Journal of large-scale research facilities, 2, A74 (2016) http://dx.doi.org/10.17815/jlsrf-2-123 Published: 25.05.2016 AIM Mobile Tra�c Acquisition: Instrument tool- box for detection and assessment of tra�c behavior Deutsches Zentrum für Luft- und Raumfahrt e.V., Institute of Transportation Systems * Instrument Scientists: - Sascha Knake-Langhorst, Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Verkehrssystemtechnik, Braunschweig, Germany, phone +49 531 295-3474, email: sascha.knake-langhorst@dlr.de - Kay Gimm, Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Verkehrssystemtechnik, Braunschweig, Germany, phone +49 531 295-3453, email: kay.gimm@dlr.de Abstract: The AIM Mobile Tra�c Acquisition as part of Test �eld AIM (Application Platform for Intel- ligent Mobility) are �eld instruments for detection and assessment of tra�c behavior based on a mobile and �exible architecture approach. They serve as a tool box for the purpose of analyzing natural tra�c behavior and phenomena, e.g. safety related phenomena, based on trajectories. Thus, the facility can be used for a number of applications in the �eld of intelligent mobility. 1 Motivation Test �eld AIM (Application Platform for Intelligent Mobility) is built-up by Institute of Transporta- tion Systems of German Aerospace Center (DLR) in Braunschweig, Germany to support and conduct research and development in the �eld of intelligent mobility (Schnieder & Lemmer, 2012, 2014). It consists of di�erent large research infrastructure facilities providing a wide range of services covering simulation environments, test tracks and �eld instruments. One of these services is called AIM Mo- bile Tra�c Acquisition, which consists of three installations to be combined to work as instrument for detection and assessment of tra�c behavior in measuring campaigns. *Cite article as: DLR Institute of Transportation Systems. (2016). AIM Mobile Tra�c Acquisition: Instrument toolbox for detection and assessment of tra�c behavior. Journal of large-scale research facilities, 2, A74. http://dx.doi.org/10.17815/jlsrf- 2-123 1 http://jlsrf.org/ http://dx.doi.org/10.17815/jlsrf-2-123 http://dx.doi.org/10.17815/jlsrf-2-123 http://dx.doi.org/10.17815/jlsrf-2-123 https://creativecommons.org/licenses/by/4.0/ Journal of large-scale research facilities, 2, A74 (2016) http://dx.doi.org/10.17815/jlsrf-2-123 2 Technical description The facility AIM Mobile Tra�c Acquisition consists of three portable sensor poles. They share their functional and software architecture with another service, the stationary AIM Research Intersection mentioned in Institute of Transportation Systems (2016). The following sections will describe the sensory set-up and give an overview about the primary output of the facilities. 2.1 Sensory set-up Figure 1 exemplarily shows the technical architecture of a sensor pole. The installation can roughly be divided into the pole itself holding a sensor head and di�erent antennas and a weather-proof cabinet, holding the di�erent processing computers as well as several electric and electronic devices. Every pole installation is based on a transportable concrete foundation. Figure 1: Technical architecture of a sensor pole (exemplary). The �eld of vision of two associated sensor poles can be fused to get a better performance of the detection. The communication for data exchange between the poles is done via WLAN. The poles have a remote access because of a LTE-connection. Figure 2 shows the di�erent sensor poles. One out of two similar poles is shown in front of the main station in Braunschweig in the left part of the picture. It uses a stereo camera setup. In the right part of the picture a stand-alone pole is illustrated. In addition to a stereo camera system, mono-cameras, a 24 GHz multi-range radar system and a laser scanner can be found. All the poles possess an active infrared lighting for arti�cial scene illumination, so that the system has the ability to be used day and night 24/7. 