F. Wang, Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications, IEEE Transactions on Intelligent Transportation Systems, vol.11, issue.3, pp.630-638, 2010.

C. Campolo, A. Molinaro, and R. Scopigno, From today's vanets to tomorrow's planning and the bets for the day after, Vehicular Communications, vol.2, issue.3, pp.158-171, 2015.

Q. Mao, F. Hu, and Q. Hao, Deep learning for intelligent wireless networks: A comprehensive survey, IEEE Communications Surveys & Tutorials, vol.20, issue.4, pp.2595-2621, 2018.

C. Zhang, P. Patras, and H. Haddadi, Deep learning in mobile and wireless networking: A survey, 2018.

N. C. Luong, D. T. Hoang, S. Gong, D. Niyato, P. Wang et al., Applications of deep reinforcement learning in communications and networking: A survey, 2018.

K. N. Qureshi and A. H. Abdullah, A survey on intelligent transportation systems, Middle-East Journal of Scientific Research, vol.15, issue.5, pp.629-642, 2013.

B. S.-h.-an, D. Lee, and . Shin, A survey of intelligent transportation systems, 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks, pp.332-337, 2011.

J. Zhang, F. Wang, K. Wang, W. Lin, X. Xu et al., Data-driven intelligent transportation systems: A survey, IEEE Transactions on Intelligent Transportation Systems, vol.12, issue.4, pp.1624-1639, 2011.

S. Sivaraman and M. M. Trivedi, Looking at vehicles on the road: A survey of visionbased vehicle detection, tracking, and behavior analysis, IEEE Transactions on Intelligent Transportation Systems, vol.14, issue.4, pp.1773-1795, 2013.

M. A. Zamil and S. Samarah, Applications of data mining techniques for vehicular ad hoc networks, 2018.

M. Pasin, A. E. Seghrouchni, A. Belbachir, S. M. Peres, and A. A. Brandao, Computational intelligence and adaptation in VANETs: Current research and new perspectives, 2018 International Joint Conference on Neural Networks (IJCNN), pp.1-7, 2018.

L. Liang, H. Ye, and G. Y. Li, Toward intelligent vehicular networks: A machine learning framework, IEEE Internet of Things Journal, vol.6, issue.1, pp.124-135, 2019.

M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks, 2017.

T. E. Bogale, X. Wang, and L. B. Le, Machine intelligence techniques for nextgeneration context-aware wireless networks, 2018.

W. G. Fink, Intelligent transportation systems, IEEE 1995 Microwave and Millimeter-Wave. Monolithic Circuits Symposium. Digest of Papers, p.3, 1995.

J. Barbaresso, G. Cordahi, D. Garcia, C. Hill, A. Jendzejec et al., Usdot's intelligent transportation systems (its) its strategic plan, Tech. Rep, 2014.

S. Mand?uka, M. ?ura, B. Horvat, D. Bi?ani?, and E. Mitsakis, Directives of the european union on intelligent transport systems and their impact on the republic of croatia, Promet-Traffic & Transportation, vol.25, issue.3, pp.273-283, 2013.

B. Williams, Intelligent transport systems standards, 2008.

C. Platform, Platform for the deployment of cooperative intelligent transport systems in the eu (e03188)(c-its platform) final report, DG MOVE-DG Mobility and Transport, 2016.

, 2018, may) Program overview

. Etsi, ETSI TR 102 638 V1.1.1 -Intelligent Transport Systems (ITS) / Vehicular Communications / Basic Set of Applications / Definitions, Tech. Rep, 2009.

, About connected intelligent transportation systems, 2018.

, 2019, may) Connected vehicle applications and supporting documentation

M. Alam, J. Ferreira, and J. Fonseca, Introduction to intelligent transportation systems, Intelligent Transportation Systems, pp.1-17, 2016.

O. Kaiwartya, A. H. Abdullah, Y. Cao, A. Altameem, M. Prasad et al., Internet of vehicles: Motivation, layered architecture, network model, challenges, and future aspects, IEEE Access, vol.4, pp.5356-5373, 2016.

J. Guerrero-ibáñez, S. Zeadally, and J. Contreras-castillo, Sensor technologies for intelligent transportation systems, Sensors, vol.18, issue.4, p.1212, 2018.

U. Hernandez, A. Perallos, N. Sainz, and I. Angulo, Vehicle on board platform: Communications test and prototyping, 2010 IEEE Intelligent Vehicles Symposium, pp.967-972, 2010.

A. Festag, Standards for vehicular communication-from ieee 802.11 p to 5g, e & i Elektrotechnik und Informationstechnik, vol.132, issue.7, pp.409-416, 2015.

R. Mijumbi, J. Serrat, J. Gorricho, N. Bouten, F. D. Turck et al., Network function virtualization: State-ofthe-art and research challenges, IEEE Communications Surveys & Tutorials, vol.18, issue.1, pp.236-262, 2016.

D. Kreutz, F. M. Ramos, P. E. Veríssimo, C. E. Rothenberg, S. Azodolmolky et al., Software-defined networking: A comprehensive survey, Proceedings of the IEEE, vol.103, issue.1, pp.14-76, 2015.

N. F. Sousa, D. A. Perez, R. V. Rosa, M. A. Santos, and C. E. Rothenberg, Network service orchestration: A survey, Computer Communications, pp.69-94, 2019.

S. Pendleton, H. Andersen, X. Du, X. Shen, M. Meghjani et al., Perception, planning, control, and coordination for autonomous vehicles, Machines, vol.5, issue.1, p.6, 2017.

A. E. Sallab, M. Abdou, E. Perot, and S. Yogamani, Deep reinforcement learning framework for autonomous driving, Electronic Imaging, vol.2017, issue.19, pp.70-76, 2017.

D. Gruyer, V. Magnier, K. Hamdi, L. Claussmann, O. Orfila et al., Perception, information processing and modeling: Critical stages for autonomous driving applications, Annual Reviews in Control, vol.44, pp.323-341, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01671375

N. E. Faouzi, H. Leung, and A. Kurian, Data fusion in intelligent transportation systems: Progress and challenges-a survey, Information Fusion, vol.12, issue.1, pp.4-10, 2011.

