Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments
Ilustraciones, fotografías
- Autores:
-
Arias Correa, Alberto Mauricio
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2024
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/86436
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
000 - Ciencias de la computación, información y obras generales::003 - Sistemas
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados
Seguridad vial
Ciclistas - Medidas de seguridad
Accidentes de tránsito - Medidas de seguridad
Redes neurales (Computadores)
Tráfico urbano - Medidas de seguridad
Flujo de tráfico
Transporte - Planificación - Procesamiento de datos
Vulnerable Road User
Intention prediction
Autonomous Driving Environments
Convolutional Neural Networks
Inertial Measurement Unit
LSTM networks
Orientation estimation
Usuarios vulnerables de la vía
Predicción de intención
Redes neuronales convolucionales
Redes LSTM
Estimación de orientación
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
| id |
UNACIONAL2_7b8d8dc90ec6c3e9f67cb7308d8ccde5 |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/86436 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| dc.title.eng.fl_str_mv |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| dc.title.translated.spa.fl_str_mv |
Predicción de intención de ciclista basada en sus ángulos de orientación como usuario vulnerable de la vía en entornos de conducción autónoma |
| title |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| spellingShingle |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados Seguridad vial Ciclistas - Medidas de seguridad Accidentes de tránsito - Medidas de seguridad Redes neurales (Computadores) Tráfico urbano - Medidas de seguridad Flujo de tráfico Transporte - Planificación - Procesamiento de datos Vulnerable Road User Intention prediction Autonomous Driving Environments Convolutional Neural Networks Inertial Measurement Unit LSTM networks Orientation estimation Usuarios vulnerables de la vía Predicción de intención Redes neuronales convolucionales Redes LSTM Estimación de orientación |
| title_short |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| title_full |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| title_fullStr |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| title_full_unstemmed |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| title_sort |
Intention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environments |
| dc.creator.fl_str_mv |
Arias Correa, Alberto Mauricio |
| dc.contributor.advisor.none.fl_str_mv |
Ballesteros Parra, John Robert Branch Bedoya, John William Madrigal González, Carlos Andrés |
| dc.contributor.author.none.fl_str_mv |
Arias Correa, Alberto Mauricio |
| dc.contributor.researchgroup.spa.fl_str_mv |
Gidia: Grupo de Investigación YyDesarrollo en Inteligencia Artificial |
| dc.contributor.orcid.spa.fl_str_mv |
Arias Correa, Alberto Mauricio [0000-0003-0619-235X] |
| dc.contributor.cvlac.spa.fl_str_mv |
https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000268046 |
| dc.contributor.researchgate.spa.fl_str_mv |
https://www.researchgate.net/profile/Mauricio-Correa-8 |
| dc.contributor.googlescholar.spa.fl_str_mv |
https://scholar.google.com/citations?user=0XMAvosAAAAJ&hl=es&oi=ao |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados |
| topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería 000 - Ciencias de la computación, información y obras generales::003 - Sistemas 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionados Seguridad vial Ciclistas - Medidas de seguridad Accidentes de tránsito - Medidas de seguridad Redes neurales (Computadores) Tráfico urbano - Medidas de seguridad Flujo de tráfico Transporte - Planificación - Procesamiento de datos Vulnerable Road User Intention prediction Autonomous Driving Environments Convolutional Neural Networks Inertial Measurement Unit LSTM networks Orientation estimation Usuarios vulnerables de la vía Predicción de intención Redes neuronales convolucionales Redes LSTM Estimación de orientación |
| dc.subject.lemb.none.fl_str_mv |
Seguridad vial Ciclistas - Medidas de seguridad Accidentes de tránsito - Medidas de seguridad Redes neurales (Computadores) Tráfico urbano - Medidas de seguridad Flujo de tráfico Transporte - Planificación - Procesamiento de datos |
| dc.subject.proposal.eng.fl_str_mv |
Vulnerable Road User Intention prediction Autonomous Driving Environments Convolutional Neural Networks Inertial Measurement Unit LSTM networks Orientation estimation |
| dc.subject.proposal.spa.fl_str_mv |
Usuarios vulnerables de la vía Predicción de intención Redes neuronales convolucionales Redes LSTM Estimación de orientación |
| description |
Ilustraciones, fotografías |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-07-12T13:31:19Z |
| dc.date.available.none.fl_str_mv |
2024-07-12T13:31:19Z |
| dc.date.issued.none.fl_str_mv |
2024-05-16 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TD |
| format |
http://purl.org/coar/resource_type/c_db06 |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/86436 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/86436 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.indexed.spa.fl_str_mv |
LaReferencia |
| dc.relation.references.spa.fl_str_mv |
Abadi, A. D., Gu, Y., Goncharenko, I., & Kamijo, S. (2022). Detection of Cyclists’ Crossing Intentions for Autonomous Vehicles. Digest of Technical Papers - IEEE International Conference on Consumer Electronics, 2022-Janua. https://doi.org/10.1109/ICCE53296.2022.9730559 Ahmed, H. E., Sahandabadi, S., Bhawya, & Ahamed, M. J. (2023). Application of MEMS Accelerometers in Dynamic Vibration Monitoring of a Vehicle. Micromachines, 14(5). https://doi.org/10.3390/mi14050923 Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2623–2631. https://doi.org/10.1145/3292500.3330701 Alhajyaseen, W. K. M., Asano, M., & Nakamura, H. (2012). Estimation of left-turning vehicle maneuvers for the assessment of pedestrian safety at intersections. IATSS Research, 36(1), 66–74. https://doi.org/10.1016/j.iatssr.2012.03.002 Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615 Badue, C., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., Jesus, L., Berriel, R., Paixão, T., Mutz, F., Veronese, L., Oliveira-Santos, T., & De Souza, A. F. (2019). Self-Driving Cars: A Survey. http://arxiv.org/abs/1901.04407 Benarbia, T., Kyamakya, K., Al Machot, F., & Kambale, W. V. (2023). Modeling and Simulation of Shared Electric Automated and Connected Mobility Systems with Autonomous Repositioning: Performance Evaluation and Deployment. Sustainability (Switzerland), 15(1). https://doi.org/10.3390/su15010881 Betancur-Vásquez, D., Mejia-Herrera, M., & Botero-Valencia, J. S. (2021). Open source and open hardware mobile robot for developing applications in education and research. https://doi.org/10.17605/OSF.IO/KQ3EW Beyer, L., Hermans, A., & Leibe, B. (2015). Biternion nets: Continuous head pose regression from discrete training labels. