, Avec une période d'échantillonnage dT , il devient possible d'écrire, pour l

J. Ackermann, J. Guldner, W. Sienel, R. Steinhauser, and V. I. Utkin, Linear and nonlinear controller design for robust automatic steering, IEEE Transactions on Control Systems Technology, vol.3, issue.1, p.132143, 1995.

A. Mécatronique--vocabulaire, , 2008.

C. Alia, T. Gilles, T. Reine, and C. Ali, Local trajectory planning and tracking of autonomous vehicles, using clothoid tentacles method, 2015 IEEE Intelligent Vehicles Symposium (IV), p.674679, 2015.

F. Altché and A. De-la-fortelle, An lstm network for highway trajectory prediction, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), p.353359, 2017.

G. S. Aoude and J. P. How, Using support vector machines and bayesian ltering for classifying agent intentions at road intersections, 2009.

K. J. Åström and T. Hägglund, PID controllers : theory, design, and tuning, vol.2, 1995.

R. Attia, R. Orjuela, and M. Basset, Combined longitudinal and lateral control for automated vehicle guidance, Vehicle System Dynamics, vol.52, issue.2, p.261279, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01027591

G. Baet, A. Charara, and D. Lechner, Estimation of vehicle sideslip, tire force and wheel cornering stiness, Control Engineering Practice, vol.17, issue.11, p.12551264, 2009.

A. Benine-neto, S. Mammar, B. Lusetti, and S. Scalzi, Piecewise ane control for lane departure avoidance, Vehicle System Dynamics, vol.51, issue.8, p.11211150, 2013.

T. Besselmann and M. Morari, Autonomous vehicle steering using explicit lpv-mpc, European Control Conference (ECC), p.26282633, 2009.

S. Bezzaoucha, Fault tolerant control for Takagi-Sugeno nonlinear systems, Theses, 2013.
URL : https://hal.archives-ouvertes.fr/tel-01750104

B. Boada, M. Boada, and V. Diaz, Vehicle sideslip angle measurement based on sensor data fusion using an integrated ans and an unscented kalman lter algorithm, Mechanical Systems and Signal Processing, vol.72, p.832845, 2016.

F. Bocklisch, S. F. Bocklisch, M. Beggiato, and J. F. Krems, Adaptive fuzzy pattern classication for the online detection of driver lane change intention, Neurocomputing, vol.262, p.148158, 2017.

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

F. Boyer and F. Lamiraux, Trajectory deformation applied to kinodynamic motion planning for a realistic car model, Proceedings 2006 IEEE International Conference on Robotics and Automation, p.487492, 2006.

P. Boyraz, M. Acar, and D. Kerr, Signal modelling and hidden markov models for driving manoeuvre recognition and driver fault diagnosis in an urban road scenario, Intelligent Vehicles Symposium, p.987992, 2007.

S. Brechtel, T. Gindele, and R. Dillmann, Probabilistic mdp-behavior planning for cars, 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), p.15371542, 2011.

S. Brechtel, T. Gindele, and R. Dillmann, Probabilistic decision-making under uncertainty for autonomous driving using continuous pomdps, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), p.392399, 2014.

L. Breiman, Random forests. Machine learning, vol.45, p.532, 2001.

L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classication and regression trees. wadsworth & brooks, Cole Statistics/Probability Series, 1984.

O. Brock and O. Khatib, High-speed navigation using the global dynamic window approach, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C), vol.1, p.341346, 1999.

A. Broggi, M. Bertozzi, A. Fascioli, C. G. Bianco, and A. Piazzi, The ARGO autonomous vehicle's vision and control systems, International Journal of Intelligent Control and Systems, vol.3, issue.4, p.409441, 1999.

G. Brusaglino, Safe and eective mobility in Europe-the contribution of the PROME-THEUS programme, Prometheus and Drive, IEE Colloquium on, 1992.

L. Cai, A. B. Rad, W. Chan, and K. Cai, A robust fuzzy PD controller for automatic steering control of autonomous vehicles, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ'03, vol.1, p.549554, 2003.

S. Chaib, M. S. Netto, and S. Mammar, H ? , adaptive, PID and fuzzy control : a comparison of controllers for vehicle lane keeping, IEEE Intelligent Vehicles Symposium, p.139144, 2004.

X. Chalandon, Conscience de la situation : invariants internes et invariants externes, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00824020

A. C. Charalampidis and D. Gillet, Speed prole optimization for vehicles crossing an intersection under a safety constraint, Control Conference (ECC), p.28942901, 2014.

F. Cheli, E. Sabbioni, M. Pesce, and S. Melzi, A methodology for vehicle sideslip angle identication : comparison with experimental data, Vehicle System Dynamics, vol.45, issue.6, p.549563, 2007.

C. Chen and H. Tan, Experimental study of dynamic look-ahead scheme for vehicle steering control, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), vol.5, p.31633167, 1999.

C. Chen, Y. He, C. Bu, J. Han, and X. Zhang, Quartic bézier curve based trajectory generation for autonomous vehicles with curvature and velocity constraints, 2014 IEEE International Conference on Robotics and Automation (ICRA), p.61086113, 2014.