K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati et al., Robust physical-world attacks on deep learning visual classification, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1625-1634, 2018.

K. Bengler, K. Dietmayer, B. Farber, M. Maurer, C. Stiller et al., Three decades of driver assistance systems: Review and future perspectives, IEEE Intelligent Transportation Systems Magazine, vol.6, issue.4, pp.6-22, 2014.

S. Garcia, J. Luengo, J. A. Sáez, V. Lopez, and F. Herrera, A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning, IEEE Transactions on Knowledge and Data Engineering, vol.25, issue.4, pp.734-750, 2013.

T. Hastie, R. Tibshirani, and J. Friedman, Unsupervised learning, The elements of statistical learning, pp.485-585, 2009.

L. P. Kaelbling, M. L. Littman, and A. W. Moore, Reinforcement learning: A survey, Journal of artificial intelligence research, vol.4, pp.237-285, 1996.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, nature, vol.521, issue.7553, p.436, 2015.

J. A. Suykens and J. Vandewalle, Least squares support vector machine classifiers, vol.9, pp.293-300, 1999.

Y. Freund, R. Schapire, and N. Abe, A short introduction to boosting, Journal-Japanese Society For Artificial Intelligence, vol.14, p.1612, 1999.

G. A. Seber and A. J. Lee, Linear regression analysis, vol.329, 2012.

A. J. Smola and B. Schölkopf, A tutorial on support vector regression, Statistics and computing, vol.14, issue.3, pp.199-222, 2004.

J. M. Keller, M. R. Gray, and J. A. Givens, A fuzzy k-nearest neighbor algorithm, IEEE transactions on systems, man, and cybernetics, issue.4, pp.580-585, 1985.

A. Liaw and M. Wiener, Classification and regression by randomforest, R news, vol.2, issue.3, pp.18-22, 2002.

J. Elith, J. R. Leathwick, and T. Hastie, A working guide to boosted regression trees, Journal of Animal Ecology, vol.77, issue.4, pp.802-813, 2008.

M. Steinbach, G. Karypis, and V. Kumar, A comparison of document clustering techniques, KDD workshop on text mining, vol.400, pp.525-526, 2000.

I. Jolliffe, Principal component analysis, International encyclopedia of statistical science, pp.1094-1096, 2011.

A. Hyvarinen, Survey on independent component analysis, Neural computing surveys, vol.2, issue.4, pp.94-128, 1999.

C. J. Watkins and P. Dayan, Qlearning, Machine Learning, vol.8, pp.279-292, 1992.

S. P. Singh and R. S. Sutton, Reinforcement learning with replacing eligibility traces, Machine learning, vol.22, issue.1-3, pp.123-158, 1996.

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness et al., Human-level control through deep reinforcement learning, Nature, vol.518, issue.7540, p.529, 2015.

R. S. Sutton, D. A. Mcallester, S. P. Singh, and Y. Mansour, Policy gradient methods for reinforcement learning with function approximation, Advances in neural information processing systems, pp.1057-1063, 2000.

D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra et al., Deterministic policy gradient algorithms, ICML, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00938992

A. Attia and S. Dayan, Global overview of imitation learning, 2018.

I. Grondman, L. Busoniu, G. A. Lopes, and R. Babuska, A survey of actor-critic reinforcement learning: Standard and natural policy gradients, Man, and Cybernetics, Part C (Applications and Reviews), vol.42, issue.6, pp.1291-1307, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00756747

V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap et al., Asynchronous methods for deep reinforcement learning, International conference on machine learning, pp.1928-1937, 2016.

T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez et al., Continuous control with deep reinforcement learning, 2015.

P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-gonzalez, V. Zambaldi et al., Relational inductive biases, deep learning, and graph networks, 2018.

J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu et al., Graph neural networks: A review of methods and applications, 2018.

G. E. Hinton, S. Osindero, and Y. Teh, A fast learning algorithm for deep belief nets, Neural computation, vol.18, issue.7, pp.1527-1554, 2006.

G. E. Hinton, A practical guide to training restricted boltzmann machines, Neural networks: Tricks of the trade, pp.599-619, 2012.

M. M. Bejani and M. Ghatee, Convolutional neural network with adaptive regularization to classify driving styles on smartphones, IEEE Transactions on Intelligent Transportation Systems, 2019.

K. Kumar, M. Parida, and V. Katiyar, Short term traffic flow prediction for a non urban highway using artificial neural network, Procedia-Social and Behavioral Sciences, vol.104, pp.755-764, 2013.

J. Lee, D. Jang, and S. Park, Deep learning-based corporate performance prediction model considering technical capability, Sustainability, vol.9, issue.6, p.899, 2017.

R. Barták, M. A. Salido, and F. Rossi, Constraint satisfaction techniques in planning and scheduling, Journal of Intelligent Manufacturing, vol.21, issue.1, pp.5-15, 2010.

I. Arel, D. C. Rose, and T. P. Karnowski, Deep machine learning-a new frontier in artificial intelligence research, IEEE computational intelligence magazine, vol.5, issue.4, pp.13-18, 2010.

S. Maldonado-bascón, S. Lafuente-arroyo, P. Gil-jimenez, H. Gómez-moreno, and F. López-ferreras, Road-sign detection and recognition based on support vector machines, IEEE transactions on intelligent transportation systems, vol.8, issue.2, pp.264-278, 2007.

A. Ruta, Y. Li, and X. Liu, Robust class similarity measure for traffic sign recognition, IEEE Transactions on Intelligent Transportation Systems, vol.11, issue.4, pp.846-855, 2010.

F. Zaklouta, B. Stanciulescu, and O. Hamdoun, Traffic sign classification using kd trees and random forests, The 2011 International Joint Conference on Neural Networks, pp.2151-2155, 2011.

