Sistema Avanzado de Ayuda a la Conducción (ADAS) en rotondas/glorietas usando imágenes aéreas y técnicas de Inteligencia Artificial para la mejora de la seguridad vial

Referencias bibliográficas

• Akçelik, R. (2004). Roundabouts with unbalanced flow patterns. Paper presented at the Institute of Trans- portation Engineers 2004 Annual Meeting.
• Anagnostopoulos, A., Kehagia, F., Damaskou, E., Mouratidis, A., & Aretoulis, G. (2021). Predicting Roundabout Lane Capacity using Artificial Neural Networks. Journal of Engineering Science and Technology Review, 14, 210-215.
• Badue, C., Guidolini, R., Carneiro, R., Azevedo, P., Cardoso, V., Forechi, A., . . . De Souza, A. (2019). Self-Driving Cars: A Survey,.
• Ballard, D., & Brown, C. (1982). Computer vision. englewood cliffs. J: Prentice Hall.
• BBC. (2020, 09 16). Uber's self-driving operator charged over fatal crash. Retrieved 12 31, 2021, from https://www.bbc.com/news/technology-54175359
• BBC. (2021, 12 15). Tesla Model 3: Paris' largest taxi firm suspends cars after fatal crash. Retrieved 12 31, 2021, from https://www.bbc.com/news/world-europe-59647069
• BCG. (2015). A Roadmap to Safer Driving through Advanced Driver Assistance Systems. Retrieved 12 30, 2021, from https://image-src.bcg.com/Images/MEMA-BCG-A-Roadmap-to-Safer-Driving-Sep-2015_tcm9-63787.pdf
• Behrisch, M., Bieker, L., Erdmann, J., & Krajzewicz, D. (2011). SUMO - Simulation of Urban MObility.
• Bernhard, W., & Portmann, P. (2000). Traffic simulation of roundabouts in Switzerland. 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165), 2, 1148-1153.
• Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. (2020). Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
• Bock, J., Krajewski, R., Moers, T., & Vater, L. (n.d.). UniD. The University Drone Dataset. Naturalistic Trajectories of Vehicles and Vulnerable Road Users Recorded at the RWTH Aachen University Campus. Retrieved 03 26, 2022, from https://www.unid-dataset.com/
• Bock, J., Krajewski, R., Moers, T., Runde, S., Vater, L., & Eckstein, L. (2020). The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections. 2020 IEEE Intelligent Vehicles Symposium (IV), 1929-1934.
• Breuer, A., Termöhlen, J.-A., Homoceanu, S., & Fingscheidt, T. (2020). OpenDD: A Large-Scale Roundabout Drone Dataset. Proceedings of International Conference on Intelligent Transportation Systems}.
• Brilon, W. (1991). Intersections Without Traffic Signals II: Proceedings of an International Workshop. Springer-Verlag.
• Brilon, W., Wu, N., & Bondzio, L. (1997). Unsignalized intersections in Germany — a state of the art 1997. Third international symposium on intersections without traffic signals, 61–70.
• Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679-698.
• Chang, Y.-C., Huang, C., Chuang, J.-H., & Liao, I.-C. (2018). Pedestrian Detection in Aerial Images Using Vanishing Point Transformation and Deep Learning. 2018 25th IEEE International Conference on Image Processing (ICIP), 1917-1921.
• Chen, Z., Wang, C., Wen, C., Teng, X., Chen, Y., Guan, H., . . . Li, J. (2015). Vehicle detection in high-resolution aerial images via sparse representation and superpixels. IEEE Transactions on Geoscience and Remote Sensing, 54(1), 103-116.
• Cheng, G., Wang, Y., Xu, S., Wang, H., Xiang, S., & Pan, C. (2017). Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network. IEEE Transactions on Geoscience and Remote Sensing, 55(5), 3322-3337.
• Chin, H. C. (1985). SIMRO: A model to simulate traffic at roundabouts. Traffic Engineering and Control, 26, 109–113.
• Chung, E., Young, W., & Akçelik, R. (1992). Comparison of roundabout capacity and delay estimates from analytical and simulation models. 16th ARRB Conference, 16, 369–385.
• Dalal, N., & Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, 886-893.
• De-Las-Heras, G., Sánchez-Soriano, J., & Puertas, E. (2021). Advanced Driver Assistance Systems (ADAS) Based on Machine Learning Techniques for the Detection and Transcription of Variable Message Signs on Roads. Sensors, 21(17).
• Deng, Z., Sun, H., Zhou, S., Zhao, J., & Zou, H. (2017). Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(8), 3652--3664.
