Among Advanced Driver Assistance Systems (ADAS) pedestrian detection is a common issue due to the vulnerability of pedestrians in the event of accidents. In the present work, a novel approach for pedestrian detection based on data fusion is presented. Data fusion helps to overcome the limitations inherent to each detection system (computer vision and laser scanner) and provides accurate and trustable tracking of any pedestrian movement. The application is complemented by an efficient communication protocol, able to alert vehicles in the surroundings by a fast and reliable communication. The combination of a powerful location, based on a GPS with inertial measurement, and accurate obstacle localization based on data fusion has allowed locating the detected pedestrians with high accuracy. Tests proved the viability of the detection system and the efficiency of the communication, even at long distances. By the use of the alert communication, dangerous situations such as occlusions or misdetections can be avoided.
Garcia, F.; de la Escalera, A.; Armingol, J.M.; Herrero, J.G.; Llinas, J. Fusion Based Safety Application for Pedestrian Detection with Danger Estimation. Proceedings of the 14th International Conference on Information Fusion (FUSION), Chicago, IL, USA, 5–8 July 2011; pp. 1–8.
Garcia, F.; de la Escalera, A.; Armingol, J.M.; Jimenez, F. Context Aided Fusion Procedure for Road Safety Application. Proceedings of 2012 IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Hamburg, Germany, 13–15 September 2012; pp. 407–412.
García, F.; Jiménez, F.; Naranjo, J.E.; Zato, J.G.; Aparicio, F.; Armingol, J.M.; de la Escalera, A. Environment perception based on LIDAR sensors for real road applications. Robotica 2012, 30, 185–193.
Premebida, C.; Monteiro, G.; Nunes, U.; Peixoto, P. A Lidar and Vision-Based Approach for Pedestrian and Vehicle Detection and Tracking. Proceedings of IEEE Intelligent Transportation Systems Conference ITSC, Seattle, WA, USA, 30 September–3 October 2007; pp. 1044–1049.
Spinello, L.; Siegwart, R. Human Detection Using Multimodal and Multidimensional Features. Proceedings of 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, 19–23, May 2008; pp. 3264–3269.
Premebida, C.; Ludwig, O.; Silva, M.; Nunes, U. A Cascade Classifier Applied in Pedestrian Detection Using Laser and Image-Based Features. Proceedings of IEEE Intelligent Transportation Systems Conference ITSC, Madeira Island, Portugal, 19–22 Sept. 2010; pp. 1153–1159.
Kaempchen, N.; Buehler, M.; Dietmayer, K. Feature-Level Fusion for Free-Form Object Tracking Using Laserscanner and Video. Proceedings of IEEE Intelligent Vehicles Symposium 2005, Las Vegas, ND, USA, 6–8 June 2005; pp. 453–458.
Hwang, J.P.; Cho, S.E.; Ryu, K.J.; Park, S.; Kim, E. Multi-Classifier Based LIDAR and Camera Fusion. Proceedings of IEEE Intelligent Transportation Systems Conference ITSC, Seattle, WA, USA, 30 September–3 October 2007; pp. 467–472.
Szarvas, M.; Sakai, U. Real-time Pedestrian Detection Using LIDAR and Convolutional Neural Networks. Proceedings of 2006 IEEE Intelligent Vehicles Symposium, Tokyo, Japan, 13–15 June 2006; pp. 213–218.
Ludwig, O.; Premebida, C.; Nunes, U.; Ara, R. Evaluation of Boosting-SVM and SRM-SVM Cascade Classifiers in Laser and Vision-Based Pedestrian Detection. Proceedings of IEEE Intelligent Transportation Systems Conference ITSC, Washington, DC, USA, 5–7 October 2011; pp. 1574–1579.
Broggi, A.; Cerri, P.; Ghidoni, S.; Grisleri, P.; Jung, H.G. Localization and Analysis of Critical Areas in Urban Scenarios. Proceedings of IEEE Intelligent Vehicles Symposium, Beijing, China, 12–15 October 2008; pp. 1074–1079.
Misener, J.A. VII California: Development and Deployment Proof of Concept and Group-Enabled Mobility and Safety (GEMS). California PATH Research Report, UCB-ITS-PRR-2010-26; Department of Transportation, University of California: Berkeley, CA, USA, 2010.
Glaser, S.; Nouveliere, L.; Lusetti, B. Speed Limitation Based on an Advanced Curve Warning System. Proceedings of 2007 IEEE Intelligent Vehicles Symposium, Seattle, WA, USA, 30 September–3 October 2007; pp. 686–691.
Buchenscheit, A.; Schaub, F.; Kargl, F.; Weber, M. A VANET-Based Emergency Vehicle Warning System. Proceedings of 2009 IEEE Vehicular Networking Conference VNC, Tokyo, Japan, 28–30 October 2009; pp. 1–8.
Garcia, F.; Musleh, B.; de Escalera, A.; Armingol, J.M. Fusion Procedure for Pedestrian Detection Based on Laser Scanner and Computer Vision. Proceedings of 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, USA, 5–7 October 2011; pp. 1325–1330.
Dalal, N.; Triggs, B. Histograms of Oriented Gradients for Human Detection. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 (CVPR 2005), San Diego, CA, USA, 20–26 June 2005; Volume 1, pp. 886–893.
Kohler, M. Using the Kalman Filter to track Human Interactive Motion—Modelling and Initialization of the Kalman Filter for Translational Motion. Technical Report 629; Informatik VII, University of Dortmund: Dortmund, Germany, 1997.
Gnawali, O.; Fonseca, R.; Jamieson, K.; Levis, P. CTP: Robust and Efficient Collection through Control and Data Plane Integration. Technical Report SING-08-02; University of Southern California: Los Angeles, CA, USA, 2008.