L. Chen and A. G. Ulsoy, Identication of a driver steering model, and model uncertainty, from driving simulator data, Transactions-American Society of Mechanical Engineers Journal of Dynamic Systems Measurement and Control, vol.123, issue.4, p.623629, 2001.

Y. Chen, Y. Ji, and K. Guo, A reduced-order nonlinear sliding mode observer for vehicle slip angle and tyre forces, Vehicle System Dynamics, vol.52, issue.12, p.17161728, 2014.

H. Cherouat, M. Braci, and S. Diop, Vehicle velocity, side slip angles and yaw rate estimation, Proceedings of the IEEE International Symposium on Industrial Electronics, vol.1, p.349354, 2005.

M. Chevrieé, Modélisation électrothermique de composants électriques et électroniques automobiles et estimation des résistances de contact dans les connecteurs, 2016.

C. Cortes and V. Vapnik, Support-vector networks, Machine learning, vol.20, issue.3, p.273297, 1995.

L. B. Cremean, T. B. Foote, J. H. Gillula, G. H. Hines, D. Kogan et al., Alice : An information-rich autonomous vehicle for high-speed desert navigation, Journal of Field Robotics, vol.23, issue.9, p.777810, 2006.

A. G. Cunningham, E. Galceran, R. M. Eustice, and E. Olson, Mpdm : Multipolicy decision-making in dynamic, uncertain environments for autonomous driving, 2015 IEEE International Conference on Robotics and Automation (ICRA), p.16701677

, IEEE, 2015.

D. A. De-lima and G. A. Pereira, Navigation of an autonomous car using vector elds and the dynamic window approach, Journal of Control, Automation and Electrical Systems, vol.24, issue.1-2, p.106116, 2013.

D. A. De-lima and A. C. Victorino, A hybrid controller for vision-based navigation of autonomous vehicles in urban environments, IEEE Transactions on Intelligent Transportation Systems, vol.17, issue.8, p.23102323, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01305733

V. , D. Oliveira, and A. Karimi, Robust and gain-scheduled PID controller design for condensing boilers by linear programming, IFAC Proceedings Volumes, vol.45, p.335340, 2012.

P. , D. Moral, and A. Doucet, Particle methods : An introduction with applications, ESAIM : proceedings, vol.44, p.146, 2014.
URL : https://hal.archives-ouvertes.fr/inria-00403917

V. Delsart and T. Fraichard, Navigating dynamic environments using trajectory deformation, IEEE/RSJ International Conference on Intelligent Robots and Systems, p.226233, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00293505

E. W. Dijkstra, A note on two problems in connexion with graphs, Numerische mathematik, vol.1, issue.1, p.269271, 1959.

N. Ding, Y. Zhang, F. Gao, and G. Xu, A Gain-Scheduled PID Controller for Automatic Path Following of a Tractor Semi-Trailer, SAE International Journal of Commercial Vehicles, vol.6, p.110117, 2013.

M. Doumiati, A. C. Victorino, A. Charara, and D. Lechner, Onboard real-time estimation of vehicle lateral tireroad forces and sideslip angle, IEEE/ASME Transactions on Mechatronics, vol.16, issue.4, p.601614, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00558840

J. C. Doyle, K. Glover, P. P. Khargonekar, and B. A. Francis, State-space solutions to standard H 2 and H ? control problems, IEEE Transactions on Automatic control, vol.34, issue.8, p.831847, 1989.

H. Du, J. Lam, K. Cheung, W. Li, and N. Zhang, Side-slip angle estimation and stability control for a vehicle with a non-linear tyre model and a varying speed, Proceedings of the Institution of Mechanical Engineers, Part D : Journal of Automobile Engineering, vol.229, issue.4, p.486505, 2015.

J. Du and T. A. Johansen, A gap metric based weighting method for multimodel predictive control of MIMO nonlinear systems, Journal of Process Control, vol.24, issue.9, p.13461357, 2014.

X. Du, H. Sun, K. Qian, Y. Li, and L. Lu, A prediction model for vehicle sideslip angle based on neural network, 2nd IEEE International Conference on Information and Financial Engineering, p.451455, 2010.

L. Dupont, L. Patte, P. Boivin, P. Flachat, B. Guichet et al., Aménagement des carrefours interurbains sur les routes principales. Carrefours plans -Guide technique, Autoroutes (SETRA). Accessed, vol.24, 1998.

C. Edwards and S. Spurgeon, Sliding mode control : theory and applications, 1998.

A. E. Hajjaji and S. Bentalba, Fuzzy path tracking control for automatic steering of vehicles, Robotics and Autonomous Systems, vol.43, issue.4, p.203213, 2003.

M. T. Emirler, K. Kahraman, M. Entürk, B. Güvenç, L. Güvenç et al., Vehicle yaw rate estimation using a virtual sensor, International Journal of Vehicular Technology, 2013.

M. R. Endsley, Toward a theory of situation awareness in dynamic systems, Human factors, vol.37, issue.1, p.3264, 1995.

G. Erinc and S. Carpin, A genetic algorithm for nonholonomic motion planning, Proceedings 2007 IEEE International Conference on Robotics and Automation, pp.1843-1849, 2007.