K. Lim, Y. Hong, Y. Choi, and H. Byun, Real-time traffic sign recognition based on a general purpose gpu and deep-learning, PLoS one, vol.12, issue.3, p.173317, 2017.

R. Qian, B. Zhang, Y. Yue, Z. Wang, and F. Coenen, Robust chinese traffic sign detection and recognition with deep convolutional neural network, Natural Computation (ICNC), 2015 11th International Conference on, pp.791-796, 2015.

J. Jin, K. Fu, and C. Zhang, Traffic sign recognition with hinge loss trained convolutional neural networks, IEEE Transactions on Intelligent Transportation Systems, vol.15, issue.5, 1991.

Y. Zeng, X. Xu, D. Shen, Y. Fang, and Z. Xiao, Traffic sign recognition using kernel extreme learning machines with deep perceptual features, IEEE Trans. Intell. Transp. Syst, vol.18, issue.6, pp.1647-1653, 2017.

Y. Yuan, Z. Xiong, and Q. Wang, An incremental framework for video-based traffic sign detection, tracking, and recognition, IEEE Transactions on Intelligent Transportation Systems, vol.18, issue.7, pp.1918-1929, 2017.

C. Brust, S. Sickert, M. Simon, E. Rodner, and J. Denzler, Convolutional patch networks with spatial prior for road detection and urban scene understanding, 2015.

G. L. Oliveira, W. Burgard, and T. Brox, Efficient deep models for monocular road segmentation, Intelligent Robots and Systems (IROS), pp.4885-4891, 2016.

V. Badrinarayanan, A. Kendall, and R. Cipolla, Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE transactions on pattern analysis and machine intelligence, vol.39, pp.2481-2495, 2017.

A. Gurghian, T. Koduri, S. V. Bailur, K. J. Carey, and V. N. Murali, Deeplanes: Endto-end lane position estimation using deep neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.38-45, 2016.

D. Levi, N. Garnett, E. Fetaya, and I. Herzlyia, Stixelnet: A deep convolutional network for obstacle detection and road segmentation, BMVC, pp.109-110, 2015.

A. Dairi, F. Harrou, M. Senouci, and Y. Sun, Unsupervised obstacle detection in driving environments using deep-learning-based stereovision, Robotics and Autonomous Systems, vol.100, pp.287-301, 2018.

G. Amato, F. Carrara, F. Falchi, C. Gennaro, and C. Vairo, Car parking occupancy detection using smart camera networks and deep learning, 2016 IEEE Symposium on Computers and Communication (ISCC), pp.1212-1217, 2016.

X. Ling, J. Sheng, O. Baiocchi, X. Liu, and M. E. Tolentino, Identifying parking spaces & detecting occupancy using vision-based iot devices, 2017 Global Internet of Things Summit (GIoTS), pp.1-6, 2017.

J. Zhao, H. Wu, and L. Chen, Road surface state recognition based on svm optimization and image segmentation processing, Journal of Advanced Transportation, vol.2017, 2017.

L. Zhang, F. Yang, Y. D. Zhang, and Y. J. Zhu, Road crack detection using deep convolutional neural network, 2016 IEEE international conference on image processing (ICIP), pp.3708-3712, 2016.

Z. Sun, G. Bebis, and R. Miller, Monocular precrash vehicle detection: features and classifiers, IEEE transactions on image processing, vol.15, issue.7, pp.2019-2034, 2006.

X. Wen, L. Shao, W. Fang, and Y. Xue, Efficient feature selection and classification for vehicle detection, IEEE Transactions on Circuits and Systems for Video Technology, vol.25, pp.508-517, 2015.

Y. Tang, C. Zhang, R. Gu, P. Li, and B. Yang, Vehicle detection and recognition for intelligent traffic surveillance system, Multimedia tools and applications, vol.76, pp.5817-5832, 2017.

X. Wen, L. Shao, Y. Xue, and W. Fang, A rapid learning algorithm for vehicle classification, Information Sciences, vol.295, pp.395-406, 2015.

Q. Fan, L. Brown, and J. Smith, A closer look at faster r-cnn for vehicle detection, 2016 IEEE intelligent vehicles symposium (IV), pp.124-129, 2016.

S. Yu, Y. Wu, W. Li, Z. Song, and W. Zeng, A model for fine-grained vehicle classification based on deep learning, Neurocomputing, vol.257, pp.97-103, 2017.

Z. Chen, N. Pears, M. Freeman, and J. Austin, Road vehicle classification using support vector machines, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, vol.4, pp.214-218, 2009.

N. Joshi, B. George, and L. Vanajakshi, Application of random forest algorithm to classify vehicles detected by a multiple inductive loop system, 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp.491-495, 2012.

W. Liu, M. Zhang, Z. Luo, and Y. Cai, An ensemble deep learning method for vehicle type classification on visual traffic surveillance sensors, IEEE Access, vol.5, pp.24-417, 2017.

D. Zhao, Y. Chen, and L. Lv, Deep reinforcement learning with visual attention for vehicle classification, IEEE Transactions on Cognitive and Developmental Systems, vol.9, issue.4, pp.356-367, 2017.

S. Z. Masood, G. Shu, A. Dehghan, and E. G. Ortiz, License plate detection and recognition using deeply learned convolutional neural networks, 2017.

H. Liu, Y. Tian, Y. Yang, L. Pang, and T. Huang, Deep relative distance learning: Tell the difference between similar vehicles, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2167-2175, 2016.

X. Liu, W. Liu, H. Ma, and H. Fu, Large-scale vehicle re-identification in urban surveillance videos, 2016 IEEE International Conference on Multimedia and Expo (ICME), pp.1-6, 2016.

X. Liu, W. Liu, T. Mei, and H. Ma, A deep learning-based approach to progressive vehicle re-identification for urban surveillance, European Conference on Computer Vision, pp.869-884, 2016.

, Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance, IEEE Transactions on Multimedia, vol.20, issue.3, pp.645-658, 2017.

J. Wang, L. Zhou, Y. Pan, S. Lee, Z. Song et al., Appearancebased brake-lights recognition using deep learning and vehicle detection, Intelligent Vehicles Symposium (IV), pp.815-820, 2016.

Z. Ouyang, J. Niu, and M. Guizani, Improved vehicle steering pattern recognition by using selected sensor data, IEEE Transactions on Mobile Computing, vol.17, issue.6, pp.1383-1396, 2018.

S. Ramyar, A. Homaifar, A. Karimoddini, and E. Tunstel, Identification of anomalies in lane change behavior using one-class svm, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), p.410, 2016.

Q. Yuan, A. Thangali, V. Ablavsky, and S. Sclaroff, Learning a family of detectors via multiplicative kernels, IEEE transactions on pattern analysis and machine intelligence, vol.33, pp.514-530, 2011.

Z. Chen, J. Yu, Y. Zhu, Y. Chen, and M. Li, Abnormal driving behaviors detection and identification using smartphone sensors, 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), vol.3, pp.524-532, 2015.

J. F. Júnior, E. Carvalho, B. V. Ferreira, C. Souza, Y. Suhara et al., Driver behavior profiling: An investigation with different smartphone sensors and machine learning, PLoS one, vol.12, issue.4, p.174959, 2017.

Z. Wang, F. Liu, X. Wang, and Y. Du, Driver modeling based on vehicular sensing data, 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018), 2018.

S. Lestyan, G. Acs, G. Biczok, and Z. Szalay, Extracting vehicle sensor signals from can logs for driver re-identification, 2019.

F. Martinelli, F. Mercaldo, V. Nardone, A. Orlando, and A. Santone, Cluster analysis for driver aggressiveness identification, ICISSP, pp.562-569, 2018.

M. Van-ly, S. Martin, and M. M. Trivedi, Driver classification and driving style recognition using inertial sensors, 2013 IEEE Intelligent Vehicles Symposium (IV), pp.1040-1045, 2013.

G. Ren, Y. Zhang, H. Liu, K. Zhang, and Y. Hu, A new lane-changing model with consideration of driving style, International Journal of Intelligent Transportation Systems Research, pp.1-9, 2019.

V. Vaitkus, P. Lengvenis, and G. ?ylius, Driving style classification using long-term accelerometer information, International Conference on Methods and Models in Automation and Robotics (MMAR), 201419.

, IEEE, pp.641-644, 2014.

Y. Bian, C. H. Lee, J. L. Zhao, and Y. Wan, A deep learning based model for driving risk assessment, Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019.

N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, international Conference on computer vision & Pattern Recognition (CVPR'05), vol.1, pp.886-893, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548512

S. Zhang, C. Bauckhage, and A. B. Cremers, Informed haar-like features improve pedestrian detection, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.947-954, 2014.

P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. Lecun, Pedestrian detection with unsupervised multi-stage feature learning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3626-3633, 2013.

X. Du, M. El-khamy, J. Lee, and L. Davis, Fused dnn: A deep neural network fusion approach to fast and robust pedestrian detection, 2017 IEEE winter conference on applications of computer vision (WACV)

, IEEE, pp.953-961, 2017.

J. Li, X. Liang, S. Shen, T. Xu, J. Feng et al., Scale-aware fast r-cnn for pedestrian detection, IEEE Transactions on Multimedia, vol.20, issue.4, pp.985-996, 2018.

N. Taherkhani and S. Pierre, Centralized and localized data congestion control strategy for vehicular ad hoc networks using a machine learning clustering algorithm, IEEE Transactions on Intelligent Transportation Systems, vol.17, issue.11, pp.3275-3285, 2016.

P. Szczurek, B. Xu, J. Lin, and O. Wolfson, Spatio-temporal information ranking in vanet applications, International Journal of Next-Generation Computing, vol.1, issue.1, pp.62-86, 2010.

L. Zhao, Y. Li, C. Meng, C. Gong, and X. Tang, A svm based routing scheme in vanets, 2016 16th International Symposium on Communications and Information Technologies (ISCIT), pp.380-383, 2016.

A. Taylor, S. Leblanc, and N. Japkowicz, Anomaly detection in automobile control network data with long short-term memory networks, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp.130-139, 2016.

N. Lyamin, D. Kleyko, Q. Delooz, and A. Vinel, AI-based malicious network traffic detection in VANETs, IEEE Network, vol.32, issue.6, pp.15-21, 2018.

O. Puñal, I. Akta?, C. Schnelke, G. Abidin, K. Wehrle et al., Machine learningbased jamming detection for ieee 802.11: Design and experimental evaluation, World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp.1-10, 2014.

M. Kang and J. Kang, Intrusion detection system using deep neural network for in-vehicle network security, PloS one, vol.11, issue.6, p.155781, 2016.

S. Zhang, J. Yang, and B. Schiele, Occluded pedestrian detection through guided attention in cnns, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.6995-7003, 2018.

T. Tang, Y. Wang, X. Yang, and Y. Wu, A new car-following model accounting for varying road condition, Nonlinear Dynamics, vol.70, issue.2, pp.1397-1405, 2012.

S. Kim, W. Liu, M. H. Ang, E. Frazzoli, and D. Rus, The impact of cooperative perception on decision making and planning of autonomous vehicles, IEEE Intelligent Transportation Systems Magazine, vol.7, issue.3, pp.39-50, 2015.

X. Baró, S. Escalera, J. Vitrià, O. Pujol, and P. Radeva, Traffic sign recognition using

Z. Yang and L. S. Pun-cheng, Vehicle detection in intelligent transportation systems and its applications under varying environments: A review, Image and Vision Computing, vol.69, pp.143-154, 2018.

S. D. Khan and H. Ullah, A survey of advances in vision-based vehicle reidentification, Computer Vision and Image Understanding, 2019.

C. M. Martinez, M. Heucke, F. Wang, B. Gao, and D. Cao, Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey, IEEE Transactions on Intelligent Transportation Systems, vol.19, issue.3, pp.666-676, 2018.

