Collision-free navigation of N-trailer vehicles with motion constraints

In this work, a collision-free navigation strategy for N-trailer vehicles is proposed. This approach is based on a scalable cascaded control scheme to perform several tasks simultaneously: trajectory tracking control, off-track error reduction, external obstacles avoidance, and inter-vehicle collision avoidance. To validate the proposed strategy, a Generalized N-trailer (GNT) structure with a car-like tractor and 10 trailers is tested in simulation to track an U-shape trajectory in presence of unknown obstacles, similar to the trajectories that agricultural vehicles must perform in real applications. The well-known information about external infrastructure is also considered to reduce unsafe trailers off-track errors in turning scenarios. Moreover, the motion constraints imposed by the car-like tractor physical limitations and the interconnections between trailers are also considered by restricting the control input in order to avoid collision between trailers. The simulation results obtained showed a safe navigation which performed feasible maneuvers without collisions between the vehicles' chain and any trailer or external obstacle

Generation of regions of interest with high potential to contain pedestrians using non-dense 3D reconstruction from monocular vision

[EN] Traffic accidents are a global public health problem, due to the high number of human victims and the elevated economic and social costs that generate. In this context, pedestrians are among the most important and vulnerable elements of the road scene that need to be protected. It is thus that, in this work an innovative proposal is presented where the monocular visual information is used to simulate the stereo vision, and from this :i) generate regions of interest (ROIs) with high possibility of containing a pedestrian, and ii) estimate the trajectory of the vehicle. Experiments have been developed into a dataset of images taken in several streets of Santiago (Región Metropolitana), Chile. This database was obtained using an experimental vehicle under real driving conditions during the day. The ROI detection rate is 86;6 % for distances less than 20 meters, 82;9 % for distances less than 30 meters and76;2 % for distances less than 40 meters.[ES] Los accidentes de tráfico son un problema de salud pública a escala mundial, por el alto número de víctimas humanas y los elevados costos económicos y sociales que generan. En este contexto, los peatones se encuentran entre los elementos más importantes y vulnerables de la escena vial que necesitan ser protegidos. Es así que en este trabajo se presenta una innovadora propuesta utilizado la información visual monocular para emular la visión estéreo, y a partir de ello: i) generar regiones de interés (ROIs) con alta posibilidad de contener un peatón, y ii) estimar la trayectoria del vehículo. Los experimentos han sido desarrollados sobre una base de datos de imágenes tomadas en varias calles de la ciudad de Santiago (Región-Metropolitana), Chile. Esta información fue obtenida usando una plataforma experimental en condiciones reales de conducción durante el día. La tasa de detección de ROIs es del 86;6 % para distancias menores a 20 metros, 82;9 % para distancias menores a 30 metros y del 76;2 % para distancias menores a 40 metros.Este proyecto ha sido financiado por la Comisión Nacional de Ciencia y Tecnología de Chile (Conicyt) a través del proyecto Fondecyt No. 11060251, por la Universidad de las Fuerzas Armadas-ESPE, a través del Plan de Movilidad con Fines de Investigación (Orden Rectorado 2017-109-ESPE-d), el proyecto de investigación Nro. 2014-PIT-007 y por la empresa Tecnologías I&H.Zubiaguirre-Bergen, I.; Torres-Torriti, M.; Flores-Calero, M. (2018). Generación de Regiones con Potencial de Contener Peatones usando Reconstrucción 3D No Densa a partir de Visión Monocular. 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URL: https://www.profesoresyseguridadvial.com/colombia-datos-de-seguridad-vial/Horgan, J., Hughes, C., McDonald, J., Yogamani, S., 2015. Vision-Based Driver Assistance Systems: Survey, Taxonomy and Advances. In: IEEE 18th International Conference on Intelligent Transportation Systems (ITSC). pp. 2032-2039. https://doi.org/10.1109/ITSC.2015.329Keller, C., Enzweiler, M., Gavrila, D., July 2011. A new benchmark for stereobased pedestrian detection. In: IEEE Intelligent Vehicles Symposium (IV). pp. 691-696.Kohler, S., Goldhammer, M., Zindler, K., Doll, K., Dietmeyer, K., September 2015. Stereo-vision-based pedestrian's intention detection in a moving vehicle. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems. pp. 2317-2322. https://doi.org/10.1109/ITSC.2015.374La Tercera, 2014. Chile es el país con mayor tasa de peatones fallecidos entre los países de la OCDE. URL: http://www.latercera.com/noticia/nacional/2014/10/680-601399-9-chile-//es-el-pais-con-mayor-tasa-de-peatones-fallecidos-entre-//los-paises-de-la.shtmlLi, X., Flohr, F., Yang, Y., Xiong, H., Braun, M., Pan, S., Li, K., Gavrila, D. M., June 2016. A new benchmark for vison-based cyclist detection. In: IEEE Intelligent Vehicles Symposium. pp. 1109-1114.Ma, G., Muller, D., Park, S.-B., Muller-Schneiders, S., Kummert, A., march 2009. Pedestrian detection using a single monochrome camera. Intelligent Transport Systems, IET 3 (1), 42 -56. https://doi.org/10.1049/iet-its:20080001Mammeri, A., Zuo, T., Boukerche, A., April 2016. Extending the Detection Range of Vision-Based Vehicular Instrumentation. IEEE Transactions on Instrumentation and Measurement 65 (4), 856-873. https://doi.org/10.1109/TIM.2016.2514780Mesmakhosroshahi, M., Chung, K.-H., Lee, Y., Kim, J., November 2014. Depth gradient based region of interest generation for pedestrian detection. In: IEEE International Conference on SoC Design (ISOCC). pp. 156-157. https://doi.org/10.1109/ISOCC.2014.7087674Min, K., Son, H., Choe, Y., Kim, Y., June 2013. Real-time pedestrian detection based on a hierarchical two-stage support vector machine. In: IEEE 8th Conference on Industrial Electronics and Applications (ICIEA). pp. 114-119.Oikawaa, S., Matsuia, Y., Doib, T., Sakuraic, T., February 2016. Relation between vehicle travel velocity and pedestrian injury risk in different age groups for the design of a pedestrian detection system. Safety Science 82, 361-367. https://doi.org/10.1016/j.ssci.2015.10.003Overett, G., Petersson, L., Brewer, N., Andersson, L., Pettersson, N., 2008. A new pedestrian dataset for supervised learning. URL: https://research.csiro.au/data61/automap-datasets-and-code/Russell, B. C., Torralba, A., Murphy, K. P., Freeman, W. T., May 2008. Label me, a database and web-based tool for image annotation. International Journal of Computer Vision (1-3). URL: http://labelme.csail.mit.edu/Shi, J., Tomasi, C., jun 1994. Good features to track. In: Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on. pp. 593 -600. https://doi.org/10.1109/CVPR.1994.323794Shou, N., Peng, H., Wang, H., Meng, L.-M., Du, K.-L., Octuber 2012. An rois based pedestrian detection system for single images.Tetik, Y., Bolat, B., June 2011. Pedestrian detection from still images. In: IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA). pp. 540-544.Villalón-Sepúlveda, G., Torres-Torriti, M., Flores-Calero, M., May 2017. Traffic Sign Detection System for Locating Road Intersections and Roundabouts: The Chilean Case. Sensors MDPI 17 (6), 138-147. https://doi.org/10.3390/s17061207Wang, L., Shi, J., Song, G., Shen, I.-f., 2007. Object detection combining recognition and segmentation. URL: https://www.cis.upenn.edu/~jshi/ped_html/Wang, X., Wang, M., Li, W., December 2014. Scene-Specific Pedestrian Detection for Static Video Surveillance. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 361-374. https://doi.org/10.1109/TPAMI.2013.124World Health Organization WHO, 2015. Road traffic injuries.Yuan, Y., Lin, W., Fang, Y., September 2015. Is pedestrian detection robust for surveillance? In: Image Processing (ICIP), 2015 IEEE International Conference on. pp. 2776 - 2780.Zhang, C., Chung, K.-H., Kim, J., November 2015a. Region-of-interest reduction using edge and depth images for pedestrian detection in urban areas.Zhang, X., Hu, H.-M., Jiang, F., Li, B., May 2015b. Pedestrian detection based on hierarchical co-occurrence model for occlusion handling. Neurocomputing 168, 861-870. https://doi.org/10.1016/j.neucom.2015.05.038Zhang, Z., Tao, W., Sun, K., Hu, W., Yao, L., May 2016. Pedestrian detection aided by fusion of binocular information. 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Comparison of 3D scan matching techniques for autonomous robot navigation in urban and agricultural environments