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9358, 157–168. https://doi.org/10.1007/978-3-319-24947-6_13 Bieshaar, M., Depping, M., Schneegans, J., & Sick, B. (2019). Starting movement detection of cyclists using smart devices. Proceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018, 313–322. https://doi.org/10.1109/DSAA.2018.00042 Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. http://arxiv.org/abs/2004.10934 Bolaños, Y. H., Rengifo, C. F., Caicedo, P. E., Rodriguez, L. E., & Sierra, W. A. (2020). Electronic system for step width estimation using programmable system-on-chip technology and time of flight cameras. HardwareX, 8. https://doi.org/10.1016/j.ohx.2020.e00126 Botero-Valencia, J. S., Mejia-Herrera, M., & Pearce, J. M. (2022). Design of a low-cost mobile multispectral albedometer with geopositioning and absolute orientation. HardwareX, 12, e00324. https://doi.org/10.1016/j.ohx.2022.e00324 Brohm, T., Haupt, K., & Thiel, R. (2019). Pedestrian Intention and Gesture Classification Using Neural Networks. ATZ Worldwide, 121(4), 26–31. https://doi.org/10.1007/s38311-019-0006-6 Brown, B., & Laurier, E. (2017). The trouble with autopilots: Assisted and autonomous driving on the social road. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 416–429. https://doi.org/10.1145/3025453.3025462 Casas, E., Ramos, L., Bendek, E., & Rivas-Echeverria, F. (2023). Assessing the Effectiveness of YOLO Architectures for Smoke and Wildfire Detection. IEEE Access, 11, 96554–96583. https://doi.org/10.1109/ACCESS.2023.3312217 Dávila García, A. (2024). BOLETÍN ESTADÍSTICO COLOMBIA Fallecidos y Lesionados por Siniestros Viales. https://ansv.gov.co/es/node/11124 DIng, Y., Zhou, X., Bao, H., Li, Y., Hamann, C., Spears, S., & Yuan, Z. (2020). Cycling-Net: A Deep Learning Approach to Predicting Cyclist Behaviors from Geo-Referenced Egocentric Video Data. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 337–346. https://doi.org/10.1145/3397536.3422258 Dung, C. V., Sekiya, H., Hirano, S., Okatani, T., & Miki, C. (2019). A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks. Automation in Construction, 102, 217–229. https://doi.org/10.1016/j.autcon.2019.02.013 Dwijayanti, S., Iqbal, M., & Suprapto, B. Y. (2022). Real-time Implementation of Face Recognition and Emotion Recognition in a Humanoid Robot Using a Convolutional Neural Network. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3200762 Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., Chai, Y., Sapp, B., Qi, C., Zhou, Y., Yang, Z., Chouard, A., Sun, P., Ngiam, J., Vasudevan, V., Mccauley, A., Shlens, J., Anguelov, D., Llc, W., & Brain, G. (n.d.). Large Scale Interactive Motion Forecasting for Autonomous Driving : The WAYMO OPEN MOTION DATASET. Fairley, P. (2017). Self-driving cars have a bicycle problem [News]. IEEE Spectrum, 54(3), 12–13. https://doi.org/10.1109/mspec.2017.7864743 Flohr, F. (2018). Vulnerable Road User Detection and Orientation Estimation for Context-Aware Automated Driving. Gao, H., Su, H., Cai, Y., Wu, R., Hao, Z., Xu, Y., Wu, W., Wang, J., Li, Z., & Kan, Z. (2021). Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections. Science China Information Sciences, 64(7). https://doi.org/10.1007/s11432-020-3071-8 García-Venegas, M., Mercado-Ravell, D. A., Pinedo-Sánchez, L. A., & Carballo-Monsivais, C. A. (2021a). On the safety of vulnerable road users by cyclist detection and tracking. Machine Vision and Applications, 32(5), 1–16. https://doi.org/10.1007/s00138-021-01231-4 Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013). Vision meets robotics: The KITTI dataset. International Journal of Robotics Research, 32(11), 1231–1237. https://doi.org/10.1177/0278364913491297 Gillani, I. S., Munawar, M. R., Talha, M., Azhar, S., Mashkoor, Y., uddin, M. S., & Zafar, U. (2022). Yolov5, Yolo-x, Yolo-r, Yolov7 Performance Comparison: A Survey. 17–28. https://doi.org/10.5121/csit.2022.121602 Girinath, N., Ganesh Babu, C., Chandru, Surendar, R., Sabarish, V., & Abhirooban, T. (2022). Arduino nano based Smart Glasses. Proceedings - 2022 6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022, 118–120. https://doi.org/10.1109/ICICCS53718.2022.9788117 Girshick, R. (2015). Fast R-CNN. http://arxiv.org/abs/1504.08083 Goldhammer, M., Kohler, S., Zernetsch, S., Doll, K., Sick, B., & Dietmayer, K. (2019). Intentions of Vulnerable Road Users--Detection and Forecasting by Means of Machine Learning. IEEE Transactions on Intelligent Transportation Systems, 1–11. https://doi.org/10.1109/tits.2019.2923319 He, C., & Saha, P. (2023a). Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People. http://arxiv.org/abs/2312.07571 He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, 2017-Octob, 2980–2988. https://doi.org/10.1109/ICCV.2017.322 Held, P., Steinhauser, D., Kamann, A., Koch, A., Brandmeier, T., & Schwarz, U. T. (n.d.). Micro-doppler extraction of bicycle pedaling movements using automotive radar. Heo, D., Nam, J. Y., & Ko, B. C. (2019). Estimation of pedestrian pose orientation using soft target training based on teacher-student framework. Sensors (Switzerland), 19(5). https://doi.org/10.3390/s19051147 Hochreiter, & Schmidhuber. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. Huang, Z., Wang, J., Pi, L., Song, X., & Yang, L. (2021). LSTM based trajectory prediction model for cyclist utilizing multiple interactions with environment. Pattern Recognition, 112. https://doi.org/10.1016/j.patcog.2020.107800 Hubert, A., Zernetsch, S., Doll, K., & Sick, B. (2017). Cyclists’ starting behavior at intersections. IEEE Intelligent Vehicles Symposium, Proceedings, Iv, 1071–1077. https://doi.org/10.1109/IVS.2017.7995856 ITF (IRTAD). OECD Publishing. (2023). Road Safety Annual Report 2023. https://www.itf-oecd.org/road-safety-annual-report-2023 Kooij, J. F. P., Flohr, F., Pool, E. A. I., & Gavrila, D. M. (2019). Context-Based Path Prediction for Targets with Switching Dynamics. International Journal of Computer Vision, 127(3), 239–262. https://doi.org/10.1007/s11263-018-1104-4 Kress, V., Jung, J., Zernetsch, S., Doll, K., & Sick, B. (2019a). Pose Based Start Intention Detection of Cyclists. 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, 2381–2386. https://doi.org/10.1109/ITSC.2019.8917215 Kress, V., Jung, J., Zernetsch, S., Doll, K., & Sick, B. (2019b). Start intention detection of cyclists using an lstm network. Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft Fur Informatik (GI), 295, 219–228. https://doi.org/10.18420/inf2019_ws25 Lan, W., Dang, J., Wang, Y., & Wang, S. (2018). Pedestrian detection based on yolo network model. Proceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018, 1547–1551. https://doi.org/10.1109/ICMA.2018.8484698 Lee, J., Lee, M. S., Jang, M., Lim, J. M., & Lee, J. (2022). Comparison of Arduino Nano and Due processors for time-based data acquisition for low-cost mobile radiation detection system. Journal of Instrumentation, 17(3). https://doi.org/10.1088/1748-0221/17/03/P03015 Lei, T., Mohamed, A. A., & Claudel, C. (2018). An IMU-based traffic and road condition monitoring system. HardwareX, 4, e00045. https://doi.org/10.1016/j.ohx.2018.e00045 Lewandowski, B., Seichter, D., Wengefeld, T., Pfennig, L., Drumm, H., & Gross, H. M. (2019). Deep orientation: Fast and Robust Upper Body orientation Estimation for Mobile Robotic Applications. IEEE International Conference on Intelligent Robots and Systems, 2(03), 441–448. https://doi.org/10.1109/IROS40897.2019.8968506 Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., & Wei, X. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. http://arxiv.org/abs/2209.02976 Li, X., Flohr, F., Yang, Y., Xiong, H., Braun, M., Pan, S., Li, K., & Gavrila, D. M. (2016). A new benchmark for vision-based cyclist detection. IEEE Intelligent Vehicles Symposium, Proceedings, 2016-Augus(Iv), 1028–1033. https://doi.org/10.1109/IVS.2016.7535515 Li, X., Li, L., Flohr, F., Wang, J., Xiong, H., Bernhard, M., Pan, S., Gavrila, D. M., & Li, K. (2017). A unified framework for concurrent pedestrian and cyclist detection. IEEE Transactions on Intelligent Transportation Systems, 18(2), 269–281. https://doi.org/10.1109/TITS.2016.2567418 Lin, Y., Wang, P., & Ma, M. (2017). Intelligent Transportation System(ITS): Concept, Challenge and Opportunity. Proceedings - 3rd IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2017, 3rd IEEE International Conference on High Performance and Smart Computing, HPSC 2017 and 2nd IEEE International Conference on Intelligent Data and Securit, 167–172. https://doi.org/10.1109/BigDataSecurity.2017.50 Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2015). SSD: Single Shot MultiBox Detector. https://doi.org/10.1007/978-3-319-46448-0_2 Liu, Y., Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Tan, K. C. (2023). A Survey on Evolutionary Neural Architecture Search. IEEE Transactions on Neural Networks and Learning Systems, 34(2), 550–570. https://doi.org/10.1109/TNNLS.2021.3100554 Liu, Z., Wang, Y., Wu, B., Cui, C., Guo, Y., & Yan, C. (2019). A critical review of fused deposition modeling 3D printing technology in manufacturing polylactic acid parts. In International Journal of Advanced Manufacturing Technology (Vol. 102, Issues 9–12, pp. 2877–2889). Springer London. https://doi.org/10.1007/s00170-019-03332-x Mandal, S., Biswas, S., Balas, V. E., Shaw, R. N., & Ghosh, A. (2020). Motion Prediction for Autonomous Vehicles from Lyft Dataset using Deep Learning. 2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020, 768–773. https://doi.org/10.1109/ICCCA49541.2020.9250790 Mannion, P. (2019a). Vulnerable road user detection: state-of-the-art and open challenges. 1–5. http://arxiv.org/abs/1902.03601 Masalov, A., Matrenin, P., Ota, J., Wirth, F., Stiller, C., Corbet, H., & Lee, E. (2019). Specialized cyclist detection dataset: Challenging real-world computer vision dataset for cyclist detection using a monocular RGB camera. IEEE Intelligent Vehicles Symposium, Proceedings, 2019-June(Iv), 114–118. https://doi.org/10.1109/IVS.2019.8813814 Mathanlal, T., Vakkada Ramachandran, A., Zorzano, M. P., & Martin-Torres, J. (2021). PACKMAN – A portable instrument to investigate space weather. HardwareX, 9. https://doi.org/10.1016/j.ohx.2020.e00169 Morimoto, A. (2019). Traffic and Safety Sciences: Interdisciplinary Wisdom of IATSS (Fisrt, Vol. 53, Issue 9). The Japan Times. https://www.iatss.or.jp/common/pdf/en/publication/commemorative-publication/iatss40.pdf Murphey, Y. L., Liu, C., Tayyab, M., & Narayan, D. (2018). Accurate pedestrian path prediction using neural networks. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018-Janua, 1–7. https://doi.org/10.1109/SSCI.2017.8285398 Oehlberg, L. (2020). Autonomous Vehicle-Cyclist Interaction : Peril and Promise. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1–12. Olah, C. (2025). Understanding LSTM Networks. URL: https://colah.github.io/posts/2015-08-Understanding-LSTMs/ Padilla, R., Netto, S. L., & da Silva, E. (2020). A survey on performance metrics for object-detection algorithms. (IEEE IWSSIP2020). Pan, D., Han, Y., Jin, Q., Kan, J., Huang, H., Mizuno, K., & Thomson, R. (2023). Probabilistic Prediction of Collisions between Cyclists and Vehicles Based on Uncertainty of Cyclists’ Movements. Transportation Research Record, 2677(3), 1151–1164. https://doi.org/10.1177/03611981221121270 Pool, E. A. I., Kooij, J. F. P., & Gavrila, D. M. (2017). Using road topology to improve cyclist path prediction. IEEE Intelligent Vehicles Symposium, Proceedings, Iv, 289–296. https://doi.org/10.1109/IVS.2017.7995734 Pool, E. A. I., Kooij, J. F. P., & Gavrila, D. M. (2019). Context-based cyclist path prediction using Recurrent Neural Networks. IEEE Intelligent Vehicles Symposium, Proceedings, 2019-June(Iv), 824–830. https://doi.org/10.1109/IVS.2019.8813889 Prokudin, S., Gehler, P., & Nowozin, S. (2018). Deep Directional Statistics: Pose Estimation with Uncertainty Quantification. http://arxiv.org/abs/1805.03430 Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection. http://arxiv.org/abs/1506.02640 Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, Faster, Stronger. http://pjreddie.com/yolo9000/ Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. http://arxiv.org/abs/1804.02767 Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. http://arxiv.org/abs/1506.01497 SAE. (2018). Taxonomy and definitions for terms related to driving automation systems for on-road moto vehicles. In Technical Report SAE International. Saini, M., & Susan, S. (2023). VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer Dataset. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(1), 752–762. https://doi.org/10.1109/TCBB.2022.3163277 Saleh, K., Hossny, M., Hossny, A., & Nahavandi, S. (2018). Cyclist detection in LIDAR scans using faster R-CNN and synthetic depth images. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018-March, 1–6. https://doi.org/10.1109/ITSC.2017.8317599 Saleh, K., Hossny, M., Nahavandi, S. (2018). Cyclist Trajectory Prediction Using Bidirectional Recurrent Neural Networks. In Australasian Joint Conference on Artificial Intelligence (Vol. 11320, Issue 61773242, pp. 284–295). Springer International Publishing. https://doi.org/10.1007/978-3-030-03991-2 Saun, T. J., & Grantcharov, T. P. (2021). Design and validation of an inertial measurement unit (IMU)-based sensor for capturing camera movement in the operating room. HardwareX, 9, e00179. https://doi.org/10.1016/j.ohx.2021.e00179 Schieben, A., Wilbrink, M., Kettwich, C., Madigan, R., Louw, T., & Merat, N. (2019). Designing the interaction of automated vehicles with other traffic participants: design considerations based on human needs and expectations. Cognition, Technology and Work, 21(1), 69–85. https://doi.org/10.1007/s10111-018-0521-z Schneemann, F., & Heinemann, P. (2016). Context-based detection of pedestrian crossing intention for autonomous driving in urban environments. IEEE International Conference on Intelligent Robots and Systems, 2016-Novem, 2243–2248. https://doi.org/10.1109/IROS.2016.7759351 SENSORTEC-BOSCH. (2014). BNO055 Intelligent 9-axis absolute orientation sensor. https://www.bosch-sensortec.com/products/smart-sensors/bno055/ Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–14. Sohan, M., Sai Ram, T., & Rami Reddy, Ch. V. (2024). A Review on YOLOv8 and Its Advancements (pp. 529–545). https://doi.org/10.1007/978-981-99-7962-2_39 Stayton, E., & Stilgoe, J. (2020). It’s Time to Rethink Levels of Automation for Self-Driving Vehicles. SSRN Electronic Journal, 2019(2016), 391–397. https://doi.org/10.2139/ssrn.3579386 Suwandi, B., Kitasuka, T., & Aritsugi, M. (2019). Vehicle vibration error compensation on IMU-accelerometer sensor using adaptive filter and low-pass filter approaches. Journal of Information Processing, 27, 33–40. https://doi.org/10.2197/ipsjjip.27.33 Talpaert, V., Sobh, I., Ravi Kiran, B., Mannion, P., Yogamani, S., El-Sallab, A., & Perez, P. (2019). Exploring Applications of Deep Reinforcement Learning for Real-world Autonomous Driving Systems. VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 5, 564–572. https://doi.org/10.5220/0007520305640572 Tan, M., & Le, Q. V. (2021). EfficientNetV2: Smaller Models and Faster Training. http://arxiv.org/abs/2104.00298 Teng, R. (2022). Prevention Detection for Cyclists based on Faster R-CNN. Proceedings - 2022 International Conference on Networks, Communications and Information Technology, CNCIT 2022, 142–148. https://doi.org/10.1109/CNCIT56797.2022.00030 Terven, J., & Cordova-Esparza, D. (2023). A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. https://doi.org/10.3390/make5040083 Thrun, S. (2010). Toward robotic cars. Communications of the ACM, 53(4), 99–106. https://doi.org/10.1145/1721654.1721679 Toy, I., Durdu, A., & Yusefi, A. (2022). Improved Dead Reckoning Localization using IMU Sensor. 2022 15th International Symposium on Electronics and Telecommunications, ISETC 2022 - Conference Proceedings. https://doi.org/10.1109/ISETC56213.2022.10010239 Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Wang, K., & Zhou, W. (2019). Pedestrian and cyclist detection based on deep neural network fast R-CNN. International Journal of Advanced Robotic Systems, 16(2). https://doi.org/10.1177/1729881419829651 Wang, L., Schmidt, B., & Nee, A. Y. C. (2013). Vision-guided active collision avoidance for human-robot collaborations. Manufacturing Letters, 1(1), 5–8. https://doi.org/https://doi.org/10.1016/j.mfglet.2013.08.001 Wells, J. (2018). A SENSOR BASED APPROACH TO ANALYZING MOTION IN MEDICAL APPLICATIONS: AV FISTULA CANNULATION AND RETT SYNDROME. https://doi.org/10.13140/RG.2.2.29733.93929 Wengefeld, T., Lewandowski, B., Seichter, D., Pfennig, L., & Gross, H. M. (2019). Real-time person orientation estimation using colored pointclouds. 2019 European Conference on Mobile Robots, ECMR 2019 - Proceedings, 2(03). https://doi.org/10.1109/ECMR.2019.8870914 Westerhuis, F., & De Waard, D. (2017). Reading cyclist intentions: Can a lead cyclist’s behaviour be predicted? Accident Analysis and Prevention, 105, 146–155. https://doi.org/10.1016/j.aap.2016.06.026 Wickramanayake, S., Li, M., & Hsu, L. W. (n.d.). Explanation-based Data Augmentation for Image Classification. World Health Organization. (2023). Global status report on road safety 2023. https://www.who.int/publications/i/item/9789240086517 Xia, K., Lv, Z., Zhou, C., Gu, G., Zhao, Z., Liu, K., & Li, Z. (2023). Mixed Receptive Fields Augmented YOLO with Multi-Path Spatial Pyramid Pooling for Steel Surface Defect Detection. Sensors, 23(11). https://doi.org/10.3390/s23115114 Ximena González-Cely, A., Callejas-Cuervo, M., & Bastos-Filho, T. (2022). Wheelchair prototype controlled by position, speed and orientation using head movement. https://doi.org/10.17632/ys9s9pgvbg.2 Xu, Y., Liang, X., Dong, X., & Chen, W. (2019). Intelligent Transportation System and Future of Road Safety. Proceedings - 4th IEEE International Conference on Smart Cloud, SmartCloud 2019 and 3rd International Symposium on Reinforcement Learning, ISRL 2019, 209–214. https://doi.org/10.1109/SmartCloud.2019.00043 Yasir, M., Zhan, L., Liu, S., Wan, J., Hossain, M. S., Isiacik Colak, A. T., Liu, M., Islam, Q. U., Raza Mehdi, S., & Yang, Q. (2023). Instance segmentation ship detection based on improved Yolov7 using complex background SAR images. Frontiers in Marine Science, 10. https://doi.org/10.3389/fmars.2023.1113669 Zernetsch, S., Kohnen, S., Goldhammer, M., Doll, K., & Sick, B. (2016). Trajectory prediction of cyclists using a physical model and an artificial neural network. IEEE Intelligent Vehicles Symposium, Proceedings, 2016-Augus(Iv), 833–838. https://doi.org/10.1109/IVS.2016.7535484 Zernetsch, S., Kress, V., Sick, B., & Doll, K. (2018). Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network. IEEE Intelligent Vehicles Symposium, Proceedings, 2018-June(Iv), 2036–2041. https://doi.org/10.1109/IVS.2018.8500428 Shijie, J., Ping, W., Peiyi, J., & Siping, H. (2017, October). Research on data augmentation for image classification based on convolution neural networks. In 2017 Chinese automation congress (CAC) (pp. 4165-4170). IEEE. |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Reconocimiento 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Reconocimiento 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
83 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Medellín - Minas - Doctorado en Ingeniería - Sistemas |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Minas |
| dc.publisher.place.spa.fl_str_mv |
Medellín, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Medellín |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/86436/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/86436/2/71726659.2024.pdf |
| bitstream.checksum.fl_str_mv |
eb34b1cf90b7e1103fc9dfd26be24b4a bbe0815df79097bd56e55827b4d611e0 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1806886221143605248 |