S. Singh, Critical reasons for crashes investigated in the national motor vehicle crash causation survey, Tech. Rep, 2015.

J. Engelbrecht, M. J. Booysen, G. Van-rooyen, and F. J. Bruwer, Survey of smartphone-based sensing in vehicles for intelligent transportation system applications, IET Intelligent Transport Systems, vol.9, issue.10, pp.924-935, 2015.

P. Baltusis, On board vehicle diagnostics, SAE Technical Paper, Tech. Rep, 2004.

S. Kaplan, M. A. Guvensan, A. G. Yavuz, and Y. Karalurt, Driver behavior analysis for safe driving: A survey, IEEE Transactions on Intelligent Transportation Systems, vol.16, issue.6, pp.3017-3032, 2015.

Z. Li, K. Zhang, B. Chen, Y. Dong, and L. Zhang, Driver identification in intelligent vehicle systems using machine learning algorithms, IET Intelligent Transport Systems, vol.13, issue.1, pp.40-47, 2018.

A. Brunetti, D. Buongiorno, G. F. Trotta, and V. Bevilacqua, Computer vision and deep learning techniques for pedestrian detection and tracking: A survey, Neurocomputing, vol.300, pp.17-33, 2018.

W. Ouyang and X. Wang, A discriminative deep model for pedestrian detection with occlusion handling, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3258-3265, 2012.

F. Qu, Z. Wu, F. Wang, and W. Cho, A security and privacy review of vanets, IEEE Transactions on Intelligent Transportation Systems, vol.16, issue.6, pp.2985-2996, 2015.

H. Chang, Y. Lee, B. Yoon, and S. Baek, Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences, IET intelligent transport systems, vol.6, issue.3, pp.292-305, 2012.

Y. Jeong, Y. Byon, M. M. Castro-neto, and S. M. Easa, Supervised weighting-online learning algorithm for short-term traffic flow prediction, IEEE Transactions on Intelligent Transportation Systems, vol.14, issue.4, pp.1700-1707, 2013.

Y. Tian and L. Pan, Predicting short-term traffic flow by long short-term memory recurrent neural network, Smart City/SocialCom/SustainCom (SmartCity), 2015 IEEE International Conference on, pp.153-158, 2015.

X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, Long short-term memory neural network for traffic speed prediction using remote microwave sensor data, Transportation Research Part C: Emerging Technologies, vol.54, pp.187-197, 2015.

J. Wang, Q. Gu, J. Wu, G. Liu, and Z. Xiong, Traffic speed prediction and congestion source exploration: A deep learning method, Data Mining (ICDM), 2016 IEEE 16th International Conference on, pp.499-508, 2016.

Y. Wu and H. Tan, Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework, 2016.

X. Cheng, R. Zhang, J. Zhou, and W. Xu, Deeptransport: Learning spatial-temporal dependency for traffic condition forecasting, 2018 International Joint Conference on Neural Networks (IJCNN), pp.1-8, 2018.

B. Shahsavari and P. , Short-term traffic forecasting: Modeling and learning spatio-temporal relations in transportation networks using graph neural networks, University of California at Berkeley, 2015.

Y. Lv, Y. Duan, W. Kang, Z. Li, and F. Wang, Traffic flow prediction with big data: A deep learning approach, IEEE Trans. Intelligent Transportation Systems, vol.16, issue.2, pp.865-873, 2015.

B. Yu, H. Yin, and Z. Zhu, Spatio-temporal graph convolutional neural network: A deep learning framework for traffic forecasting, 2017.

A. Koesdwiady, R. Soua, and F. Karray, Improving traffic flow prediction with weather information in connected cars: a deep learning approach, IEEE Transactions on Vehicular Technology, vol.65, issue.12, pp.9508-9517, 2016.

Y. Jia, J. Wu, M. Ben-akiva, R. Seshadri, and Y. Du, Rainfall-integrated traffic speed prediction using deep learning method, IET Intelligent Transport Systems, vol.11, issue.9, pp.531-536, 2017.

R. Soua, A. Koesdwiady, and F. Karray, Big-data-generated traffic flow prediction using deep learning and dempster-shafer theory, Neural Networks (IJCNN), 2016 International Joint Conference on, pp.3195-3202, 2016.

C. Wu, J. Ho, and D. Lee, Traveltime prediction with support vector regression, IEEE transactions on intelligent transportation systems, vol.5, issue.4, pp.276-281, 2004.

Y. Duan, Y. Lv, and F. Wang, Travel time prediction with lstm neural network, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

, IEEE, pp.1053-1058, 2016.

X. Niu, Y. Zhu, and X. Zhang, Deepsense: A novel learning mechanism for traffic prediction with taxi gps traces, 2014 IEEE global communications conference, pp.2745-2750, 2014.

F. Zhang, X. Zhu, T. Hu, W. Guo, C. Chen et al., Urban link travel time prediction based on a gradient boosting method considering spatiotemporal correlations, ISPRS International Journal of Geo-Information, vol.5, issue.11, p.201, 2016.

C. Siripanpornchana, S. Panichpapiboon, and P. Chaovalit, Travel-time prediction with deep learning, Region 10 Conference (TENCON), pp.1859-1862, 2016.

C. Chen, An arrival time prediction method for bus system, IEEE Internet of Things Journal, vol.5, issue.5, pp.4231-4232, 2018.

L. Oneto, E. Fumeo, G. Clerico, R. Canepa, F. Papa et al., Train delay prediction systems: a big data analytics perspective, Big data research, vol.11, pp.54-64, 2018.

D. Wang, J. Zhang, W. Cao, J. Li, and Y. Zheng, When will you arrive? estimating travel time based on deep neural networks," in Thirty-Second AAAI Conference on Artificial Intelligence, 2018.

Ü. Dogan, J. Edelbrunner, and I. Iossifidis, Autonomous driving: A comparison of machine learning techniques by means of the prediction of lane change behavior, 2011 IEEE International Conference on Robotics and Biomimetics, pp.1837-1843, 2011.

