OJS Kammel, S., Ziegler, J., Pitzer, B., Werling, M., Gindele, T., Jagzent, D., … Stiller, C. (2008). Team AnnieWAY’s autonomous system for the 2007 DARPA Urban Challenge. Journal of Field Robotics, 25(9), 615-639. doi:10.1002/rob.20252 Spies, M., & Spies, H. (2006). Automobile Lidar Sensorik: Stand, Trends und zukünftige Herausforderungen. Advances in Radio Science, 4, 99-104. doi:10.5194/ars-4-99-2006

[EN] Road security applications are getting more complex thanks to the latest advances. These applications require sensors able to provide information to perform complex and demanding tasks. Laser Scanners sensors have proved to be a trustable sensor family. This reliability has lead to increase the research activities related to laser scanners usability in road applications. Every day is more frequent to find Advance Driver Assistant Systems (ADAS) that takes advantage of the characteristics of these devices. These systems performs different task, some of them related to detection and prediction of movements of obstacles in the surroundings of a vehicle. Present work studies the possibility of using two different sensors in road applications. The second part of the work focuses on the presentation of a new algorithm able to detect moving obstacles in the surrounding of a vehicle using scanner laser. Bar-Shalom, Y. (1978). Tracking methods in a multitarget environment. IEEE Transactions on Automatic Control, 23(4), 618-626. doi:10.1109/tac.1978.1101790 Broggi, A., Cappalunga, A., Caraffi, C., Cattani, S., Ghidoni, S., Grisleri, P., … Beck, J. (2008). The passive sensing suite of the TerraMax autonomous vehicle. 2008 IEEE Intelligent Vehicles Symposium. doi:10.1109/ivs.2008.4621208 Broggi, A., Cerri, P., Ghidoni, S., Grisleri, P., & Jung, H. G. (2008). Localization and analysis of critical areas in urban scenarios. 2008 IEEE Intelligent Vehicles Symposium. doi:10.1109/ivs.2008.4621266 Gandhi, T., & Trivedi, M. M. (2007). Pedestrian Protection Systems: Issues, Survey, and Challenges. IEEE Transactions on Intelligent Transportation Systems, 8(3), 413-430. doi:10.1109/tits.2007.903444 Gate, G., & Nashashibi, F. (2008). Using targets appearance to improve pedestrian classification with a laser scanner. 2008 IEEE Intelligent Vehicles Symposium. doi:10.1109/ivs.2008.4621253 Gate, G., Breheret, A., & Nashashibi, F. (2009). Centralized fusion for fast people detection in dense environment. 2009 IEEE International Conference on Robotics and Automation. doi:10.1109/robot.2009.5152645 Gidel, S., Blanc, C., Chateau, T., Checchin, P., & Trassoudaine, L. (2009). A method based on multilayer laserscanner to detect and track pedestrians in urban environment. 2009 IEEE Intelligent Vehicles Symposium. doi:10.1109/ivs.2009.5164270 Kaempchen, N., Buehler, M., & Dietmayer, K. (2005). Feature-level fusion for free-form object tracking using laserscanner and video. IEEE Proceedings. Intelligent Vehicles Symposium, 2005. doi:10.1109/ivs.2005.1505145 Mendes, A., Bento, L. C., & Nunes, U. (s. f.). Multi-target detection and tracking with a laserscanner. IEEE Intelligent Vehicles Symposium, 2004. doi:10.1109/ivs.2004.1336486 Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., Dolgov, D., Ettinger, S., … Thrun, S. (2008). Junior: The Stanford entry in the Urban Challenge. Journal of Field Robotics, 25(9), 569-597. doi:10.1002/rob.20258 Nashashibi, F., & Bargeton, A. (2008). Laser-based vehicles tracking and classification using occlusion reasoning and confidence estimation. 2008 IEEE Intelligent Vehicles Symposium. doi:10.1109/ivs.2008.4621244 Ogawa, T., & Takagi, K. (s. f.). Lane Recognition Using On-vehicle LIDAR. 2006 IEEE Intelligent Vehicles Symposium. doi:10.1109/ivs.2006.1689684 Premebida, C., Ludwig, O., & Nunes, U. (2009). LIDAR and vision-based pedestrian detection system. Journal of Field Robotics, 26(9), 696-711. doi:10.1002/rob.20312 Reid, D. (1979). An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control, 24(6), 843-854. doi:10.1109/tac.1979.1102177 Streller, D., Furstenberg, K., & Dietmayer, K. (s. f.). Vehicle and object models for robust tracking in traffic scenes using laser range images. Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems. doi:10.1109/itsc.2002.1041199 Thrun, S. (2003). Autonomous Robots, 15(2), 111-127. doi:10.1023/a:1025584807625 Urmson, C., Anhalt, J., Bagnell, D., Baker, C., Bittner, R., Clark, M. N., … Ferguson, D. (2008). Autonomous driving in urban environments: Boss and the Urban Challenge. Journal of Field Robotics, 25(8), 425-466. doi:10.1002/rob.20255 G. Widmann, M. Daniels, L. Hamilton, L. Humm,B. Riley,J. K. Schiffmann, D.E. Schnelkery, W. H. Wishon, (2000) “Comparison of lidar-based and radar-based adaptive cruise control systems”. SAE Technical paper Series. SAE 2000 World Congress, paper n° 2000-01-0345. Detroit Michigan, 6–9 March.