Global navigation satellite system (GNSS) is the standard solution for solving the localization problem in outdoor environments, but its signal might be lost when driving in dense urban areas or in the presence of heavy vegetation or overhanging canopies. Hence, there is a need for alternative or complementary localization methods for autonomous driving. In recent years, exteroceptive sensors have gained much attention due to significant improvements in accuracy and cost-effectiveness, especially for 3D range sensors. By registering two successive 3D scans, known as scan matching, it is possible to estimate the pose of a vehicle. This work aims to provide in-depth analysis and comparison of the state-of-the-art 3D scan matching approaches as a solution to the localization problem of autonomous vehicles. Eight techniques (deterministic and probabilistic) are investigated: iterative closest point (with three different embodiments), normal distribution transform, coherent point drift, Gaussian mixture model, support vector-parametrized Gaussian mixture and the particle filter implementation. They are demonstrated in long path trials in both urban and agricultural environments and compared in terms of accuracy and consistency. On the one hand, most of the techniques can be successfully used in urban scenarios with the probabilistic approaches that show the best accuracy. On the other hand, agricultural settings have proved to be more challenging with significant errors even in short distance trials due to the presence of featureless natural objects. The results and discussion of this work will provide a guide for selecting the most suitable method and will encourage building of improvements on the identified limitations.This project has been supported by the National Agency of Research and Development (ANID, ex-Conicyt) under Fondecyt grant 1201319, Basal grant FB0008, DGIIP-UTFSM Chile, National Agency for Research and Development (ANID)/PCHA/Doctorado Nacional/2020-21200700, Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Span ish Ministry of Science, Innovation and Universities (project RTI2018- 094222-B-I00) for partially funding this research. The Spanish Ministry of Education is thanked for Mr. J. Gene’s pre-doctoral fellowships (FPU15/03355). We would also like to thank Nufri (especially Santiago Salamero and Oriol Morreres) for their support during data acquisitio

Real-time approaches for characterization of fully and partially scanned canopies in groves

Efficient information management in orchard characterization leads to more efficient agricultural processes. In this brief, a set of computational geometry methods are presented and evaluated for orchard characterization; in particular, for the estimation of canopy volume and shape in groves and orchards using a LiDAR (Light Detection And Ranging) sensor mounted on an agricultural service unit. The proposed approaches were evaluated and validated in the field, showing they are convergent in the estimation process and that they are able to estimate the crown volume for fully scanned canopies in real time; for partially observed tree crowns, accuracy decreases up to 30% (the worst case). The latter is the major contribution of this brief since it implies that the automated service unit does not need to cover all alley-ways for an accurate modeling of the orchard, thus saving valuable resources.The authors would like to thank to CONICYT (Chile): FONDECYT Grant 1140575 and Basal Grant FB0008. Also, this research was partially funded by the Spanish Ministry of Science and Innovation and by the European Union through the FEDER funds (projects Optidosa-AGL2007-66093-C04-03 and Safespray-AGL2010-22304-C04-03)

A Density-Based Approach for Effective Pedestrian Counting at Bus Stops

Abstract-Accurately counting people waiting at bus stops is essential for automated bus fleet scheduling and dispatch. Estimating the passenger demand in regular open bus stops is a nontrivial problem because of the varying conditions, such as illumination, crowdedness, people poses, to name a few. This paper presents a simple, but very effective approach to estimate the passenger count using people density estimates. People density is obtained from foreground segmentation using a Gaussian mixture background model. A linear model, which is employed to correct the densities due to perspective scaling for people far from the camera position, yields the final people count estimates. The approach is compared to the well-know Viola-Jones detector and shown to yield better people count estimates despite its simplicity, because it is more robust to occlusions, pose changes, and due to the fact that it does not attempt to find body parts. The proposed method is general and can be employed to count people in other public spaces, such as buildings

Robust Lane Sensing and Departure Warning under Shadows and Occlusions

A prerequisite for any system that enhances drivers’ awareness of road conditions and threatening situations is the correct sensing of the road geometry and the vehicle’s relative pose with respect to the lane despite shadows and occlusions. In this paper we propose an approach for lane segmentation and tracking that is robust to varying shadows and occlusions. The approach involves color-based clustering, the use of MSAC for outlier removal and curvature estimation, and also the tracking of lane boundaries. Lane boundaries are modeled as planar curves residing in 3D-space using an inverse perspective mapping, instead of the traditional tracking of lanes in the image space, i.e., the segmented lane boundary points are 3D points in a coordinate frame fixed to the vehicle that have a depth component and belong to a plane tangent to the vehicle’s wheels, rather than 2D points in the image space without depth information. The measurement noise and disturbances due to vehicle vibrations are reduced using an extended Kalman filter that involves a 6-DOF motion model for the vehicle, as well as measurements about the road’s banking and slope angles. Additional contributions of the paper include: (i) the comparison of textural features obtained from a bank of Gabor filters and from a GMRF model; and (ii) the experimental validation of the quadratic and cubic approximations to the clothoid model for the lane boundaries. The results show that the proposed approach performs better than the traditional gradient-based approach under different levels of difficulty caused by shadows and occlusions