I. Kim, J. Bong, J. Park, and S. Park, Prediction of driver's intention of lane change by augmenting sensor information using machine learning techniques, Sensors, vol.17, issue.6, p.1350, 2017.

A. I. Maqueda, A. Loquercio, G. Gallego, N. García, and D. Scaramuzza, Event-based vision meets deep learning on steering prediction for self-driving cars, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5419-5427, 2018.

P. Ondruska and I. Posner, Deep tracking: Seeing beyond seeing using recurrent neural networks, Thirtieth AAAI Conference on Artificial Intelligence, 2016.

B. Kim, C. M. Kang, J. Kim, S. H. Lee, C. C. Chung et al., Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

, IEEE, pp.399-404, 2017.

N. Djuric, V. Radosavljevic, H. Cui, T. Nguyen, F. Chou et al., Motion prediction of traffic actors for autonomous driving using deep convolutional networks, 2018.

H. Xue, D. Q. Huynh, and M. Reynolds, Sslstm: a hierarchical lstm model for pedestrian trajectory prediction, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp.1186-1194, 2018.

E. Rehder, F. Wirth, M. Lauer, and C. Stiller, Pedestrian prediction by planning using deep neural networks, 2018 IEEE International Conference on Robotics and Automation (ICRA), pp.1-5, 2018.

Y. Xu, Z. Piao, and S. Gao, Encoding crowd interaction with deep neural network for pedestrian trajectory prediction, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5275-5284, 2018.

P. Zhang, W. Ouyang, P. Zhang, J. Xue, and N. Zheng, Sr-lstm: State refinement for lstm towards pedestrian trajectory prediction, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019.

S. Hoermann, M. Bach, and K. Dietmayer, Dynamic occupancy grid prediction for urban autonomous driving: A deep learning approach with fully automatic labeling, 2018 IEEE International Conference on Robotics and Automation (ICRA), pp.2056-2063, 2018.

S. Hoermann, M. Bach, and K. Dietmayer, Learning long-term situation prediction for automated driving, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp.1000-1005, 2017.

Y. Zheng, S. Rajasegarar, and C. Leckie, Parking availability prediction for sensorenabled car parks in smart cities, 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pp.1-6, 2015.

W. Alajali, S. Wen, and W. Zhou, On-street car parking prediction in smart city: A multisource data analysis in sensor-cloud environment, International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, pp.641-652, 2017.

W. Shao, Y. Zhang, B. Guo, K. Qin, J. Chan et al., Parking availability prediction with long short term memory model, International Conference on Green, Pervasive, and Cloud Computing, pp.124-137, 2018.

S. Yang, W. Ma, X. Pi, and S. Qian, A deep learning approach to real-time parking occupancy prediction in spatiotermporal networks incorporating multiple spatio-temporal data sources, 2019.

S. Das, Time series analysis, 1994.

X. Ma, Z. Dai, Z. He, J. Ma, Y. Wang et al., Learning traffic as images: a deep convolutional neural network for largescale transportation network speed prediction, Sensors, vol.17, issue.4, p.818, 2017.

Y. Wang, Y. Zheng, and Y. Xue, Travel time estimation of a path using sparse trajectories, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.25-34, 2014.

T. Gindele, S. Brechtel, and R. Dillmann, Learning driver behavior models from traffic observations for decision making and planning, IEEE Intelligent Transportation Systems Magazine, vol.7, issue.1, pp.69-79, 2015.

J. Wiest, M. Höffken, U. Kreßel, and K. Dietmayer, Probabilistic trajectory prediction with gaussian mixture models, 2012 IEEE Intelligent Vehicles Symposium, pp.141-146, 2012.

Y. Kong and Y. Fu, Human action recognition and prediction: A survey, 2018.

M. Althoff and J. M. Dolan, Online verification of automated road vehicles using reachability analysis, IEEE Transactions on Robotics, vol.30, issue.4, pp.903-918, 2014.

M. Althoff and S. Magdici, Set-based prediction of traffic participants on arbitrary road networks, IEEE Transactions on Intelligent Vehicles, vol.1, issue.2, pp.187-202, 2016.

S. El-tantawy, B. Abdulhai, and H. Abdelgawad, Design of reinforcement learning parameters for seamless application of adaptive traffic signal control, Journal of Intelligent Transportation Systems, vol.18, issue.3, pp.227-245, 2014.

M. Abdoos, N. Mozayani, and A. L. Bazzan, Holonic multi-agent system for traffic signals control, Engineering Applications of Artificial Intelligence, vol.26, issue.5-6, pp.1575-1587, 2013.

I. Arel, C. Liu, T. Urbanik, and A. Kohls, Reinforcement learning-based multi-agent system for network traffic signal control, IET Intelligent Transport Systems, vol.4, issue.2, pp.128-135, 2010.

P. Balaji, X. German, and D. Srinivasan, Urban traffic signal control using reinforcement learning agents, IET Intelligent Transport Systems, vol.4, issue.3, pp.177-188, 2010.

W. Genders and S. Razavi, Using a deep reinforcement learning agent for traffic signal control, 2016.

J. Gao, Y. Shen, J. Liu, M. Ito, and N. Shiratori, Adaptive traffic signal control: Deep reinforcement learning algorithm with experience replay and target network, 2017.

R. Zhang, A. Ishikawa, W. Wang, B. Striner, and O. Tonguz, Partially observable reinforcement learning for intelligent transportation systems, 2018.

F. Zhu and S. V. Ukkusuri, Accounting for dynamic speed limit control in a stochastic traffic environment: A reinforcement learning approach, Transportation research part C: emerging technologies, vol.41, pp.30-47, 2014.

Z. Li, P. Liu, C. Xu, H. Duan, and W. Wang, Reinforcement learning-based variable speed limit control strategy to reduce traffic congestion at freeway recurrent bottlenecks, IEEE transactions on intelligent transportation systems, vol.18, pp.3204-3217, 2017.

E. Walraven, M. T. Spaan, and B. Bakker, Traffic flow optimization: A reinforcement learning approach, Engineering Applications of Artificial Intelligence, vol.52, pp.203-212, 2016.

C. Wang, J. Zhang, L. Xu, L. Li, and B. Ran, A new solution for freeway congestion: Cooperative speed limit control using distributed reinforcement learning, IEEE Access, 2019.

Q. Huy, S. Mita, and K. Yoneda, A practical and optimal path planning for autonomous parking using fast marching algorithm and support vector machine, IEICE TRANSAC-TIONS on Information and Systems, vol.96, issue.12, pp.2795-2804, 2013.

P. Abbeel, D. Dolgov, A. Y. Ng, and S. Thrun, Apprenticeship learning for motion planning with application to parking lot navigation, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp.1083-1090, 2008.

W. Liu, Z. Li, L. Li, and F. Wang, Parking like a human: A direct trajectory planning solution, IEEE Transactions on Intelligent Transportation Systems, vol.18, issue.12, pp.3388-3397, 2017.

L. Yu, X. Shao, Y. Wei, and K. Zhou, Intelligent land-vehicle model transfer trajectory planning method based on deep reinforcement learning, Sensors, vol.18, issue.9, p.2905, 2018.

M. Bojarski, D. Testa, D. Dworakowski, B. Firner, B. Flepp et al., End to end learning for self-driving cars, 2016.

Z. Yang, Y. Zhang, J. Yu, J. Cai, and J. Luo, End-to-end multi-modal multi-task vehicle control for self-driving cars with visual perceptions, 2018 24th International Conference on Pattern Recognition (ICPR), pp.2289-2294, 2018.

L. Xu, J. Hu, H. Jiang, and W. Meng, Establishing style-oriented driver models by imitating human driving behaviors, IEEE Transactions on Intelligent Transportation Systems, vol.16, issue.5, pp.2522-2530, 2015.

L. Li, K. Ota, and M. Dong, Humanlike driving: empirical decision-making system for autonomous vehicles, IEEE Transactions on Vehicular Technology, vol.67, issue.8, pp.6814-6823, 2018.

A. Kuefler, J. Morton, T. Wheeler, and M. Kochenderfer, Imitating driver behavior with generative adversarial networks, 2017 IEEE Intelligent Vehicles Symposium (IV), pp.204-211, 2017.

R. P. Bhattacharyya, D. J. Phillips, C. Liu, J. K. Gupta, K. Driggs-campbell et al., Simulating emergent properties of human driving behavior using multiagent reward augmented imitation learning, 2019.

X. Huang, T. Yuan, G. Qiao, and Y. Ren, Deep reinforcement learning for multimedia traffic control in software defined networking, IEEE Network, vol.32, issue.6, pp.35-41, 2018.

Z. Xu, J. Tang, J. Meng, W. Zhang, Y. Wang et al., Experience-driven networking: A deep reinforcement learning based approach, IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp.1871-1879, 2018.

C. An, C. Wu, T. Yoshinaga, X. Chen, and Y. Ji, A context-aware edge-based VANET communication scheme for ITS, Sensors, vol.18, issue.7, p.2022, 2018.

H. Ye and G. Y. Li, Deep reinforcement learning for resource allocation in V2V communications, 2018 IEEE International Conference on Communications (ICC), pp.1-6, 2018.

K. Zhang, S. Leng, X. Peng, L. Pan, S. Maharjan et al., Artificial intelligence inspired transmission scheduling in cognitive vehicular communications and networks, IEEE Internet of Things Journal, 2018.

D. Zhang, F. R. Yu, R. Yang, and H. Tang, A deep reinforcement learning-based trust management scheme for software-defined vehicular networks, Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, pp.1-7, 2018.

M. A. Salahuddin, A. Al-fuqaha, and M. Guizani, Reinforcement learning for resource provisioning in the vehicular cloud, IEEE Wireless Communications, vol.23, issue.4, pp.128-135, 2016.

Q. Qi and Z. Ma, Vehicular edge computing via deep reinforcement learning, 2018.

Q. Qi, J. Wang, Z. Ma, H. Sun, Y. Cao et al., Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach, IEEE Transactions on Vehicular Technology, 2019.

W. Wang, R. Lan, J. Gu, A. Huang, H. Shan et al., Edge caching at base stations with device-to-device offloading, IEEE Access, vol.5, pp.6399-6410, 2017.

S. S. Tanzil, W. Hoiles, and V. Krishnamurthy, Adaptive scheme for caching youtube content in a cellular network: Machine learning approach, Ieee Access, vol.5, pp.5870-5881, 2017.

Y. He, F. R. Yu, N. Zhao, H. Yin, and A. Boukerche, Deep reinforcement learning (drl)-based resource management in softwaredefined and virtualized vehicular ad hoc networks, Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications, pp.47-54, 2017.

Y. He, N. Zhao, and H. Yin, Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach, IEEE Transactions on Vehicular Technology, vol.67, issue.1, pp.44-55, 2018.

Y. He, F. R. Yu, N. Zhao, V. C. Leung, and H. Yin, Software-defined networks with mobile edge computing and caching for smart cities: A big data deep reinforcement learning approach, IEEE Communications Magazine, vol.55, issue.12, pp.31-37, 2017.

Y. He, C. Liang, Z. Zhang, F. R. Yu, N. Zhao et al., Resource allocation in software-defined and information-centric vehicular networks with mobile edge computing, Vehicular Technology Conference, pp.1-5, 2017.

L. T. Tan and R. Q. Hu, Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning, IEEE Transactions on Vehicular Technology, vol.67, issue.11, pp.10-190, 2018.

R. F. Atallah, C. M. Assi, and J. Y. Yu, A reinforcement learning technique for optimizing downlink scheduling in an energylimited vehicular network, IEEE Transactions on Vehicular Technology, vol.66, issue.6, pp.4592-4601, 2017.

R. Atallah, C. Assi, and M. Khabbaz, Deep reinforcement learning-based scheduling for roadside communication networks, Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt, pp.1-8, 2017.

R. F. Atallah, C. M. Assi, and M. J. Khabbaz, Scheduling the operation of a connected vehicular network using deep reinforcement learning, IEEE Transactions on Intelligent Transportation Systems, issue.99, pp.1-14, 2018.

X. Qi, Y. Luo, G. Wu, K. Boriboonsomsin, and M. J. Barth, Deep reinforcement learning-based vehicle energy efficiency autonomous learning system, Intelligent Vehicles Symposium, pp.1228-1233, 2017.

Y. Hu, W. Li, K. Xu, T. Zahid, F. Qin et al., Energy management strategy for a hybrid electric vehicle based on deep reinforcement learning, Applied Sciences, vol.8, issue.2, p.187, 2018.

H. Hartenstein and L. Laberteaux, A tutorial survey on vehicular ad hoc networks, IEEE Communications magazine, vol.46, issue.6, pp.164-171, 2008.

S. Bakker and J. J. Trip, Policy options to support the adoption of electric vehicles in the urban environment, Transportation Research Part D: Transport and Environment, vol.25, pp.18-23, 2013.

E. S. Rigas, S. D. Ramchurn, and N. Bassiliades, Managing electric vehicles in the smart grid using artificial intelligence: A survey, IEEE Transactions on Intelligent Transportation Systems, vol.16, issue.4, pp.1619-1635, 2015.

A. Vogel, D. Ramachandran, R. Gupta, and A. Raux, Improving hybrid vehicle fuel efficiency using inverse reinforcement learning, Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012.

S. Ermon, Y. Xue, C. Gomes, and B. Selman, Learning policies for battery usage optimization in electric vehicles, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.195-210, 2012.

T. Liu, X. Hu, S. E. Li, and D. Cao, Reinforcement learning optimized look-ahead energy management of a parallel hybrid electric vehicle, IEEE/ASME Transactions on Mechatronics, vol.22, issue.4, pp.1497-1507, 2017.

X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, Multi-view 3d object detection network for autonomous driving, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, Joint 3d proposal generation and object detection from view aggregation, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.1-8, 2018.

A. Mousavian, D. Anguelov, J. Flynn, and J. Kosecka, 3d bounding box estimation using deep learning and geometry, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.7074-7082, 2017.

B. Xu and Z. Chen, Multi-level fusion based 3d object detection from monocular images, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.2345-2353, 2018.

P. Li, X. Chen, and S. Shen, Stereo r-cnn based 3d object detection for autonomous driving, 2019.

J. Xue, J. Fang, T. Li, B. Zhang, P. Zhang et al., Blvd: Building a large-scale 5d semantics benchmark for autonomous driving, 2019.

J. Xue, J. Fang, and P. Zhang, A survey of scene understanding by event reasoning in autonomous driving, International Journal of Automation and Computing, vol.15, issue.3, pp.249-266, 2018.

K. Sjoberg, P. Andres, T. Buburuzan, and A. Brakemeier, Cooperative intelligent transport systems in europe: Current deployment status and outlook, IEEE Vehicular Technology Magazine, vol.12, issue.2, pp.89-97, 2017.

M. A. Javed, S. Zeadally, and E. B. Hamida, Data analytics for cooperative intelligent transport systems, Vehicular communications, vol.15, pp.63-72, 2019.

G. Thandavarayan, M. Sepulcre, and J. Gozalvez, Generation of cooperative perception messages for connected and automated vehicles, 2019.

M. A. Javed, E. B. Hamida, A. Alfuqaha, and B. Bhargava, Adaptive security for intelligent transport system applications, IEEE Intelligent Transportation Systems Magazine, vol.10, issue.2, pp.110-120, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01591878

M. Amoozadeh, A. Raghuramu, C. Chuah, D. Ghosal, H. M. Zhang et al., Security vulnerabilities of connected vehicle streams and their impact on cooperative driving, IEEE Communications Magazine, vol.53, issue.6, pp.126-132, 2015.

Y. Yuan and F. Wang, Towards blockchain-based intelligent transportation systems, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp.2663-2668, 2016.

N. Wanichayapong, W. Pruthipunyaskul, W. Pattara-atikom, and P. Chaovalit, Social-based traffic information extraction and classification, 2011 11th International Conference on ITS Telecommunications, pp.107-112, 2011.

X. Zheng, W. Chen, P. Wang, D. Shen, S. Chen et al., Big data for social transportation, IEEE Transactions on Intelligent Transportation Systems, vol.17, issue.3, pp.620-630, 2015.

Y. Lv, Y. Chen, X. Zhang, Y. Duan, and N. L. Li, Social media based transportation research: The state of the work and the networking, IEEE/CAA Journal of Automatica Sinica, vol.4, issue.1, pp.19-26, 2017.

Y. Zeng, Q. Wu, and R. Zhang, Accessing from the sky: A tutorial on uav communications for 5g and beyond, 2019.

H. Menouar, I. Guvenc, K. Akkaya, A. S. Uluagac, A. Kadri et al., Uavenabled intelligent transportation systems for the smart city: Applications and challenges, IEEE Communications Magazine, vol.55, issue.3, pp.22-28, 2017.

N. Ammour, H. Alhichri, Y. Bazi, B. Benjdira, N. Alajlan et al., Deep learning approach for car detection in uav imagery, Remote Sensing, vol.9, issue.4, p.312, 2017.

Z. Yijing, Z. Zheng, Z. Xiaoyi, and L. Yang, Q learning algorithm based uav path learning and obstacle avoidence approach, 2017 36th Chinese Control Conference (CCC), pp.3397-3402, 2017.

Y. Zhao, Z. Zheng, and Y. Liu, Survey on computational-intelligence-based uav path planning, Knowledge-Based Systems, vol.158, pp.54-64, 2018.

U. Challita, A. Ferdowsi, M. Chen, and W. Saad, Machine learning for wireless connectivity and security of cellularconnected uavs, IEEE Wireless Communications, vol.26, issue.1, pp.28-35, 2019.