| spelling |
Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ballesteros Parra, John Robert8b10cddb5010474c877da0b0b9ef76faBranch Bedoya, John Williamf42af09c4155d70bd93c48fba945c3b0Madrigal González, Carlos Andrés1e7e58a58f46fa6d897830622b797e8fArias Correa, Alberto Mauricioae316c2dbbd6cac0d8fb533f6b50768eGidia: Grupo de Investigación YyDesarrollo en Inteligencia ArtificialArias Correa, Alberto Mauricio [0000-0003-0619-235X]https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000268046https://www.researchgate.net/profile/Mauricio-Correa-8https://scholar.google.com/citations?user=0XMAvosAAAAJ&hl=es&oi=ao2024-07-12T13:31:19Z2024-07-12T13:31:19Z2024-05-16https://repositorio.unal.edu.co/handle/unal/86436Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones, fotografíasTraffic accidents are currently the eighth leading cause of death, according to the World Health Organization (WHO). Of these deaths, 71% are vulnerable road users (VRUs), with cyclists accounting for 3%. In an environment where autonomous vehicles (AVs) are the most prominent non-vulnerable road actors, VRUS must be effectively and swiftly detected by these AVs. This task remains an open challenge, as cyclists exhibit highly complex movement patterns, and occlusions and lighting issues in urban roads hinder their detection. In this doctoral thesis, we propose predicting the intentions of cyclists on urban roads by estimating both their orientation and inclination during movement near AVs. Due to the lack of hardware and software for the data acquisition systems, containing images associated with cyclists' orientation angles, a system was designed, and a dataset called Cyclops was compiled. This dataset was then used to train an effective cyclist detector using the YOLOv8 architecture. A refined subset of the dataset enabled the training of a model based on modified VGG16 for angular regression and another with similar features based on EfficientNetV2-s. Both models showed better cyclist orientation estimation results than those currently found in the state-of-the-art. Finally, we trained an LSTM network to predict two subsequent periods of angular change (for orientation and inclination) from six previous states, maintaining a prediction sequence and achieving the proposed objective. (Tomado de la fuente)Las muertes por accidentes de tránsito son actualmente la octava causa de muerte según la Organización Mundial de la Salud (OMS). El 71% de esas muertes corresponde a usuarios vulnerables de la vía (VRU), en particular el 3% son ciclistas. En un entorno en el cual los vehículos autónomos (AV) son los actores viales no vulnerables de mayor presencia, será de gran importancia que los VRU sean detectados por dichos AV de forma efectiva y en el menor tiempo posible. Esta tarea aún es un desafío abierto, debido a que los ciclistas tienen patrones de movimiento altamente complejos y su detección se ve afectada por oclusiones y problemas asociados a la iluminación cuando se desplazan sobre vías urbana. En esta tesis doctoral, se propone predecir la intención de los ciclistas en vías urbanas a partir de la estimación tanto de su orientación como de su inclinación durante el movimiento en cercanías de AVs. Debido a la falta de datasets que contengan imágenes asociadas a ángulos de orientación de ciclistas, se diseñó un sistema y se construyó un dataset denominado Cyclops. Posteriormente el dataset fue utilizado para entrenar un detector de ciclistas efectivo utilizando la arquitectura YOLOv8. Un subconjunto depurado del dataset permitió entrenar un modelo basado en VGG16 modificado para regresión angular y otro con las mismas características, pero basado en EfficientNetV2-s. Ambos modelos presentaron resultados de estimación de orientación de ciclistas mejores a los actualmente encontrados en el estado del arte. Finalmente se entrenó una red LSTM que permitía para predecir dos periodos posteriores de cambio angular (para orientación e inclinación) a partir de seis estados anteriores y mantener una secuencia de predicción, logrando así el objetivo propuesto.DoctoradoDoctor en IngenieríaIngeniería De Sistemas E Informática.Sede Medellín83 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - SistemasFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería000 - Ciencias de la computación, información y obras generales::003 - Sistemas000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores600 - Tecnología (Ciencias aplicadas)::607 - Educación, investigación, temas relacionadosSeguridad vialCiclistas - Medidas de seguridadAccidentes de tránsito - Medidas de seguridadRedes neurales (Computadores)Tráfico urbano - Medidas de seguridadFlujo de tráficoTransporte - Planificación - Procesamiento de datosVulnerable Road UserIntention predictionAutonomous Driving EnvironmentsConvolutional Neural NetworksInertial Measurement UnitLSTM networksOrientation estimationUsuarios vulnerables de la víaPredicción de intenciónRedes neuronales convolucionalesRedes LSTMEstimación de orientaciónIntention prediction of cyclist based on their orientation angles as vulnerable road users in autonomous driving environmentsPredicción de intención de ciclista basada en sus ángulos de orientación como usuario vulnerable de la vía en entornos de conducción autónomaTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDLaReferenciaAbadi, A. D., Gu, Y., Goncharenko, I., & Kamijo, S. (2022). Detection of Cyclists’ Crossing Intentions for Autonomous Vehicles. Digest of Technical Papers - IEEE International Conference on Consumer Electronics, 2022-Janua. https://doi.org/10.1109/ICCE53296.2022.9730559Ahmed, H. E., Sahandabadi, S., Bhawya, & Ahamed, M. J. (2023). Application of MEMS Accelerometers in Dynamic Vibration Monitoring of a Vehicle. Micromachines, 14(5). https://doi.org/10.3390/mi14050923Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2623–2631. https://doi.org/10.1145/3292500.3330701Alhajyaseen, W. K. M., Asano, M., & Nakamura, H. (2012). Estimation of left-turning vehicle maneuvers for the assessment of pedestrian safety at intersections. IATSS Research, 36(1), 66–74. https://doi.org/10.1016/j.iatssr.2012.03.002Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615Badue, C., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., Jesus, L., Berriel, R., Paixão, T., Mutz, F., Veronese, L., Oliveira-Santos, T., & De Souza, A. F. (2019). Self-Driving Cars: A Survey. http://arxiv.org/abs/1901.04407Benarbia, T., Kyamakya, K., Al Machot, F., & Kambale, W. V. (2023). Modeling and Simulation of Shared Electric Automated and Connected Mobility Systems with Autonomous Repositioning: Performance Evaluation and Deployment. Sustainability (Switzerland), 15(1). https://doi.org/10.3390/su15010881Betancur-Vásquez, D., Mejia-Herrera, M., & Botero-Valencia, J. S. (2021). Open source and open hardware mobile robot for developing applications in education and research. https://doi.org/10.17605/OSF.IO/KQ3EWBeyer, L., Hermans, A., & Leibe, B. (2015). Biternion nets: Continuous head pose regression from discrete training labels. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9358, 157–168. https://doi.org/10.1007/978-3-319-24947-6_13Bieshaar, M., Depping, M., Schneegans, J., & Sick, B. (2019). Starting movement detection of cyclists using smart devices. Proceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018, 313–322. https://doi.org/10.1109/DSAA.2018.00042Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. http://arxiv.org/abs/2004.10934Bolaños, Y. H., Rengifo, C. F., Caicedo, P. E., Rodriguez, L. E., & Sierra, W. A. (2020). Electronic system for step width estimation using programmable system-on-chip technology and time of flight cameras. HardwareX, 8. https://doi.org/10.1016/j.ohx.2020.e00126Botero-Valencia, J. S., Mejia-Herrera, M., & Pearce, J. M. (2022). Design of a low-cost mobile multispectral albedometer with geopositioning and absolute orientation. HardwareX, 12, e00324. https://doi.org/10.1016/j.ohx.2022.e00324Brohm, T., Haupt, K., & Thiel, R. (2019). Pedestrian Intention and Gesture Classification Using Neural Networks. ATZ Worldwide, 121(4), 26–31. https://doi.org/10.1007/s38311-019-0006-6Brown, B., & Laurier, E. (2017). The trouble with autopilots: Assisted and autonomous driving on the social road. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 416–429. https://doi.org/10.1145/3025453.3025462Casas, E., Ramos, L., Bendek, E., & Rivas-Echeverria, F. (2023). Assessing the Effectiveness of YOLO Architectures for Smoke and Wildfire Detection. IEEE Access, 11, 96554–96583. https://doi.org/10.1109/ACCESS.2023.3312217Dávila García, A. (2024). BOLETÍN ESTADÍSTICO COLOMBIA Fallecidos y Lesionados por Siniestros Viales. https://ansv.gov.co/es/node/11124DIng, Y., Zhou, X., Bao, H., Li, Y., Hamann, C., Spears, S., & Yuan, Z. (2020). Cycling-Net: A Deep Learning Approach to Predicting Cyclist Behaviors from Geo-Referenced Egocentric Video Data. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 337–346. https://doi.org/10.1145/3397536.3422258Dung, C. V., Sekiya, H., Hirano, S., Okatani, T., & Miki, C. (2019). A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks. Automation in Construction, 102, 217–229. https://doi.org/10.1016/j.autcon.2019.02.013Dwijayanti, S., Iqbal, M., & Suprapto, B. Y. (2022). Real-time Implementation of Face Recognition and Emotion Recognition in a Humanoid Robot Using a Convolutional Neural Network. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3200762Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., Chai, Y., Sapp, B., Qi, C., Zhou, Y., Yang, Z., Chouard, A., Sun, P., Ngiam, J., Vasudevan, V., Mccauley, A., Shlens, J., Anguelov, D., Llc, W., & Brain, G. (n.d.). Large Scale Interactive Motion Forecasting for Autonomous Driving : The WAYMO OPEN MOTION DATASET.Fairley, P. (2017). Self-driving cars have a bicycle problem [News]. IEEE Spectrum, 54(3), 12–13. https://doi.org/10.1109/mspec.2017.7864743Flohr, F. (2018). Vulnerable Road User Detection and Orientation Estimation for Context-Aware Automated Driving.Gao, H., Su, H., Cai, Y., Wu, R., Hao, Z., Xu, Y., Wu, W., Wang, J., Li, Z., & Kan, Z. (2021). Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections. Science China Information Sciences, 64(7). https://doi.org/10.1007/s11432-020-3071-8García-Venegas, M., Mercado-Ravell, D. A., Pinedo-Sánchez, L. A., & Carballo-Monsivais, C. A. (2021a). On the safety of vulnerable road users by cyclist detection and tracking. Machine Vision and Applications, 32(5), 1–16. https://doi.org/10.1007/s00138-021-01231-4Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013). Vision meets robotics: The KITTI dataset. International Journal of Robotics Research, 32(11), 1231–1237. https://doi.org/10.1177/0278364913491297Gillani, I. S., Munawar, M. R., Talha, M., Azhar, S., Mashkoor, Y., uddin, M. S., & Zafar, U. (2022). Yolov5, Yolo-x, Yolo-r, Yolov7 Performance Comparison: A Survey. 17–28. https://doi.org/10.5121/csit.2022.121602Girinath, N., Ganesh Babu, C., Chandru, Surendar, R., Sabarish, V., & Abhirooban, T. (2022). Arduino nano based Smart Glasses. Proceedings - 2022 6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022, 118–120. https://doi.org/10.1109/ICICCS53718.2022.9788117Girshick, R. (2015). Fast R-CNN. http://arxiv.org/abs/1504.08083Goldhammer, M., Kohler, S., Zernetsch, S., Doll, K., Sick, B., & Dietmayer, K. (2019). Intentions of Vulnerable Road Users--Detection and Forecasting by Means of Machine Learning. IEEE Transactions on Intelligent Transportation Systems, 1–11. https://doi.org/10.1109/tits.2019.2923319He, C., & Saha, P. (2023a). Investigating YOLO Models Towards Outdoor Obstacle Detection For Visually Impaired People. http://arxiv.org/abs/2312.07571He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, 2017-Octob, 2980–2988. https://doi.org/10.1109/ICCV.2017.322Held, P., Steinhauser, D., Kamann, A., Koch, A., Brandmeier, T., & Schwarz, U. T. (n.d.). Micro-doppler extraction of bicycle pedaling movements using automotive radar.Heo, D., Nam, J. Y., & Ko, B. C. (2019). Estimation of pedestrian pose orientation using soft target training based on teacher-student framework. Sensors (Switzerland), 19(5). https://doi.org/10.3390/s19051147Hochreiter, & Schmidhuber. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.Huang, Z., Wang, J., Pi, L., Song, X., & Yang, L. (2021). LSTM based trajectory prediction model for cyclist utilizing multiple interactions with environment. Pattern Recognition, 112. https://doi.org/10.1016/j.patcog.2020.107800Hubert, A., Zernetsch, S., Doll, K., & Sick, B. (2017). Cyclists’ starting behavior at intersections. IEEE Intelligent Vehicles Symposium, Proceedings, Iv, 1071–1077. https://doi.org/10.1109/IVS.2017.7995856ITF (IRTAD). OECD Publishing. (2023). Road Safety Annual Report 2023. https://www.itf-oecd.org/road-safety-annual-report-2023Kooij, J. F. P., Flohr, F., Pool, E. A. I., & Gavrila, D. M. (2019). Context-Based Path Prediction for Targets with Switching Dynamics. International Journal of Computer Vision, 127(3), 239–262. https://doi.org/10.1007/s11263-018-1104-4Kress, V., Jung, J., Zernetsch, S., Doll, K., & Sick, B. (2019a). Pose Based Start Intention Detection of Cyclists. 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, 2381–2386. https://doi.org/10.1109/ITSC.2019.8917215Kress, V., Jung, J., Zernetsch, S., Doll, K., & Sick, B. (2019b). Start intention detection of cyclists using an lstm network. Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft Fur Informatik (GI), 295, 219–228. https://doi.org/10.18420/inf2019_ws25Lan, W., Dang, J., Wang, Y., & Wang, S. (2018). Pedestrian detection based on yolo network model. Proceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018, 1547–1551. https://doi.org/10.1109/ICMA.2018.8484698Lee, J., Lee, M. S., Jang, M., Lim, J. M., & Lee, J. (2022). Comparison of Arduino Nano and Due processors for time-based data acquisition for low-cost mobile radiation detection system. Journal of Instrumentation, 17(3). https://doi.org/10.1088/1748-0221/17/03/P03015Lei, T., Mohamed, A. A., & Claudel, C. (2018). An IMU-based traffic and road condition monitoring system. HardwareX, 4, e00045. https://doi.org/10.1016/j.ohx.2018.e00045Lewandowski, B., Seichter, D., Wengefeld, T., Pfennig, L., Drumm, H., & Gross, H. M. (2019). Deep orientation: Fast and Robust Upper Body orientation Estimation for Mobile Robotic Applications. IEEE International Conference on Intelligent Robots and Systems, 2(03), 441–448. https://doi.org/10.1109/IROS40897.2019.8968506Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., Nie, W., Li, Y., Zhang, B., Liang, Y., Zhou, L., Xu, X., Chu, X., Wei, X., & Wei, X. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. http://arxiv.org/abs/2209.02976Li, X., Flohr, F., Yang, Y., Xiong, H., Braun, M., Pan, S., Li, K., & Gavrila, D. M. (2016). A new benchmark for vision-based cyclist detection. IEEE Intelligent Vehicles Symposium, Proceedings, 2016-Augus(Iv), 1028–1033. https://doi.org/10.1109/IVS.2016.7535515Li, X., Li, L., Flohr, F., Wang, J., Xiong, H., Bernhard, M., Pan, S., Gavrila, D. M., & Li, K. (2017). A unified framework for concurrent pedestrian and cyclist detection. IEEE Transactions on Intelligent Transportation Systems, 18(2), 269–281. https://doi.org/10.1109/TITS.2016.2567418Lin, Y., Wang, P., & Ma, M. (2017). Intelligent Transportation System(ITS): Concept, Challenge and Opportunity. Proceedings - 3rd IEEE International Conference on Big Data Security on Cloud, BigDataSecurity 2017, 3rd IEEE International Conference on High Performance and Smart Computing, HPSC 2017 and 2nd IEEE International Conference on Intelligent Data and Securit, 167–172. https://doi.org/10.1109/BigDataSecurity.2017.50Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2015). SSD: Single Shot MultiBox Detector. https://doi.org/10.1007/978-3-319-46448-0_2Liu, Y., Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Tan, K. C. (2023). A Survey on Evolutionary Neural Architecture Search. IEEE Transactions on Neural Networks and Learning Systems, 34(2), 550–570. https://doi.org/10.1109/TNNLS.2021.3100554Liu, Z., Wang, Y., Wu, B., Cui, C., Guo, Y., & Yan, C. (2019). A critical review of fused deposition modeling 3D printing technology in manufacturing polylactic acid parts. In International Journal of Advanced Manufacturing Technology (Vol. 102, Issues 9–12, pp. 2877–2889). Springer London. https://doi.org/10.1007/s00170-019-03332-xMandal, S., Biswas, S., Balas, V. E., Shaw, R. N., & Ghosh, A. (2020). Motion Prediction for Autonomous Vehicles from Lyft Dataset using Deep Learning. 2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020, 768–773. https://doi.org/10.1109/ICCCA49541.2020.9250790Mannion, P. (2019a). Vulnerable road user detection: state-of-the-art and open challenges. 1–5. http://arxiv.org/abs/1902.03601Masalov, A., Matrenin, P., Ota, J., Wirth, F., Stiller, C., Corbet, H., & Lee, E. (2019). Specialized cyclist detection dataset: Challenging real-world computer vision dataset for cyclist detection using a monocular RGB camera. IEEE Intelligent Vehicles Symposium, Proceedings, 2019-June(Iv), 114–118. https://doi.org/10.1109/IVS.2019.8813814Mathanlal, T., Vakkada Ramachandran, A., Zorzano, M. P., & Martin-Torres, J. (2021). PACKMAN – A portable instrument to investigate space weather. HardwareX, 9. https://doi.org/10.1016/j.ohx.2020.e00169Morimoto, A. (2019). Traffic and Safety Sciences: Interdisciplinary Wisdom of IATSS (Fisrt, Vol. 53, Issue 9). The Japan Times. https://www.iatss.or.jp/common/pdf/en/publication/commemorative-publication/iatss40.pdfMurphey, Y. L., Liu, C., Tayyab, M., & Narayan, D. (2018). Accurate pedestrian path prediction using neural networks. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018-Janua, 1–7. https://doi.org/10.1109/SSCI.2017.8285398Oehlberg, L. (2020). Autonomous Vehicle-Cyclist Interaction : Peril and Promise. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1–12. Olah, C. (2025). Understanding LSTM Networks. URL: https://colah.github.io/posts/2015-08-Understanding-LSTMs/Padilla, R., Netto, S. L., & da Silva, E. (2020). A survey on performance metrics for object-detection algorithms. (IEEE IWSSIP2020).Pan, D., Han, Y., Jin, Q., Kan, J., Huang, H., Mizuno, K., & Thomson, R. (2023). Probabilistic Prediction of Collisions between Cyclists and Vehicles Based on Uncertainty of Cyclists’ Movements. Transportation Research Record, 2677(3), 1151–1164. https://doi.org/10.1177/03611981221121270Pool, E. A. I., Kooij, J. F. P., & Gavrila, D. M. (2017). Using road topology to improve cyclist path prediction. IEEE Intelligent Vehicles Symposium, Proceedings, Iv, 289–296. https://doi.org/10.1109/IVS.2017.7995734Pool, E. A. I., Kooij, J. F. P., & Gavrila, D. M. (2019). Context-based cyclist path prediction using Recurrent Neural Networks. IEEE Intelligent Vehicles Symposium, Proceedings, 2019-June(Iv), 824–830. https://doi.org/10.1109/IVS.2019.8813889Prokudin, S., Gehler, P., & Nowozin, S. (2018). Deep Directional Statistics: Pose Estimation with Uncertainty Quantification. http://arxiv.org/abs/1805.03430Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2015). You Only Look Once: Unified, Real-Time Object Detection. http://arxiv.org/abs/1506.02640Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, Faster, Stronger. http://pjreddie.com/yolo9000/Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. http://arxiv.org/abs/1804.02767Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. http://arxiv.org/abs/1506.01497SAE. (2018). Taxonomy and definitions for terms related to driving automation systems for on-road moto vehicles. In Technical Report SAE International.Saini, M., & Susan, S. (2023). VGGIN-Net: Deep Transfer Network for Imbalanced Breast Cancer Dataset. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(1), 752–762. https://doi.org/10.1109/TCBB.2022.3163277Saleh, K., Hossny, M., Hossny, A., & Nahavandi, S. (2018). Cyclist detection in LIDAR scans using faster R-CNN and synthetic depth images. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018-March, 1–6. https://doi.org/10.1109/ITSC.2017.8317599Saleh, K., Hossny, M., Nahavandi, S. (2018). Cyclist Trajectory Prediction Using Bidirectional Recurrent Neural Networks. In Australasian Joint Conference on Artificial Intelligence (Vol. 11320, Issue 61773242, pp. 284–295). Springer International Publishing. https://doi.org/10.1007/978-3-030-03991-2Saun, T. J., & Grantcharov, T. P. (2021). Design and validation of an inertial measurement unit (IMU)-based sensor for capturing camera movement in the operating room. HardwareX, 9, e00179. https://doi.org/10.1016/j.ohx.2021.e00179Schieben, A., Wilbrink, M., Kettwich, C., Madigan, R., Louw, T., & Merat, N. (2019). Designing the interaction of automated vehicles with other traffic participants: design considerations based on human needs and expectations. Cognition, Technology and Work, 21(1), 69–85. https://doi.org/10.1007/s10111-018-0521-zSchneemann, F., & Heinemann, P. (2016). Context-based detection of pedestrian crossing intention for autonomous driving in urban environments. IEEE International Conference on Intelligent Robots and Systems, 2016-Novem, 2243–2248. https://doi.org/10.1109/IROS.2016.7759351SENSORTEC-BOSCH. (2014). BNO055 Intelligent 9-axis absolute orientation sensor. https://www.bosch-sensortec.com/products/smart-sensors/bno055/Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–14.Sohan, M., Sai Ram, T., & Rami Reddy, Ch. V. (2024). A Review on YOLOv8 and Its Advancements (pp. 529–545). https://doi.org/10.1007/978-981-99-7962-2_39Stayton, E., & Stilgoe, J. (2020). It’s Time to Rethink Levels of Automation for Self-Driving Vehicles. SSRN Electronic Journal, 2019(2016), 391–397. https://doi.org/10.2139/ssrn.3579386Suwandi, B., Kitasuka, T., & Aritsugi, M. (2019). Vehicle vibration error compensation on IMU-accelerometer sensor using adaptive filter and low-pass filter approaches. Journal of Information Processing, 27, 33–40. https://doi.org/10.2197/ipsjjip.27.33Talpaert, V., Sobh, I., Ravi Kiran, B., Mannion, P., Yogamani, S., El-Sallab, A., & Perez, P. (2019). Exploring Applications of Deep Reinforcement Learning for Real-world Autonomous Driving Systems. VISIGRAPP 2019 - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 5, 564–572. https://doi.org/10.5220/0007520305640572Tan, M., & Le, Q. V. (2021). EfficientNetV2: Smaller Models and Faster Training. http://arxiv.org/abs/2104.00298Teng, R. (2022). Prevention Detection for Cyclists based on Faster R-CNN. Proceedings - 2022 International Conference on Networks, Communications and Information Technology, CNCIT 2022, 142–148. https://doi.org/10.1109/CNCIT56797.2022.00030Terven, J., & Cordova-Esparza, D. (2023). A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS. https://doi.org/10.3390/make5040083Thrun, S. (2010). Toward robotic cars. Communications of the ACM, 53(4), 99–106. https://doi.org/10.1145/1721654.1721679Toy, I., Durdu, A., & Yusefi, A. (2022). Improved Dead Reckoning Localization using IMU Sensor. 2022 15th International Symposium on Electronics and Telecommunications, ISETC 2022 - Conference Proceedings. https://doi.org/10.1109/ISETC56213.2022.10010239Wang, C.-Y., Bochkovskiy, A., & Liao, H.-Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.Wang, K., & Zhou, W. (2019). Pedestrian and cyclist detection based on deep neural network fast R-CNN. International Journal of Advanced Robotic Systems, 16(2). https://doi.org/10.1177/1729881419829651Wang, L., Schmidt, B., & Nee, A. Y. C. (2013). Vision-guided active collision avoidance for human-robot collaborations. Manufacturing Letters, 1(1), 5–8. https://doi.org/https://doi.org/10.1016/j.mfglet.2013.08.001Wells, J. (2018). A SENSOR BASED APPROACH TO ANALYZING MOTION IN MEDICAL APPLICATIONS: AV FISTULA CANNULATION AND RETT SYNDROME. https://doi.org/10.13140/RG.2.2.29733.93929Wengefeld, T., Lewandowski, B., Seichter, D., Pfennig, L., & Gross, H. M. (2019). Real-time person orientation estimation using colored pointclouds. 2019 European Conference on Mobile Robots, ECMR 2019 - Proceedings, 2(03). https://doi.org/10.1109/ECMR.2019.8870914Westerhuis, F., & De Waard, D. (2017). Reading cyclist intentions: Can a lead cyclist’s behaviour be predicted? Accident Analysis and Prevention, 105, 146–155. https://doi.org/10.1016/j.aap.2016.06.026Wickramanayake, S., Li, M., & Hsu, L. W. (n.d.). Explanation-based Data Augmentation for Image Classification.World Health Organization. (2023). Global status report on road safety 2023. https://www.who.int/publications/i/item/9789240086517Xia, K., Lv, Z., Zhou, C., Gu, G., Zhao, Z., Liu, K., & Li, Z. (2023). Mixed Receptive Fields Augmented YOLO with Multi-Path Spatial Pyramid Pooling for Steel Surface Defect Detection. Sensors, 23(11). https://doi.org/10.3390/s23115114Ximena González-Cely, A., Callejas-Cuervo, M., & Bastos-Filho, T. (2022). Wheelchair prototype controlled by position, speed and orientation using head movement. https://doi.org/10.17632/ys9s9pgvbg.2Xu, Y., Liang, X., Dong, X., & Chen, W. (2019). Intelligent Transportation System and Future of Road Safety. Proceedings - 4th IEEE International Conference on Smart Cloud, SmartCloud 2019 and 3rd International Symposium on Reinforcement Learning, ISRL 2019, 209–214. https://doi.org/10.1109/SmartCloud.2019.00043Yasir, M., Zhan, L., Liu, S., Wan, J., Hossain, M. S., Isiacik Colak, A. T., Liu, M., Islam, Q. U., Raza Mehdi, S., & Yang, Q. (2023). Instance segmentation ship detection based on improved Yolov7 using complex background SAR images. Frontiers in Marine Science, 10. https://doi.org/10.3389/fmars.2023.1113669Zernetsch, S., Kohnen, S., Goldhammer, M., Doll, K., & Sick, B. (2016). Trajectory prediction of cyclists using a physical model and an artificial neural network. IEEE Intelligent Vehicles Symposium, Proceedings, 2016-Augus(Iv), 833–838. https://doi.org/10.1109/IVS.2016.7535484Zernetsch, S., Kress, V., Sick, B., & Doll, K. (2018). Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network. IEEE Intelligent Vehicles Symposium, Proceedings, 2018-June(Iv), 2036–2041. https://doi.org/10.1109/IVS.2018.8500428Shijie, J., Ping, W., Peiyi, J., & Siping, H. (2017, October). Research on data augmentation for image classification based on convolution neural networks. In 2017 Chinese automation congress (CAC) (pp. 4165-4170). IEEE.EstudiantesInvestigadoresMaestrosPúblico generalResponsables políticosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86436/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL71726659.2024.pdf71726659.2024.pdfTesis de Doctorado en Ingeniería - Sistemasapplication/pdf5436770https://repositorio.unal.edu.co/bitstream/unal/86436/2/71726659.2024.pdfbbe0815df79097bd56e55827b4d611e0MD52unal/86436oai:repositorio.unal.edu.co:unal/864362024-07-12 08:33:49.725Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |