injury are characterised by increasing results. Thus, it is proved that more than half of the accidents that involve bicyclists are caused by the cyclist itself. To understand causes of accidents and to eventually arrange preventive measures and enhance cyclists’ safety, critical situations were detected. The application is based on cyclists’ trajectories generated from video sequences. As a result, atypical and dangerous traffic situations can be identified automatically whereas rule violations can be detected manually. First experiences at an intersection in Berlin show a general applicability of this approach, which has to be widely tested at other intersections.

Applying tire pressure monitoring devices for traffic management purposes

In this paper we perform the analysis of the popular TPMS (tire pressure monitoring systems) and their application for traffic management purposes. In particular, we evaluate several of the commercially available TPMS devices and analyze their architecture and communication features. Furthermore, we propose the architecture of an external sensor device used for effective eavesdropping of TPMS ID data. Finally, we evaluate the possibility of utilizing such systems for the identification and re-identification of traffic participants using the unique ID of TPMS sensors.

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.

Traffic Data Collection Using Tire Pressure Monitoring System

In this paper we present initial results in utilization of TPMS (Tire Pressure Monitoring System) for collecting traffic data and deriving traffic information, i.e. travel times. The obtained results show that current detection ratio is less than 5 % and the obtained travel times are in consistency with referent data. The experiment is performed on DLR test track in Berlin. In particular, architecture of TPMS receiver is proposed. Next, the algorithm for reducing data redundancy and for deriving traffic information is introduced. Finally the obtained results are presented.

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).

Evaluating Tire Pressure Monitoring System for Traffic Management Purposes - Simulation Study

In this paper, we evaluate Tire Pressure Monitoring System (TPMS) for traffic management purposes. It has been shown that up to 60% of the vehicles can be detected in urban traffic environments, which makes it suitable for deriving: routes, travel times and the traffic state. In particular, the theoretical background and basic concepts are given. Furthermore, we present a simple simulation model of TPMS based on empirical investigations. A simulation platform, based on traffic simulator, used for evaluation is introduced. Next, simulation results related to the number of detected vehicles are given regarding detection range, sensor transmission period and traffic flow. The impact of the roadside units location, as well as the number of detected vehicles, is investigated by simulating a realistic traffic scenario. Finally, the applicability of TPMS for deriving different traffic information is evaluated.