• Dijkstra, K., van de Loosdrecht, J., Schomaker, L. R., & Wiering, M. A. (2019). Hyperspectral demosaicking and crosstalk correction using deep learning. Machine Vision and Applications, 30, 1-21.
• Dirección General de Tráfico - Ministerio del Interior. (2021, 12 23). Código de Tráfico y Seguridad Vial. Retrieved 01 01, 2022, from https://www.boe.es/biblioteca_juridica/codigos/abrir_pdf.php?fich=020_Codigo_de_Trafico_y_Seguridad_Vial.pdf
• Dirección General de Tráfico - Ministerio del Interior. (2018). Cuestiones De Seguridad Vial. Retrieved 12 30, 2021, from http://www.dgt.es/Galerias/seguridad-vial/formacion-vial/cursos-para-profesores-y-directores-de-autoescuelas/XXI-Cuso-Profesores/Manual-II-Cuestiones-de-Seguridad-Vial-2018.pdf
• DJI. (n.d.). DJI Mini 2. Retrieved 03 26, 2022, from https://www.dji.com/ca/mini-2/specs
• Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., . . . Tian, Q. (2018). The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking. European Conference on Computer Vision (ECCV).
• Elkhrachy, I. (2021). Accuracy Assessment of Low-Cost Unmanned Aerial Vehicle (UAV) Photogrammetry. Alexandria Engineering Journal, 60(6), 5579-5590.
• Everingham, M., Van Gool, L., & Williams, C. (2010). The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision, 88, 303-338.
• Felzenszwalb, P., McAllester, D., & Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part mode, computer vision and pattern recognition. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8.
• fizyr. (n.d.). Keras RetinaNet. Retrieved 04 05, 2022, from https://github.com/fizyr/keras-retinanet
• Gallardo, V. (2005). Funciones de las rotondas urbanas y requerimientos urbanísticos de organización.
• García Cuenca, L., Puertas, E., Fernandez Andrés, J., & Aliane, N. (2019). Autonomous Driving in Roundabout Maneuvers Using Reinforcement Learning with Q-Learning. Electronics, 8(12).
• García Cuenca, L., Sanchez-Soriano, J., Puertas, E., Fernandez Andrés, J., & Aliane, N. (2019). Machine Learning Techniques for Undertaking Roundabouts in Autonomous Driving. Sensors, 19(10).
• Girshick, R. (2015). Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV), 1440-1448.
• Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2013). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
• Gonzalez, R., Woods, R., & Masters, B. (2009). Digital image processing.
• Guichet, B. (1997). Roundabouts in France: Development, safety, design, and capacity. Proceedings of the third international symposium on intersections without traffic signals, 100–105.
• Gupta, A., Watson, S., & Yin, H. (2021). Deep learning-based aerial image segmentation with open data for disaster impact assessment. Neurocomputing, 439, 22-33.
• Hagring, O. (2000). Effects of OD Flows on Roundabout Entry Capacity. Fourth international symposium on highway capacity(434–445).
• Hough, P. (1962, 12 18). U.S. Patent No. US3069654A.
• ImageJ. (n.d.). Hough Circle Transform. Retrieved 01 16, 2022, from https://imagej.net/plugins/hough-circle-transform
• Kathuria, A. (n.d.). Data Augmentation for Bounding Boxes: Rotation and Shearing. Retrieved 04 05, 2022, from https://blog.paperspace.com/data-augmentation-for-object-detection-rotation-and-shearing/
• Kembhavi, A., Harwood, D., & Davis, L. S. (2010). Vehicle detection using partial least squares. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(6), 1250-1265.
• Kim, J., Candemir, S., Chew, E. Y., & Thoma, G. R. (2018). Region of Interest Detection in Fundus Images Using Deep Learning and Blood Vessel Information. 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), 357-362.
• Kimber, R. M. (1980). LR942 The traffic capacity of roundabouts. Transport and Road Research Laboratory.
• Krajewski, R., Bock, J., Kloeker, L., & Eckstein, L. (2018). The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2118-2125.
• Krajewski, R., Bock, J., Moers, T., & Vater, L. (n.d.). ExiD. The Exits and Entries Drone Dataset. Naturalistic Trajectories of Vehicles Recorded at Exits and Entries of German Highways . Retrieved 03 26, 2022, from https://www.exid-dataset.com/
• Krajewski, R., Moers, T., Bock, J., Vater, L., & Eckstein, L. (2020). The rounD Dataset: A Drone Dataset of Road User Trajectories at Roundabouts in Germany. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), 1-6, 2020.
