Hostname: page-component-76fb5796d-vfjqv Total loading time: 0 Render date: 2024-04-25T08:35:09.624Z Has data issue: false hasContentIssue false

Throttle and brake pedals automation for populated areas

Published online by Cambridge University Press:  11 December 2009

E. Onieva*
Affiliation:
Industrial Computer Science Department, Instituto de Automática Industrial (CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain
V. Milanés
Affiliation:
Industrial Computer Science Department, Instituto de Automática Industrial (CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain
C. González
Affiliation:
Industrial Computer Science Department, Instituto de Automática Industrial (CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain
T. de Pedro
Affiliation:
Industrial Computer Science Department, Instituto de Automática Industrial (CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain
J. Pérez
Affiliation:
Industrial Computer Science Department, Instituto de Automática Industrial (CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain
J. Alonso
Affiliation:
Industrial Computer Science Department, Instituto de Automática Industrial (CSIC), La Poveda-Arganda del Rey, 28500 Madrid, Spain
*
*Corresponding author. E-mail: onieva@iai.csic.es

Summary

Artificial intelligence techniques applied to control processes are particularly useful when the elements to be controlled are complex and can not be described by a linear model. A trade-off between performance and complexity is the main factor in the design of this kind of system. The use of fuzzy logic is specially indicated when trying to emulate such human control actions as driving a car. This paper presents a fuzzy system that cooperatively controls the throttle and brake pedals for automatic speed control up to 50km/h. It is thus appropriate for populated areas where driving involves constant speed changes, but within a range of low speeds because of traffic jams, road signs, traffic lights, etc. The system gets the current and desired speeds for the car and generates outputs to control the two pedals. It has been implemented in a real car, and tested in real road conditions, showing good speed control with smooth actions resulting in accelerations that are comfortable for the car's occupants.

