Abstract
In this paper, evolving Takagi-Sugeno (eTS) fuzzy driver model is proposed for simultaneous lateral and longitudinal control of a vehicle in a test track closed to traffic. The developed eTS fuzzy driver model can capture human operator’s driving expertise for generating desired steering angle, throttle angle and brake pedal command values by processing only information which can be supplied by the vehicle’s on-board control systems in real time. Apart from other fuzzy rule based (FRB) models requiring human expert knowledge or off-line clustering, the developed eTS driver model can adapt itself automatically, even ‘from scratch’, by an on-line learning process using eTS algorithm while human driver is supervising the vehicle. Proposed eTS fuzzy driver model’s on-line human driver identification capability and autonomous vehicle driving performance were evaluated on real road profiles created by digitizing two different intercity express ways of Turkey in IPG© CarMaker® software. The training and validation simulation results demonstrated that eTS fuzzy driver model can be used in product development phase to speed up different tests via realistic simulations. Furthermore eTS fuzzy driver model has an application potential in the field of autonomous driving.
Similar content being viewed by others
References
Akita, T., Inagaki, S., Suzuki, T., Hayakawa, S. and Tsuchida, N. (2007). Analysis of vehicle following behavior of human driver based on hybrid dynamical system model. 16th IEEE Int. Conf. Control Applications Part of IEEE Multi-Conf. Systems and Control, Singapore.
Angelov, P. and Filev, D. (2004). An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Trans. Systems, Man, and Cybernetics, 34, 484–498.
Angelov, P. and Filev, D. (2005). Simpl_eTS: A simplified method for learning evolving Takagi-Sugeno fuzzy models. Proc. 2005 IEEE Int. Conf. Fuzzy Systems, 1068–1073.
Angelov, P., Xydeas, C. and Filev, D. (2004). On-line identification of MIMO evolving Takagi Sugeno models. IJCNN-FUZZ-IEEE, Budapest, Hungary.
Arslan, H. (2009). Critical appraisal of Turkish and American standards for the design of rural highway links with the comparison of British approach. TEKNOLOJI 12, 1, 13–24.
Delice, I. (2005). Human Driver Modelling For Lateral & Longitudinal Control of a Vehicle. M.S. Thesis. Istanbul Technical University. Turkey.
Filev, D., Lu, J., Tseng, F. and Prakah-Asante, K. (2011). Real-time driver characterization during car following using stochastic evolving models. IEEE Int. Conf. Systems, Man, and Cybernetics, Anchorage, AK.
Fritz, H. (1996). Model-based neural distance control for autonomous road vehicles. Proc. Intelligent Vehicles Symp., 29–34.
Guo, K., Ding, H., Zhang, J., Lu, J. and Wang, R. (2004). Development of a longitudinal and lateral driver model for autonomous vehicle control. Int. J. Vehicle Design 36, 1, 50–65.
ai]Gwak, M., Jo, K. and Sunwoo, M. (2013). Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles. Int. J. Automotive Technology 14, 2, 265–274.
Hessburg, T. and Tomizuka, M. (1994). Fuzzy logic control for lateral vehicle guidance. Control Systems Magazine, 14, 55–63.
Ho, M. L., Chan, P. T., Rad, A. B., Shirazi, M. and Cina, M. (2012). A novel fused neural network controller for lateral control of autonomous vehicles. Applied Soft Computing, 12, 3514–3525.
Holzmann, H., Halfmann, C., Germann, S., Würtenberger, M. and Bermann, R. (1997). Longitudinal and lateral control and supervision of autonomous intelligent vehicles. Control Eng. Practice 5, 11, 1599–1605.
Huang, S. and Ren, W. (1999). Use of neural fuzzy networks with mixed genetic/gradient algorithm in automated vehicle control. IEEE Trans. Industrial Electronics 46, 6, 1090–1101.
IPG Automotive GmbH (2011). CarMaker® Reference Manual. IPG Automotive GmbH.
Khodayari, A., Ameli, S., Ghaffari, A. and Flahatgar, J. (2010). A historical review on lateral and longitudinal control of autonomous vehicle motions. 2010 Int. Conf. Mechanical and Electrical Technology, Singapore.
Kodagoda, K. R. S., Wijesoma, W. S. and TeohI, E. K. (2002). Fuzzy speed and steering control of an AGV. EEE Trans. Control Systems Technology 10, 1, 112–120.
Kumarawadu, S. and Lee, T. T. (2006). Neuroadaptive combined lateral and longitudinal control of highway vehicles using RBF networks. IEEE Trans. Intelligent Transportation Systems 7, 4, 500–512.
Kuriyagawa, Y., Im, H. E., Kageyama, I. and Onishi, S. (2002). A research on analytical method of driver-vehicle-environement system for construction of intelligent driver support system. Vehicle System Dynamics 37, 5, 339–358.
Lee, G. D., Kim, S. W., Yim, Y. U., Jung, J. H., Oh, S. Y. and Kim, B. S. (1999). Longitudinal and lateral control system development for a platoon of vehicles. IEEE/IEEJ/JSAI Int. Conf. Intelligent Transportation Systems, Tokyo.
Lee, T., Kang, J., Yi, K., Noh, K. and Lee, K. (2010). Integration of longitudinal and lateral human driver models for evaluation of the vehicle active safety systems. SAE Paper No. 2010-01-0084.
Lin, Y., Tang, P., Zhang, W. J. and Yu, Q. (2005). Artificial neural network modelling of driving handling behaviour in a driver-vehicle-environment system. Int. J. Vehicle Design, 37, 24–45.
MacAdam, C. and Johnson, G. E. (1996). Application of elementary neural networks and preview sensors for representing driver steering control behaviour. Vehicle System Dynamics, 25, 3–30.
Milanés, V., Llorca, D. F., Villagrá, J., Pérez, J., Fernández, C., Parra, I., González, C. and Sotelo, M. A. (2012). Intelligent automatic overtaking system using vision for vehicle detection. Expert Systems with Applications, 39, 3362–3373.
Naranjo, J. E., Gonziilez, C., Garcia, R., Pedro, T. de, Revuelto, J. and Reviejo, J. (2004). Fuzzy logic based lateral control for GPS map tracking. 2004 IEEE Intelligent Vehicles Symp., University of Parma Parma, Italy.
Neusser, S., Nijhuis, J., Spaanenburg, L., Hoefflinger, B., Franke, U. and Fritz, H. (1993). Neurocontrol for lateral vehicle guidance. IEEE Micro 13, 1, 57–66.
Onieva, E., Godoy, J., Villagrá, J., Milanés, V. and Pérez, J. (2013). On-line learning of a fuzzy controller for a precise vehicle cruise control system. Expert Systems with Applications, 40, 1046–1053.
Onieva, E., Naranjo, J. E., Milanés, V., Alonso, J., García, R. and Pérez, J. (2011). Automatic lateral control for unmanned vehicles via genetic algorithms. Applied Soft Computing, 11, 1303–1309.
Pasquier, M. and Oentaryo, R. J. (2008). Learning to drive the human way a step towards intelligent vehicles. Int. J. Vehicle Autonomous Systems 6, ½, 24–47.
Perez, D., Saez, Y., Recio, G. and Isasi, P. (2008). Evolving a rule system controller for automatic driving in a car racing competition. IEEE Symp. Computational Intelligence and Games, Univ. of Madrid, Leganes, Spain.
Pilutti, T. and Ulsoy, A. G. (1999). Identification of driver state for lane-keeping tasks. IEEE Trans. Systems, Man, and Cybernetics-Part A: Systems and Humans 29, 5, 486–502.
Plöchl, M. and Edelmann, J. (2007). Driver models in automobile dynamics application. Vehicle System Dynamics 45, 7–8, 699–741.
Ress, C., Balzer, D., Bracht, A., Durekovic, S. and Löwenau, J. (2008). Adasis protocol for advanced invehicle applications. ITS World Cong., New York.
Shin, Y., Kim, Y., Choi, Y., Yoon, S. and Lee, M. H. (2011). Modified lateral control of an autonomous vehicle by look-ahead and look-down sensing. Int. J. Automotive Technology 12, 1, 103–110.
Ungoren, A. Y. and Peng, H. (2005). An adaptive lateral preview driver model. Vehicle System Dynamics 43, 4, 245–259.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Akca, S., Ertugrul, S. eTS fuzzy driver model for simultaneous longitudinal and lateral vehicle control. Int.J Automot. Technol. 15, 781–794 (2014). https://doi.org/10.1007/s12239-014-0082-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12239-014-0082-y