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM, 60(6), 84–90.
• Kukkala, V. K., Tunnell, J., Pasricha, S., & Bradley, T. (2018). Advanced Driver- Assistance Systems: A Path Toward Autonomous Vehicles. IEEE Consumer Electronics Magazine, 18-25.
• Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2020). Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 318-327.
• Liu, K., & Mattyus, G. (2015). Fast Multiclass Vehicle Detection on Aerial Images. IEEE Geoscience and Remote Sensing Letters, 12(9), 1938-1942.
• Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. (2016). Ssd: Single shot multibox detector. European conference on computer vision.
• Macioszek, E. (2020). Roundabout Entry Capacity Calculation—A Case Study Based on Roundabouts in Tokyo, Japan, and Tokyo Surroundings. Sustainability, 12, 1533.
• Marlow, M., & Maycock, G. (1982). SR724 The effect of Zebra crossings on junction entry capacities. Crowthorne: Transport and Road Research Laboratory.
• Mauro, R., & Guerrieri, M. (2012). Right-turn Bypass Lanes at Roundabouts: Geometric Schemes and Functional Analysis. Modern Applied Science, 7.
• Michon, J. (1993). Generic Intelligent Driver Support: A Comprehensive Report on GIDS.
• Microsimulation, P. (2011). S-Paramics (Parallel Microscopic Traffic Simulator).
• Montgomery, F., Tenekeci, G., & Wainaina, S. (2010). Roundabout capacity in adverse weather and light conditions. Proceedings of The Institution of Civil Engineers-transport - PROC INST CIVIL ENG-TRANSPORT, 163, 29-39.
• Naranjo, J., Sotelo, M.-A., Gonzalez, C., Garcia, R., & Pedro, T. (2007). Using Fuzzy Logic in Automated Vehicle Control. Intelligent Systems, IEEE, 22, 36-45.
• O'Kane, S. (2018, 04 19). How Tesla and Waymo are tackling a major problem for self- driving cars: data. Retrieved 01 01, 2022, from https://www.theverge.com/transportation/2018/4/19/17204044/tesla-waymo- self-driving-car-data-simulation
• OpenCV. (n.d.). Canny Edge Detector . Retrieved 01 16, 2022, from https://docs.opencv.org/4.x/da/d5c/tutorial_canny_detector.html
• OpenCV. (n.d.). Image Thresholding . Retrieved 01 15, 2022, from https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html
• OpenCV. (n.d.). Morphological Transformations. Retrieved 01 15, 2022, from https://docs.opencv.org/3.4/d9/d61/tutorial_py_morphological_ops.html
• OpenCV. (n.d.). Operations on arrays. Retrieved 01 15, 2022, from https://docs.opencv.org/3.4/d2/de8/group__core__array.html#ga6fef31bc8c4071cbc114a758a2b79c14
• OpenCV. (n.d.). Smoothing Images. Retrieved 01 15, 2022, from https://docs.opencv.org/4.x/d4/d13/tutorial_py_filtering.html
• OpenCV. Open Source Computer Vision. (n.d.). Hough Circle Transform . Retrieved 01 16, 2022, from https://docs.opencv.org/3.4/d4/d70/tutorial_hough_circle.html
• OpenCV. Open Source Computer Vision. (n.d.). Hough Line Transform . Retrieved 01 16, 2022, from https://docs.opencv.org/4.x/d9/db0/tutorial_hough_lines.html
• Popular Science. (2021, 12 30). Tesla caps off a tumultuous year with a massive recall. Retrieved 12 31, 2021, from https://www.popsci.com/technology/tesla-recalls-vehicles-safety-issues/
• Puertas, E., De-Las-Heras, G., Fernández-Andrés, J., & Sánchez-Soriano, J. (2022). Dataset: Roundabout Aerial Images for Vehicle Detection. Data, 7(4).
• Rebaza, J. (2007). Detección de Bordes Mediante el Algoritmo de Canny. Master’s Thesis. Trujillo, Peru: Universidad Nacional de Trujillo.
• Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition.
• Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767.
• Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 779–788.
• Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137-1149.
• Robicquet, A., Sadeghian, A., Alahi, A., & Savarese, S. (2016). Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes. European Conference on Computer Vision (ECCV).
• Rodrigues, M., Gest, G., McGordon, A., & Marco, J. (2017). Adaptive behaviour selection for autonomous vehicle through naturalistic speed planning. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 1-7.
• Rodríguez, J. I. (2014). Cómo circular por una glorieta. Tráfico y seguridad Vial, Septiembre-Octubre(228), 28-30.