2 http://dx.doi.org/10.17815/jlsrf-2-123 https://creativecommons.org/licenses/by/4.0/ http://dx.doi.org/10.17815/jlsrf-2-123 Journal of large-scale research facilities, 2, A74 (2016) Figure 2: Mobile sensor poles at railway station and level crossing. 2.2 In- and outputs The sensor data is fused and processed to obtain the main output of the Mobile Tra�c Acquisition, which are trajectories of the detected tra�c participants. These trajectories hold information about the classi�cation and dimensions of the object as well as its location, velocity and other dynamic state variables. The closure state of the railway crossing can also be detected. Figure 3 and 4 show the corresponding scene videos with augmented object information in di�erent tra�c situations. The two angles of vision are shown in the left and the right side of the picture. Figure 3: Visualization of a railway crossing tra�c scene with augmented object information. Figure 4: Visualization of a shared space tra�c scene with augmented object information. 3 http://dx.doi.org/10.17815/jlsrf-2-123 https://creativecommons.org/licenses/by/4.0/ Journal of large-scale research facilities, 2, A74 (2016) http://dx.doi.org/10.17815/jlsrf-2-123 These trajectories are produced with a rate of 25Hz. They are automatically stored in a data base for o�ine analysis purposes with the respective scene videos for manual assessment and validation. 3 Project application examples One pole was placed at a railway level crossing in Braunschweig-Bienrode. There the tra�c behav- ior was investigated especially referring to red light violations. Details of this study can be seen in Grippenkoven et al. (2015) and Schnieder, Grippenkoven, Wang, & Lackhove (2015). In the future the system can be used to accompany the development of actions to increase the level crossing safety and evaluate speci�c measures. The system consisting out of two poles has been mounted for a test campaign in the front of the railway main station in Braunschweig. There the behavior of di�erent tra�c participants like pedestrians, cyclists, taxis and buses and trams can be observed at shared tra�c space. Prospectively the interaction between autonomous vehicles and vulnerable road users can be examined. References Grippenkoven, J., Gimm, K., Stamer, M., Naumann, A., & Schnieder, L. (2015). Fehlverhalten von Verkehrsteilnehmern an Bahnübergängen mit Blinklichtsicherung. Signal + Dranht, 12, 23-27. Institute of Transportation Systems. (2016). AIM Research Intersection: Instrument for tra�c detection and behavior assessment for a complex urban intersection. Journal of large-scale research facilities, 2, A65. http://dx.doi.org/10.17815/jlsrf-2-122 Lackhove, C., Grippenkoven, J., Lemmer, K., Schnieder, L., & Wang, W. (2013). Aufbau eines Forschungsbahnübergangs im Rahmen der Anwendungsplattform Intelligente Mobilität. Signal + Dranht, 6, 25-28. Schnieder, L., Grippenkoven, J., Wang, W., & Lackhove, C. (2015). Untersuchung beobachtbaren Verhal- tens von Straßenverkehrsteilnehmern am Forschungsbahnübergang Braunschweig-Bienrode. In 16. Symposium Automatisierungssysteme, Assistenzsysteme und eingebettete Systeme für Transportmittel (AAET) (p. 138-152). Braunschweig, Deutschland. (12. - 13. Feb. 2015) Schnieder, L., Grippenkoven, J., Wang, W., Lackhove, C., & Lemmer, K. (2015). Der Forschungs- bahnübergang – eine Forschungsinfrastruktur zur Untersuchung beobachtbaren Verhaltens von Straßenverkehrsteilnehmern. ZEVrail, 139, 73-81. Schnieder, L., & Lemmer, K. (2012). Anwendungsplattform Intelligente Mobilität - eine Plattform für die verkehrswissenschaftliche Forschung und die Entwicklung intelligenter Mobilitätsdienste. Internationales Verkehrswesen, 64(4), 62-63. Schnieder, L., & Lemmer, K. (2014). Entwicklung intelligenter Mobilitätsdienste im realen Verkehrsum- feld in der Anwendungsplattform Intelligenten Mobilität. Internationales Verkehrswesen, 66(2), 77-79. 4 http://dx.doi.org/10.17815/jlsrf-2-123 http://dx.doi.org/10.17815/jlsrf-2-122 https://creativecommons.org/licenses/by/4.0/ Motivation Technical description Sensory set-up In- and outputs Project application examples