[ES] Los últimos avances en seguridad vial, con sistemas cada vez más complejos, requieren de los más modernos sistemas de adquisición de información. La naturaleza misma del problema requiere sensores capaces de proveer información fiable para tareas complejas y exigentes. Los escáneres láser (LIDAR) han demostrado ser una familia de sensores altamente fiable, por lo que durante los últimos años los esfuerzos dedicados a investigar posibles aplicaciones viales han ido en aumento. De esta forma, es cada vez más frecuente observar sistemas de ayuda a la conducción (ADAS) con este tipo de dispositivos que proveen de información del entorno necesaria para realizar tareas complejas como detección y predicción de situaciones peligrosas. En el presente trabajo, dos sistemas LIDAR han sido probados para comprobar sus capacidades reales en entornos viales. En segundo término, se propone una aplicación que hace uso de las capacidades de dichos sensores para la detección y clasificación de obstáculos en entornos viarios. El trabajo presentado ha sido desarrollado gracias a la financiación del ministerio de Ciencia e Innovación (SIAC proyecto TRA2007-67786-C02-01 y TRA2007-67786-C02-02) y la CAM mediante el proyecto SEGVAUTO. García, F.; Jiménez, F.; Naranjo, JE.; Zato, JG.; Aparicio, F.; De La Escalera, A. (2011). Laser Scanner Como Sistema de Detección de Entornos Viales. Revista Iberoamericana de Automática e Informática industrial. 8(1):44-53. https://doi.org/10.1016/S1697-7912(11)70007-3

[is] The latest developments in road safety, with increasingly complex systems, require the most modern information acquisition systems. The very nature of the problem requires sensors capable of providing reliable information for complex and demanding tasks. Laser scanners (LIDAR) have proven to be a highly reliable family of sensors, so efforts to investigate potential road applications have been increasing in recent years. In this way, driving aids systems (ADAS) are increasingly observed with such devices that provide information from the environment needed to perform complex tasks such as detection and prediction of dangerous situations. In this work, two LIDAR systems have been tested to check their real skills in road environments. Secondly, an application is proposed that makes use of the capabilities of these sensors for the detection and classification of obstacles in road environments. The work presented has been developed thanks to the funding of the Ministry of Science and Innovation (SIAC project TRA2007-67786-C02-01 and TRA2007-67786-C02-02) and the CAM through the SEGVAUTO project. García, F.; Jiménez, F.; Naranjo, JE.; Zato, JG.; Aparicio, F.; De La Escalera, A. (2011). Laser Scanner as a Vial Entrant Detection System. Ibero-American magazine for Automatic and Industrial Informatics. 8 (1): 44-53. https://doi.org/10.1016/S1697-7912 (11) 70007-3

Laser Scanner to detect Road Environments

Laser Scanner Como Sistema de Detección de Entornos Viales

Abstract