Feed-forward multilayer neural network model for vehicle lateral guidance control

Advanced Robotics

Volumn 12, Issue 7-8, 1997, Pages 735-753

  Wang, Guo Wen ^a   Fujiwara, Naofumi ^a   Bao, Yue ^a  

^a KANAZAWA UNIVERSITY   (Japan)

Author keywords

Automobile; Mobile robot; Neural network; Vehicle; Vehicle lateral guidance

Indexed keywords

COMPUTER SIMULATION; FEEDFORWARD NEURAL NETWORKS; MOBILE ROBOTS; MULTILAYER NEURAL NETWORKS; THREE TERM CONTROL SYSTEMS;

VEHICLE LATERAL GUIDANCE CONTROL (VLGC);

INTELLIGENT VEHICLE HIGHWAY SYSTEMS;

EID: 0032687599     PISSN: 01691864     EISSN: 15685535     Source Type: Journal    
DOI: 10.1163/156855399X00117     Document Type: Article

References (21)

1. J. G. Bender. An overview of system studies of automated highway system, IEEE Trans. Vehicular Technol. VT-40(1). 82-99 (1991).
2. R. E. Fenton and R. J. Mayhan, Automated highway studies at the Ohio State University - an overview. IEEE Trans. Vehicular Technol. VT-40(1). 100-113 (1991).
3. H.-S. J. Tsao, R. W. Hall and B. E. Mayhan. Design options for operating automated highway systems, in: Proc. Vehicle Navigation and Information Systems Conf. Ottawa. Canada, pp. 494-500(1993).
4. P. Varaiya. Smart cars on smart roads: problems of control. IEEE Trans. Automatic Control 38 (2). 195-207 (1993).
5. J. K. Hedrick, M. Tomizuka and P. Varaiya. Control issues in automated highway systems, IEEE Control Syst. Mag. 14 (6). 21-33 (1994).
6. H. Peng and M. Tomizuka, Preview control for vehicle lateral guidance in highway automation. ASMEJ. Dyn. Syst. Meas. Control 115,679-686(1993).
7. T. Hessburg and M. Tomizuka. Fuzzy logic control lateral vehicle guidance, IEEE Control Syst. Mag. 14 (4), 55-63 (1994).
8. H. A. Pham, J. K. Hedric and M. Tomizuka. Combined lateral and longitudinal control of vehicle for automated highway system, in: Proc. American Control Conf. Baltimore, pp. 3157-3158 (1994).
9. M. A. Arain, R. Tribe, E. An and C. Harris. Action planning for the collision avoidance system using neural network, in: IEEE Proc. Intelligent Vehicle's 93 Symp. Tokyo. Japan, pp. 119-124 1993.
10. G. DcMuth and S. Springsteen. Obstacle avoidance using neural network, in: IEEE Proc. Symp. on Autonomous Uunderwater Vehicle Technology. Washington. DC, pp. 213-215 (1990).
11. J. Yuh. Learning control for underwater robotic vehicles. IEEE Control Syst. Mag. 14 (2). 39-47 2033.
12. W. Thomas Miller. III. Real-time neural network control of a biped walking robot. IEEE Control Syst. Mag. 14(1), 41-49 (1994).
13. T. Shibata and T. Fukuda. Hierarchical intelligent control for robotic motion. IEEE Trans. Neural Network 5 (5), 823-832 (1994).
14. D. E. Rumelhart, G. E. Hinton and R. J. Williams. Learning internal representations by error propagation. Parallel Distributed Processing 1, 318-362 (1986).
15. D. E. Rumelhart, G. E. Hinton and R. J. Williams. Learning representations by back-propagation errors. Nature 323.533-536 (1986).
16. M. Hagiwara. Novel back propagation algorithm for reduction of hidden units and acceleration of convergence using anificial selection, in: Proc. IJCNN. San Diego, pp. 625-630 (1990).
17. N. Murata, S. Yoshizawa and S. Amari, Network information criterion - determining the number of hidden units for an anificial neural network model, METR 92-05 Technical Reports. University of Tokyo (1992).
18. H. Akaikei, Information theory and an extension of maximum likelihood principle, in: Proc. 2nd Int. Symp. on Information Theory, pp. 267-281. Akademia Kiato, Budapest (1973).
19. G. Schwarz. Estimating the dimension of a model, Ann. Statistics 6 (2). 461-464 (1972).
20. J. Rissanen. Modeling by shortest data description. Automático 14 (2). 465-471 (1978).
21. B. Irie and S. Miyake, Capabilities of three-layered perceptrons, in: Proc. IEEE Int. Conf. on Neural Networks. pp. 641-648 (1988).