P. Falcone, F. Borrelli, J. Asgari, H. E. Tseng, and D. Hrovat, Predictive active steering control for autonomous vehicle systems, IEEE Transactions on control systems technology, vol.15, issue.3, p.566580, 2007.

D. Fox, W. Burgard, and S. Thrun, The dynamic window approach to collision avoidance, IEEE Robotics & Automation Magazine, vol.4, issue.1, p.2333, 1997.

T. Fraichard and H. Asama, Inevitable collision statesa step towards safer robots ?, Advanced Robotics, vol.18, issue.10, p.10011024, 2004.

T. Fraichard and A. Scheuer, From reeds and shepp's to continuous-curvature paths, IEEE Transactions on Robotics, vol.20, issue.6, p.10251035, 2004.

A. Furda and L. Vlacic, Enabling safe autonomous driving in real-world city trac using multiple criteria decision making, IEEE Intelligent Transportation Systems Magazine, vol.3, issue.1, p.417, 2011.

R. Garcia, O. Aycard, T. Vu, and M. Ahrholdt, High level sensor data fusion for automotive applications using occupancy grids, Control, Automation, Robotics and Vision, p.530535, 2008.

T. Gindele, S. Brechtel, and R. Dillmann, A probabilistic model for estimating driver behaviors and vehicle trajectories in trac environments, Intelligent Transportation Systems (ITSC), p.16251631, 2010.

S. Glaser, B. Vanholme, S. Mammar, D. Gruyer, and L. Nouveliere, Maneuver-based trajectory planning for highly autonomous vehicles on real road with trac and driver interaction, IEEE Transactions on Intelligent Transportation Systems, vol.11, issue.3, p.589606, 2010.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, 2016.

C. Götte, M. Keller, C. Rösmann, T. Nattermann, C. Haÿ et al., A real-time capable model predictive approach to lateral vehicle guidance, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), p.19081913, 2016.

R. Graf, H. Deusch, M. Fritzsche, and K. Dietmayer, A learning concept for behavior prediction in trac situations, Intelligent Vehicles Symposium (IV), 2013 IEEE, 2013.

Y. Granjon, Automatique -systèmes linéaires, non linéaires, à temps continu, à temps discret, représentation d'état, événements discrets, 2015.

H. F. Grip, L. Imsland, T. A. Johansen, T. I. Fossen, J. C. Kalkkuhl et al., Nonlinear vehicle side-slip estimation with friction adaptation, Automatica, vol.44, issue.3, pp.611-622, 2008.

T. Gu and J. M. Dolan, Toward human-like motion planning in urban environments, IEEE Intelligent Vehicles Symposium Proceedings, p.350355, 2014.

J. Guldner, W. Sienel, H. Tan, J. Ackermann, S. Patwardhan et al., Robust automatic steering control for look-down reference systems with front and rear sensors, IEEE transactions on control systems technology, vol.7, issue.1, p.211, 1999.

M. T. Hagan, H. B. Demuth, M. H. Beale, and O. De-jesús, Neural network design, vol.20, 1996.

D. L. Hall and J. Llinas, An introduction to multisensor data fusion, Proceedings of the IEEE, vol.85, issue.1, p.623, 1997.

N. Hamzah, Y. Sam, H. Selamat, M. Aripin, and M. Ismail, Yaw stability improvement for four-wheel active steering vehicle using sliding mode control, 2012.

G. Han, W. Fu, and W. Wang, The study of intelligent vehicle navigation path based on behavior coordination of particle swarm, Computational intelligence and neuroscience, 2016.

L. Han, H. Yashiro, H. T. Nejad, Q. H. Do, and S. Mita, Bezier curve based path planning for autonomous vehicle in urban environment, 2010 IEEE Intelligent Vehicles Symposium, p.10361042, 2010.

P. E. Hart, N. J. Nilsson, and B. Raphael, A formal basis for the heuristic determination of minimum cost paths, IEEE transactions on Systems Science and Cybernetics, vol.4, issue.2, p.100107, 1968.

C. Hatipoglu, U. Ozguner, and K. A. , Automated lane change controller design, IEEE transactions on intelligent transportation systems, vol.4, issue.1, p.1322, 2003.

X. He, D. Xu, H. Zhao, M. Moze, F. Aioun et al., A human-like trajectory planning method by learning from naturalistic driving data, IEEE Intelligent Vehicles Symposium (IV), p.339346, 2018.

C. Hermes, J. Einhaus, M. Hahn, C. Wöhler, and F. Kummert, Vehicle tracking and motion prediction in complex urban scenarios, 2010 IEEE Intelligent Vehicles Symposium, p.2633, 2010.

M. Himmelsbach, T. Luettel, F. Hecker, F. Von-hundelshausen, and H. Wuensche, Autonomous o-road navigation for mucar-3, vol.25, p.145149, 2011.

A. Houenou, P. Bonnifait, V. Cherfaoui, and W. Yao, Vehicle trajectory prediction based on motion model and maneuver recognition, IEEE/RSJ International Conference on Intelligent Robots and Systems, p.43634369, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00881100

R. A. Howard, Dynamic programming and markov processes, 1960.

Y. J. Hsu, S. M. Laws, and J. C. Gerdes, Estimation of tire slip angle and friction limits using steering torque, IEEE Transactions on Control Systems Technology, vol.18, issue.4, p.896907, 2010.