Type
Article
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Eurobarometer, “Use of Intelligent Systems in Vehicles,” European Commission (2006).Google Scholar
2.White Paper, “European Transport Policy for 2010. Time to Decide,” European Commission (2001).Google Scholar
3.Real Decreto 1428/2003. Boletín Oficial del Estado (BOE). vol. 306 (Dec. 2003) pp. 45684 to 45772.Google Scholar
4.Laumond, J. P., ed., Robot Motion Planning and Control vol. 229. (Springer-Verlag, New York, NY, 1998).Google Scholar
5.Pomerleau, D. A., “ALVINN: An Autonomous Land Vehicle in a Neural Network,” In: Advances in Neural Information Processing Systems, vol. 1 (Morgan Kaufmann, San Mateo, CA, 1989).Google Scholar
6.Stafylopatis, A. and Blekas, K., “Autonomous vehicle navigation using evolutionary reinforcement learning,” Eur. J. Oper. Res. 108 (2), 306318 (1998).CrossRefGoogle Scholar
7.Driankov, D. and Saffioti, A., eds., Fuzzy Logic Techniques for Autonomous Vehicle Navigation (Springer-Verlag, New York, NY, 2001).CrossRefGoogle Scholar
8.Sugeno, M., Murofushi, T. and Mori, T., “Fuzzy algorithmic control of a model car by oral instructions,” Fuzzy Sets Syst. 32 (2), 207219 (1989).Google Scholar
9.Sugeno, M., Hirano, I., Nakamura, S. and Kotsu, S., “Development of an intelligent unmanned helicopter,” Proc. IEEE Int. Conf. Fuzzy Syst. 5, 3344 (1995).Google Scholar
10.Kim, H. M., Dickerson, J. and Kosko, B., “Fuzzy throttle and brake control for platoons of smart cars,” Fuzzy Sets Syst. 84 (3), 209234 (1996).CrossRefGoogle Scholar
11.Marsden, G., McDonald, M. and Brackstone, M.. “Towards an understanding of adaptive cruise control,” Transp. Res. C: Emerging Technol. 9 (1), 3351 (2001).CrossRefGoogle Scholar
12.Davis, L. C., “Effect of adaptive cruise control systems on traffic flow,” Phys. Rev. E. 69 (6), 066110 (2004).CrossRefGoogle ScholarPubMed
13.Piao, J. and McDonald, M., “Low Speed Car Following Behaviour from Floating Vehicle Data,” Proceedings of IEEE Intelligent Vehicles Symposium, Columbus, OH, USA (2003) pp. 462467.Google Scholar
14.Acarman, T., Yiting, L. and Ozguner, U., “Intelligent cruise control stop and go with and without communication,” Am. Control Conf. (2006).Google Scholar
15.Naranjo, J. E., Gonzalez, C., Garcia, R. and Pedro, T. de, “ACC+Stop&go maneuvers with throttle and brake fuzzy control,” IEEE Trans. Intell. Transp. Syst. 7 (2), 213225 (2006).CrossRefGoogle Scholar
16.Bing-Fei, W., Tsen-Wei, C., Jau-Woei, P., Hsin-Han, C., Chao-Jung, C., Tien-Yu, L., Shinq-Jen, W. and Tsu-Tian, L.. “Design and implementation of the intelligent stop and go system in smart car, TAIWAN iTS-1,” IEEE Int. Conf. Syst. Man Cybern. 3, 811 (2006).Google Scholar
17.Naranjo, J. E., Gonzalez, C., Garcia, R. and de Pedro, T.. “Cooperative throttle and brake fuzzy control for ACC+Stop&Go maneuvers,” IEEE Trans. Veh. Technol. 56 (4), 16231630 (2007).CrossRefGoogle Scholar
18.Mendel, J. M., “Fuzzy logic systems for engineering: A tutorial,” Proc. IEEE 83 (3), 345377 (1995).Google Scholar
19.Naranjo, J. E., González, C., Reviejo, J., García, R., de Pedro, T. and Sotelo, M. A., “Using fuzzy logic in automated vehicle control,” IEEE Intell. Syst. 22 (1), 2645 (2007).CrossRefGoogle Scholar
20.RTCM Special Committee no. 104, RTCM Recommended Standards for Differential NAVSTAR GPS Service, 1994, Arlington, VA: Radio Technical Commission Maritime Services. RTCM paper 194–193/SC104-STD.Google Scholar
21.García, R. and De Pedro, T.. “First Application of the ORBEX Coprocessor: Control of Unmanned Vehicles,” EUSFLAT-ESTYLF Joint Conference. Mathware and Soft Computing, Palma de Mallorca, Spain 7 (2–3), (2000) pp. 265273.Google Scholar
22.García Rosa, R. and Pedro, T. De. “Modeling a fuzzy coprocessor and its programming language,” Mathware Soft Comput. 5 (2–3), 167174 (1998).Google Scholar
23.Zadeh, L., “The concept of a linguistic variable and its application to approximate reasoning,” Inf. Sci. 8 (3), 199249 (1975).CrossRefGoogle Scholar
24.Mamdani, E. H., “Applications of fuzzy algorithms for simple dynamic plant,” Proc. IEEE 62 (12), 15851588 (1974).Google Scholar
25.Takagi, T. and Sugeno, M., “Fuzzy identification of systems and its applications to modeling and control,” IEEE Trans. Syst. Man Cybern. 15 (1), 116132 (1985).Google Scholar
26.Sugeno, M., “On stability of fuzzy systems expressed by fuzzy rules,” IEEE Trans. Fuzzy Syst. 7 (2), 201224 (1999).CrossRefGoogle Scholar
27.Naranjo, J. E., González, C., García, R., de Pedro, T. and Haber, R. E., “Power steering control architecture for automatic driving,” IEEE Trans. Intell. Transp. Syst. 6 (4), 406415 (2005).CrossRefGoogle Scholar
28.Salgado, P. and Cunha, J. B., “Greenhouse climate hierarchical fuzzy modeling,” Control Eng. Pract. 13 (5), 613628 (2005).CrossRefGoogle Scholar
29.Vachkov, G. and Fukuda, T., “Structured learning and decomposition of fuzzy models for robotic control applications,” J. Intell. Robot. Syst. 32 (1), 121 (2001).Google Scholar
30.Milanés, V., Naranjo, J. E., González, C., Alonso, J and de Pedro, T.. “Autonomous vehicle based on cooperative GPS and inertial systems,” Robotica 26, 627633 (2008).CrossRefGoogle Scholar