• Rosebrock, A. (2015). Zero-Parameter, Automatic Canny Edge Detection with Python and OpenCV. Retrieved 01 16, 2022, from https://www.pyimagesearch.com/2015/04/06/zero-parameter-automatic-canny-edge-detection-with-python-and-opencv/
• scikit-image development team. (n.d.). Circular and Elliptical Hough Transforms. Retrieved 01 16, 2022, from https://scikit-image.org/docs/stable/auto_examples/edges/plot_circular_elliptical_hough_transform.html
• Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & Lecun, Y. (2013). Over Feat: Integrated Recognition, Localization and Detection using Convolutional Networks. arXiv preprint arXiv:1312.6229.
• Shehata, A., Mohammad, S., Abdallah, M., & Ragab, M. (n.d.). A survey on Hough transform, theory, techniques and applications. arXiv 2015, arXiv:1502.02160.
• Shen, J., Liu, N., & Sun, H. (2021). Vehicle detection in aerial images based on lightweight deep convolutional network. IET Image Processing, 15(2), 479-491.
• Silva, A., Vasconcelos, A., & Santos, S. (2014). Moving from Conventional Roundabouts to Turbo-Roundabouts. Procedia - Social and Behavioral Sciences, 111, 137-146.
• Soleimani, A., & Nasrabadi, N. M. (2018). Convolutional Neural Networks for Aerial Multi-Label Pedestrian Detection. 2018 21st International Conference on Information Fusion (FUSION), 1005-1010.
• Soviany, P., & Ionescu, R. (2018). Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Prediction. In Proceedings of the 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.
• Sreedhar, K., & Panlal, B. (2012). Enhancement of images using morphological transformations. Int. J. Comput. Sci. Inf. Technol(4), 33–50.
• Stuparu, D.-G., Ciobanu, R.-I., & Dobre, C. (2020). Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model. Sensors, 20(22).
• Systems, T.-T. S. (2011). Aimsun (Advanced Interactive Microscopic Simulator for Urban and Non-Urban Networks).
• Tan, L., Huangfu, T., & Wu, L. (2021). Comparison of RetinaNet, SSD, and YOLO v3 for real-time pill identification. BMC Medical Informatics and Decision Making, 21.
• Tang, T., Zhou, S., Deng, Z., Zou, H., & Lei, L. (2020). Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining. Sensors, 20(22).
• The New York Times. (2021, 04 18). 2 Killed in Driverless Tesla Car Crash, Officials Say. Retrieved 12 31, 2021, from https://www.nytimes.com/2021/04/18/business/tesla-fatal-crash-texas.html
• Tollazzi, T., Rencelj, M., & Turnsek, S. (2011). New Type of Roundabout: Roundabout with “Depressed” Lanes for Right Turning – “Flower Roundabout”. PROMET - Traffic&Transportation, 23.
• Troutbeck, R. J. (1989). SR45 evaluating the performance of a roundabout.
• Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference, 1.
• Viola, P., & Jones, M. (2004). Robust real-time face detection. Int. J. Comput. Vis., 57, 137–154.
• World Health Organization. (2021, 06 21). Road traffic injuries. Retrieved 12 31, 2021, from https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
• World Health Organization. (n.d.). Decade of Action for Road Safety 2011–2020. Retrieved 12 30, 2021, from https://www.who.int/publications/i/item/decade-of-action-for-road-safety-2011- 2020
• Xie, Y., & Ji, Q. (2002). A new efficient ellipse detection method. Pattern Recognition, 2002. Proceedings. 16th International Conference on, 2.
• Yap, Y. H., Gibson, H. M., & Waterson, B. J. (2013). An International Review of Roundabout Capacity Modelling. Transport Reviews, 33(5), 593-616.
• Yap, Y. H., Gibson, H., & Waterson, B. (2015). Models of Roundabout Lane Capacity. Transportation engineering journal of ASCE, 141.
• Yu, Y., Gu, T., Guan, H., Li, D., & Jin, S. (2019). Vehicle detection from high-resolution remote sensing imagery using convolutional capsule networks. IEEE Geoscience and Remote Sensing Letters, 16(12), 1894-1898.
• Zhong, J., Lei, T., & Yao, G. (2017). Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks. Sensors, 17(12).
• Zoph, B., Cubuk, E., Ghiasi, G., Lin, T.-Y., Shlens, J., & Le, Q. (2020). Learning Data Augmentation Strategies for Object Detection. Computer Vision - ECCV 2020, 566-583.
• Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object Detection in 20 Years: A Survey. arXiv:1905.05055v2.