C. Hu, H. Jing, R. Wang, F. Yan, and M. Chadli, Robust H ? output-feedback control for path following of autonomous ground vehicles, Mechanical Systems and Signal Processing, vol.70, p.414427, 2016.

J. Huang and H. Tan, Vehicle future trajectory prediction with a dgps/ins-based positioning system, American Control Conference, p.6, 2006.

C. Hubmann, M. Aeberhard, and C. Stiller, A generic driving strategy for urban environments, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), p.10101016, 2016.

J. Hwan-jeon, S. Karaman, and E. Frazzoli, Anytime computation of time-optimal o-road vehicle maneuvers using the rrt*, 50th IEEE Conference on Decision and Control and European Control Conference, p.32763282, 2011.

S. , Optimisation de la navigation robotique, 2016.

S. J. Julier and J. K. Uhlmann, New extension of the kalman lter to nonlinear systems. In Signal processing, sensor fusion, and target recognition VI, International Society for Optics and Photonics, vol.3068, p.182194, 1997.

L. P. Kaelbling, M. L. Littman, and A. R. Cassandra, Planning and acting in partially observable stochastic domains, Articial intelligence, vol.101, issue.1-2, p.99134, 1998.

E. Käfer, C. Hermes, C. Wöhler, H. Ritter, and F. Kummert, Recognition of situation classes at road intersections, 2010 IEEE International Conference on Robotics and Automation, p.39603965, 2010.

R. Kala and K. Warwick, Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes. Engineering Applications of Articial Intelligence, vol.26, p.15881601, 2013.

R. E. Kalman, A new approach to linear ltering and prediction problems, Journal of basic Engineering, vol.82, issue.1, p.3545, 1960.

S. Karaman and E. Frazzoli, Incremental sampling-based algorithms for optimal motion planning, 2010.

D. Kasper, G. Weidl, T. Dang, G. Breuel, A. Tamke et al., Objectoriented bayesian networks for detection of lane change maneuvers, IEEE Intelligent Transportation Systems Magazine, vol.4, issue.3, p.1931, 2012.

C. Katrakazas, M. Quddus, W. Chen, and L. Deka, Real-time motion planning methods for autonomous on-road driving : State-of-the-art and future research directions, Transportation Research Part C : Emerging Technologies, vol.60, p.416442, 2015.

M. Keller, F. Homann, C. Hass, T. Bertram, and A. Seewald, Planning of optimal collision avoidance trajectories with timed elastic bands, IFAC Proceedings Volumes, vol.47, p.98229827, 2014.

M. Khatib, H. Jaouni, R. Chatila, and J. Laumond, Dynamic path modication for carlike nonholonomic mobile robots, Proceedings of International Conference on Robotics and Automation, vol.4, p.29202925, 1997.

O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots, Autonomous robot vehicles, p.396404, 1986.

U. Kiencke and A. Daiÿ, Observation of lateral vehicle dynamics, Control Engineering Practice, vol.5, issue.8, p.11451150, 1997.

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), p.399404, 2017.

K. Kim and P. R. Kumar, An mpc-based approach to provable system-wide safety and liveness of autonomous ground trac, IEEE Transactions on Automatic Control, vol.59, issue.12, p.33413356, 2014.

J. Kong, M. Pfeier, G. Schildbach, and F. Borrelli, Kinematic and dynamic vehicle models for autonomous driving control design, 2015 IEEE Intelligent Vehicles Symposium (IV), p.10941099, 2015.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classication with deep convolutional neural networks, Advances in neural information processing systems, p.10971105, 2012.

M. Kuderer, S. Gulati, and W. Burgard, Learning driving styles for autonomous vehicles from demonstration, 2015 IEEE International Conference on Robotics and Automation (ICRA), p.26412646, 2015.

Y. Kuwata, J. Teo, G. Fiore, S. Karaman, E. Frazzoli et al., Real-time motion planning with applications to autonomous urban driving, IEEE Transactions on Control Systems Technology, vol.17, issue.5, p.11051118, 2009.

F. Lamiraux, D. Bonnafous, and O. Lefebvre, Reactive path deformation for nonholonomic mobile robots, IEEE transactions on robotics, vol.20, issue.6, p.967977, 2004.
URL : https://hal.archives-ouvertes.fr/hal-02294191

J. Latombe, Robot motion planning, vol.124, 2012.

S. M. Lavalle, Rapidly-exploring random trees : A new tool for path planning, 1998.

A. Lawitzky, D. Altho, C. F. Passenberg, G. Tanzmeister, D. Wollherr et al., Interactive scene prediction for automotive applications, 2013 IEEE Intelligent Vehicles Symposium (IV), p.10281033, 2013.

S. Lefèvre, D. Vasquez, and C. Laugier, A survey on motion prediction and risk assessment for intelligent vehicles, ROBOMECH journal, vol.1, issue.1, p.1, 2014.

R. D. Leighty, DARPA ALV (Autonomous Land Vehicle) Summary, 1986.

D. J. Leith and W. E. Leithead, Survey of gain-scheduling analysis and design, International journal of control, vol.73, issue.11, p.10011025, 2000.

G. Li, S. E. Li, Y. Liao, W. Wang, B. Cheng et al., Lane change maneuver recognition via vehicle state and driver operation signalsresults from naturalistic driving data, Intelligent Vehicles Symposium (IV), p.865870, 2015.

J. Li and J. Zhang, Vehicle sideslip angle estimation based on hybrid kalman lter. Mathematical Problems in Engineering, 2016.

X. Li, X. Song, and C. Chan, Reliable vehicle sideslip angle fusion estimation using low-cost sensors, Measurement, vol.51, p.241258, 2014.

M. Likhachev, G. J. Gordon, and S. Thrun, Ara* : Anytime a* with provable bounds on sub-optimality, Advances in neural information processing systems, p.767774, 2004.

P. Liu and A. Kurt, Trajectory prediction of a lane changing vehicle based on driver behavior estimation and classication, Intelligent Transportation Systems (ITSC), p.942947, 2014.

D. Luenberger, An introduction to observers, IEEE Transactions on automatic control, vol.16, issue.6, p.596602, 1971.

P. Lytrivis, G. Thomaidis, M. Tsogas, and A. Amditis, An advanced cooperative path prediction algorithm for safety applications in vehicular networks, IEEE Transactions on Intelligent Transportation Systems, vol.12, issue.3, p.669679, 2011.

S. Mammar, N. M. Enache, S. Glaser, B. Lusetti, and A. B. Neto, Lane keeping automation at tire saturation, Proceedings of the 2010 American Control Conference, pp.6466-6471, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00869539

Y. Mansour, Pessimistic decision tree pruning based on tree size, MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE, p.195201

. Citeseer, , 1997.

R. Marino, S. Scalzi, and M. Netto, Nested PID steering control for lane keeping in autonomous vehicles, Control Engineering Practice, vol.19, issue.12, p.14591467, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00913069

M. Mcnaughton, C. Urmson, J. M. Dolan, and J. Lee, Motion planning for autonomous driving with a conformal spatiotemporal lattice, Robotics and Automation (ICRA), 2011 IEEE International Conference on, p.48894895, 2011.

S. Melzi and E. Sabbioni, On the vehicle sideslip angle estimation through neural networks : Numerical and experimental results, Mechanical Systems and Signal Processing, vol.25, issue.6, 2011.

R. Miller and Q. Huang, An adaptive peer-to-peer collision warning system, Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring, vol.1, p.317321, 2002.

M. Montemerlo, J. Becker, S. Bhat, H. Dahlkamp, D. Dolgov et al., The stanford entry in the urban challenge, Journal of eld Robotics, vol.25, issue.9, p.569597, 2008.

A. Morand, Commande assistée au conducteur basée sur la conduite en formation de type "banc de poissons, 2014.

A. Morand, X. Moreau, P. Melchior, M. Moze, and F. Guillemard, CRONE cruise control system, IEEE Transactions on Vehicular Technology, vol.65, issue.1, p.1528, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01712504

B. Morris, A. Doshi, and M. Trivedi, Lane change intent prediction for driver assistance : On-road design and evaluation, 2011 IEEE Intelligent Vehicles Symposium (IV), p.895901, 2011.

H. Mouhagir, V. Cherfaoui, R. Talj, F. Aioun, and F. Guillemard, Using evidential occupancy grid for vehicle trajectory planning under uncertainty with tentacles, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), p.17
URL : https://hal.archives-ouvertes.fr/hal-01576974

, IEEE, 2017.

B. Nagy and A. Kelly, Trajectory generation for car-like robots using cubic curvature polynomials, Field and Service Robots, vol.11, 2001.

G. Naus, J. Ploeg, M. Van-de-molengraft, W. Heemels, and M. Steinbuch, Design and implementation of parameterized adaptive cruise control : An explicit model predictive control approach, Control Engineering Practice, vol.18, issue.8, p.882892, 2010.

N. J. Nilsson and ;. Center, A mobile automaton : An application of articial intelligence techniques, 1969.

M. Nolte, M. Rose, T. Stolte, and M. Maurer, Model predictive control based trajectory generation for autonomous vehiclesan architectural approach, 2017 IEEE Intelligent Vehicles Symposium (IV), p.798805, 2017.

R. T. O'brien, P. A. Iglesias, and T. J. Urban, Vehicle lateral control for automated highway systems, IEEE Transactions on Control Systems Technology, vol.4, issue.3, p.266273, 1996.

J. J. Oh and S. B. Choi, Vehicle velocity observer design using 6-d imu and multipleobserver approach, IEEE Transactions on Intelligent Transportation Systems, vol.13, issue.4, p.18651879, 2012.

S. Oh, J. Lee, and D. Choi, A new reinforcement learning vehicle control architecture for vision-based road following, IEEE Transactions on Vehicular Technology, vol.49, issue.3, p.9971005, 2000.

M. G. Ortiz, J. Fritsch, F. Kummert, and A. Gepperth, Behavior prediction at multiple time-scales in inner-city scenarios, 2011 IEEE Intelligent Vehicles Symposium (IV), p.10681073, 2011.

A. Oustaloup, La commande crone : commande robuste d'ordre non entier, 1991.

A. Oustaloup and B. Dubuisson, Diversity and non-integer dierentiation for system dynamics, 2014.

A. Oustaloup, O. Cois, and L. Lay, Représentation et identication par modèle non entier, 2005.

H. Pacejka, Tire and vehicle dynamics, 2005.

J. Park and Y. Tai, A simulation based method for vehicle motion prediction, Computer Vision and Image Understanding, vol.136, p.7991, 2015.

R. Parthasarathi and T. Fraichard, An inevitable collision state-checker for a car-like vehicle, Proceedings 2007 IEEE International Conference on Robotics and Automation, p.30683073, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00134471

J. Pérez, V. Milanés, T. D. Pedro, and L. Vlacic, Autonomous driving manoeuvres in urban road trac environment : a study on roundabouts, IFAC Proceedings Volumes, vol.44, p.1379513800, 2011.

J. Pérez, V. Milanés, and E. Onieva, Cascade architecture for lateral control in autonomous vehicles, IEEE Transactions on Intelligent Transportation Systems, vol.12, issue.1, p.7382, 2011.

B. Perrin, La voiture sans accident, Auto Journal, vol.895, p.3234, 2013.

M. Pivtoraiko and A. Kelly, Generating near minimal spanning control sets for constrained motion planning in discrete state spaces, IEEE/RSJ International Conference on Intelligent Robots and Systems, p.32313237, 2005.

J. R. Quinlan, Induction of decision trees, Machine learning, vol.1, issue.1, p.81106, 1986.

S. Quinlan and O. Khatib, Elastic bands : Connecting path planning and control, Proceedings IEEE International Conference on Robotics and Automation, p.802807, 1993.

, IEEE, 1993.

M. Raharijaona, M. Duc, and M. Mammar, Linear parameter-varying control and Hinnity synthesis dedicated to lateral driving assistance, IEEE Intelligent Vehicles Symposium, p.407412, 2004.

R. Rajamani, Vehicle dynamics and control, 2011.

Y. Rasekhipour, A. Khajepour, S. Chen, and B. Litkouhi, A potential eld-based model predictive path-planning controller for autonomous road vehicles, IEEE Transactions on Intelligent Transportation Systems, vol.18, issue.5, p.12551267, 2017.

L. R. Ray, Nonlinear state and tire force estimation for advanced vehicle control, IEEE Transactions on Control Systems Technology, vol.3, issue.1, p.117124, 1995.

C. Raymond, Décodage conceptuel : co-articulation des processus de transcription et compréhension dans les systèmes de dialogue, 2005.

J. Receveur, S. Victor, and P. Melchior, Multi-criteria trajectory optimization for autonomous vehicles, IFAC-PapersOnLine, vol.50, issue.1, p.1252012525, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01703957

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You Only Look Once : Unied, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, p.779788, 2016.

T. Ren, T. Chen, and C. Chen, Motion control for a two-wheeled vehicle using a self-tuning pid controller, Control Engineering Practice, vol.16, issue.3, p.365375, 2008.

P. Resende and F. Nashashibi, Real-time dynamic trajectory planning for highly automated driving in highways, Intelligent Transportation Systems (ITSC), p.653658, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00533487

A. Rezaeian, R. Zarringhalam, S. Fallah, W. Melek, A. Khajepour et al., Novel tire force estimation strategy for real-time implementation on vehicle applications, IEEE Transactions on Vehicular Technology, vol.64, issue.6, p.22312241, 2015.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by backpropagating errors, Cognitive modeling, vol.5, issue.3, p.1, 1988.

K. Saadeddin, M. F. Abdel-hafez, M. A. Jaradat, and M. A. Jarrah, Performance enhancement of low-cost, high-accuracy, state estimation for vehicle collision prevention system using ans, Mechanical Systems and Signal Processing, vol.41, issue.1-2, p.239253, 2013.

A. Sathyanarayana, P. Boyraz, and J. H. Hansen, Driver behavior analysis and route recognition by hidden markov models, Vehicular Electronics and Safety, p.276281, 2008.

A. Scheuer and T. Fraichard, Continuous-curvature path planning for car-like vehicles, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS'97, vol.2, pp.997-1003, 1997.
URL : https://hal.archives-ouvertes.fr/inria-00000018

S. M. Shinners, Modern control system theory and design, 1998.

M. Sokolova and G. Lapalme, A systematic analysis of performance measures for classication tasks, Information Processing & Management, vol.45, issue.4, p.427437, 2009.

A. Stentz, Optimal and ecient path planning for partially known environments, Intelligent Unmanned Ground Vehicles, p.203220, 1997.

J. Stephant, A. Charara, and D. Meizel, Contact roue-sol : comparaison de modèles d'eorts. Journées d'Etude : Automatique et Automobile, 2001.

J. Stéphant, A. Charara, and D. Meizel, Evaluation of a sliding mode observer for vehicle sideslip angle, Control Engineering Practice, vol.15, issue.7, p.803812, 2007.

R. S. Sutton and A. G. Barto, Introduction to reinforcement learning, vol.135, 1998.

G. Tagne, R. Talj, and A. Charara, Higher-order sliding mode control for lateral dynamics of autonomous vehicles, with experimental validation, 2013 IEEE Intelligent Vehicles Symposium (IV), p.678683, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00858299

T. Takagi and M. Sugeno, Fuzzy identication of systems and its applications to modeling and control, Readings in Fuzzy Sets for Intelligent Systems, p.387403, 1993.

W. Tan, H. J. Marquez, and T. Chen, Operating point selection in multimodel controller design, Proceedings of the 2004 American Control Conference, vol.4, pp.3652-3657, 2004.

F. Thau, Observing the state of non-linear dynamic systems, International journal of control, vol.17, issue.3, p.471479, 1973.

S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron et al., The robot that won the DARPA Grand Challenge, Journal of eld Robotics, vol.23, issue.9, p.661692, 2006.

K. Leung, J. F. Whidborne, D. Purdy, and A. Dunoyer, A review of ground vehicle dynamic state estimations utilising gps/ins. Vehicle System Dynamics, vol.49, p.2958, 2011.

Q. Tran and J. Firl, Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression, 2014 IEEE Intelligent Vehicles Symposium Proceedings, p.918923, 2014.

M. Tsogas, X. Dai, G. Thomaidis, P. Lytrivis, and A. Amditis, Detection of maneuvers using evidence theory, Intelligent Vehicles Symposium, p.126131, 2008.

, IEEE, 2008.

S. Tsugawa, Vision-based vehicles in Japan : Machine vision systems and driving control systems, IEEE Transactions on industrial electronics, vol.41, issue.4, p.398405, 1994.

S. Ulbrich and M. Maurer, Probabilistic online pomdp decision making for lane changes in fully automated driving, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), p.20632067, 2013.

, Convention on road trac, 1968.

, Rapport du groupe de travail de la sécurité et de la circulation routières sur sa soixante-huitième session, Avril, 2014.

. Ece/trans/wp, Genève. Accessed, vol.24, 2019.

, Rapport du groupe de travail de la sécurité et de la circulation routières sur sa soixante-quatorzième session, 2017.

. Ece/trans/wp, Genève. Accessed, vol.24, 2019.

C. Urmson, J. Anhalt, D. Bagnell, C. Baker, R. Bittner et al., Autonomous driving in urban environments : Boss and the urban challenge, Journal of Field Robotics, vol.25, issue.8, p.425466, 2008.

F. Von-hundelshausen, M. Himmelsbach, F. Hecker, A. Mueller, and H. Wuensche, Driving with tentacles : Integral structures for sensing and motion, Journal of Field Robotics, vol.25, issue.9, p.640673, 2008.

J. Wang and M. Tomizuka, Robust H ? lateral control of heavy-duty vehicles in automated highway system, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), vol.5, p.36713675, 1999.

G. Welch and G. Bishop, An introduction to the Kalman lter, 1995.

M. Wielitzka, M. Dagen, and T. Ortmaier, State estimation of vehicle's lateral dynamics using unscented kalman lter, 53rd IEEE Conference on Decision and Control, p.50155020, 2014.

M. T. Wolf and J. W. Burdick, Articial potential functions for highway driving with collision avoidance, 2008 IEEE International Conference on Robotics and Automation, p.37313736, 2008.

G. Xie, H. Gao, L. Qian, B. Huang, K. Li et al., Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models, IEEE Transactions on Industrial Electronics, vol.65, issue.7, p.59996008, 2018.

W. Xu, J. Wei, J. M. Dolan, H. Zhao, and H. Zha, A real-time motion planner with trajectory optimization for autonomous vehicles, 2012 IEEE International Conference on Robotics and Automation, p.20612067, 2012.

Z. Yacine, Observateurs pour l'estimation de la dynamique latérale du véhicule : application à la détection de situations critiques, 2016.

J. Yoon, S. E. Li, and C. Ahn, Estimation of vehicle sideslip angle and tire-road friction coecient based on magnetometer with gps, International journal of automotive technology, vol.17, issue.3, p.427435, 2016.

S. Yoon, S. Yoon, U. Lee, and D. H. Shim, Recursive path planning using reduced states for car-like vehicles on grid maps, IEEE Transactions on Intelligent Transportation Systems, vol.16, issue.5, p.27972813, 2015.

Y. Yoon, J. Shin, H. J. Kim, Y. Park, and S. Sastry, Model-predictive active steering and obstacle avoidance for autonomous ground vehicles, Control Engineering Practice, vol.17, issue.7, p.741750, 2009.

M. Zeitz, The extended Luenberger observer for nonlinear systems, Systems & Control Letters, vol.9, issue.2, p.149156, 1987.

B. Zhang, H. Du, J. Lam, N. Zhang, and W. Li, A novel observer design for simultaneous estimation of vehicle steering angle and sideslip angle, IEEE Transactions on Industrial Electronics, vol.63, issue.7, p.43574366, 2016.

L. Zhao, Z. Liu, and H. Chen, Design of a nonlinear observer for vehicle velocity estimation and experiments, IEEE Transactions on Control Systems Technology, vol.19, issue.3, p.664672, 2011.

J. Zheng, K. Suzuki, and M. Fujita, Predicting driver's lane-changing decisions using a neural network model. Simulation Modelling Practice and Theory, vol.42, p.7383, 2014.

J. Ziegler, M. Werling, and J. Schroder, Navigating car-like robots in unstructured environments using an obstacle sensitive cost function, IEEE Intelligent Vehicles Symposium, p.787791, 2008.

A. , Outils pour l'analyse fréquentielle des systèmes dynamiques x ? R n , le vecteur d'état

L. Y-?-r-q and . Vecteur-de-sortie,

L. U-?-r-p and . Vecteur-de-commande,

, A(t) ? R nxn , la matrice d'état

. B(t)-?-r-nxp,

, C(t) ? R qxn , la matrice d'observation

, D(t) ? R qxp , la matrice d'action directe

, La représentation d'état est donc une représentation matricielle de l'évolution physique d'un système. À partir de cette représentation d'état, il est possible de calculer la fonction de transfert qui permettra l

, A.1.4 Fonction de transfert

, Contrairement à la représentation d'état qui modélise l'état physique interne d'un système physique, la fonction de transfert représente les liens entre une entrée et une sortie de ce système. La fonction de transfert peut s'exprimer à partir de la représentation d'état

, H(s) = C(sI n ? A) ?1 B + D

, H(s) est une matrice de transfert à qxp dimensions

, Il est à noter que la fonction de transfert ne tient pas compte des conditions aux limites en supposant les conditions initiales nulles

, Soit H(s) = N um(s) Den(s) une fonction de transfert rationnelle, avec N um(s) et Den(s) les

. Fonctions-de-laplace-du-numérateur, Les pôles de H(s) sont les racines de Den(s) = 0 et les zéros de H(s) sont les racines de N um(s) = 0. À partir des pôles de la fonction de transfert

, Un système représenté par une fonction de transfert H(s) est stable EBSB (Entrée Bornée / Sortie Bornée) si les pôles de H(s) sont à partie réelle strictement négative

A. , Diagramme de Bode À partir de la fonction de transfert du système, il devient possible de représenter graphiquement son comportement dans le domaine fréquentiel avec un diagramme de Bode

D. Le-diagramme, Bode d'amplitude trace le gain de la fonction de transfert en décibel en fonction de la fréquence angulaire. Pour une fonction de transfert H(s), il est calculé par H dB (?) =

D. Le-diagramme, Bode de phase trace la phase de la fonction de transfert en degré en fonction de la fréquence angulaire. Pour une fonction de transfert H(s)

, Pour un Citroën C4 Picasso, la masse totale autorisée en charge est de 2100 kg et la masse à vide de 1500 kg. Ainsi, pour évaluer l'inuence de la masse sur la dynamique latérale du véhicule, les congurations suivantes de répartition de masse sont utilisées

L. , observateur est de trouver l'estimation d'état de telle sorte que l'erreur d'estimation tende vers 0, c'est-à-dire

, Or, en utilisant les équations (C.1) et (C.3), il vient : x(t) ??(t) = (A ? LC) (x(t) ? x(t))

. Le-ltre-de-kalman, innie permettant d'estimer l'état d'un système à partir d'une série de mesures. Ces mesures peuvent être incomplètes ou bruitées. Ce ltre est de loin le plus utilisé dans l'industrie automobile. Dans l'utilisation d'observateur à modèle linéaire, l'observateur de Kalman est adapté au modèle LPV et donc approprié au modèle linéaire de la dynamique latérale du véhicule décrit dans la section précédente. La forme discrète du ltre de Kalman est présentée en vue d'une utilisation embarquée. La notation discrète des équations suivantes utilise l'indice k pour l

, Les représentations d'état continue et discrète sont liées par les équations

, Un modèle non linéaire, comme son nom l'indique, n'établit pas de relation linéaire entre tous les états d'un système

, Le Chapitre 3 présente une méthode de multirégulation, basé sur des régulateurs de type PID, pour le guidage latérale d'un véhicule autonome. La méthode permet de choisir les points de fonctionnement du régulateur : leur nombre et leurs valeurs. Pour cela, la méthode fait en sorte que la variation de phase du procédé soit constante entre chaque point de fonctionnement

, Cette annexe présente l'inuence de ce découpage pour le calcul du multirégulateur sur la boucle ouverte. Le procédé utilisé est celui de la dynamique latérale et plusieurs multi-PID sont calculés avec des découpages diérents

D. , Les types de découpages possibles Le système à réguler peut s'écrire par la représentation d

=. Cx,

, ) la commande, y(t) la sortie et A(?(t)), B(?(t)), C les matrices d'état. Les deux premières matrices dépendent d'un paramètre variant dans le temps ?(t). Le paramètre ? peut varier sur l'intervalle

L. Ici,

?. Op, le nombre de points de fonctionnement, ? ? i : les valeurs du paramètres variant aux points de fonctionnement

, Il y a deux manières de découper l'intervalle [? min , ? max ] pour déterminer les points de fonctionnement : ? Variation constante du paramètre entre deux points de fonctionnement : ? i+1 ? ? i = Var ?

, ? Variation constance de la phase du procédé, à la fréquence au gain unité ? u , entre deux points de fonctionnement : arg G(j? u )| ? i+1 ? arg G