Identification of a driver’s starting intention based on an artificial neural network for vehicles equipped with an automated manual transmission

Abstract

The driver’s starting intention, which coordinates the engine output torque and the engagement speed of clutch for a vehicle equipped with an automated manual transmission, may be the key state for automated manual transmission clutch control. Fast and accurate identification of the starting intention can ensure a smooth clutch engagement and a smooth start of a vehicle. In this paper, a novel method based on an artificial error back-propagation neural network is proposed to identify the driver’s starting intention. By analysis of the experimental data, the driver’s starting intention can be defined strictly and divided into three modes: a slow start, a medium start and a fast start. The statistical regularity of the acceleration pedal opening is obtained on the basis of a novel method for processing the experimental data. Because in the first period of time in a starting process, the time proportion of the acceleration pedal opening over a certain value is closely related to the driver’s starting intention, therefore, this statistical regularity of the acceleration pedal opening is regarded as the input of the neural network, and the Broyden–Fletcher–Goldfarb–Shanno algorithm is applied to train the neural network. The real-vehicle test results with different drivers show that the identification accuracy of the driver’s starting intention is greater than 95% during the first 600 ms with the proposed artificial error back-propagation neural network. This can provide a reasonable quantization method of the driver’s starting intention for smooth automated manual transmission clutch control.

Keywords

Starting intention automated manual transmission artificial neural network Broyden–Fletcher–Goldfarb–Shanno algorithm

Get full access to this article

View all access options for this article.

References

Yang

Zhang

. Correctional DP-based energy management strategy of plug-in hybrid electric bus for city-bus-route. IEEE Trans Veh Technol 2014; 64(7): 2792–2803.

Yang

. Design and simulation of fuzzy control strategy for parallel hybrid electric vehicle. In: 2010 IEEE international conference on intelligent system design and engineering application, Changsha, People’s Republic of China, 13–14 October 2010, Vol 1, pp. 539–543. New York: IEEE.

Yang

Jiao

. Electromechanical coupling driving control for single-shaft parallel hybrid powertrain. Sci China Technol Sci 2014; 57(3): 541–549.

Dai

Zhang

. Optimal control of dry clutch engagement based on the driver’s starting intentions. Proc IMechE Part D: J Automobile Engineering 2012; 226(8): 1048–1057.

Gao

Chen

. Improved optimal controller for start-up of amt trucks in consideration of driver’s intentions. Int J Automot Technol 2013; 14(2): 213–220.

Yang

Jiao

. Robust coordinated control for hybrid electric bus with single-shaft parallel hybrid powertrain. IET Control Theory Applic 2014; 9(2): 270–282.

Zhu

Jin

. Real-time yaw rate prediction based on a non-linear model and feedback compensation for vehicle dynamics control. Proc IMechE Part D: J Automobile Engineering 2013; 227(10):1431–1445.

Dai

Zhang

T L

. Modelling and recognition of the driver’s starting intentions. Proc IMechE Part D: J Automobile Engineering 2012; 226(5): 623–633.

You

Wang

. Handling characteristics of drivers under braking conditions considering the fluctuation in the steering-wheel torque. Proc IMechE Part D: J Automobile Engineering 2015; 229(4): 473–485.

10.

Ding

Dang

Wang

. Driver intention recognition method based on comprehensive lane-change environment assessment. In: 2014 IEEE intelligent vehicles symposium, Dearborn, Michigan, USA, 8–11 June 2014, pp. 214–220. New York: IEEE.

11.

Mizushima

Raksincharonensak

Nagai

. Direct yaw-moment control adapted to driver behavior recognition. In: SICE–ICASE international joint conference, Busan, Republic of Korea, 18–21 October 2006, pp. 534–539. New York: IEEE.

12.

Berndt

Emmert

Dietmayer

. Continuous driver intention recognition with hidden Markov models. In: 11th international IEEE conference on intelligent transportation systems, Beijing, People’s Republic of China, 12–15 October 2008, pp. 1189–1194. New York: IEEE.

13.

Yamada

Matsuyama

Uchida

. A method for analyzing interaction of driver intention through vehicle behavior when merging. In: 2014 IEEE intelligent vehicles symposium, Dearborn, Michigan, USA, 8–11 June 2014, pp. 158–163. New York: IEEE.

14.

Wang

Feng

. Driving intention inference based on dynamic Bayesian networks. In: Wen

(eds) Practical applications of intelligent systems, Advances in Intelligent Systems and Computing, Vol 279. Berlin: Springer, 2014, pp. 1109–1119.

15.

Weidl

Madsen

Kasper

Breuel

. Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements. In: 2014 IEEE international symposium on intelligent control, Juan les Pins, France, 8–10 October 2014, pp. 1626–1631. New York: IEEE.

16.

Hou

Jin

Niu

. Driver intention recognition method using continuous hidden Markov model. Int J Comput Intell Systems 2011; 4(3): 386–393.

17.

Berndt

Dietmayer

. Driver intention inference with vehicle onboard sensors. In: 2009 IEEE international conference on vehicular electronics and safety, Pune, India, 11–12 November 2009, pp. 102–107. New York: IEEE.

18.

Qaddoumi

El-Hag

Saker

. Outdoor insulators testing using artificial neural network-based near-field microwave technique. IEEE Trans Instrumn Measmt 2014; 63(2): 260–266.

19.

Qasim

Khadkikar

. Application of artificial neural networks for shunt active power filter control. IEEE Trans Ind Informatics 2014; : 1765–1774.

20.

Cheah

. Adaptive neural network control of robot based on a unified objective bound. IEEE Trans Control Systems Technol 2014; 22(3): 1032–1043.

21.

Shinzato

Grassi

Osório

Wolf

. Fast visual road recognition | horizon detection using multiple artificial neural networks. In: 2012 IEEE intelligent vehicles symposium, Alcalá de Henares, Spain, 3–7 June 2012, pp. 1090–1095. New York: IEEE.

22.

Xie

Quan

Chen

. Control strategy of hybrid power system for fuel cell electric vehicle based on neural network optimization. In: 2008 IEEE international conference on automation and logistics, Qingdao, People’s Republic of China, 1–3 September 2008, pp. 753–757. New York: IEEE.

23.

Kosbatwar

Pathan

. Pattern association for character recognition by back-propagation algorithm using neural network approach. Int J Comput Sci Engng Surv 2012; 3(1): 127–134.

24.

Dahl

Deng

. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans Audio, Speech, Language Processing 2012; 20(1): 30–42.

25.

Guarneri

Rocca

Gobbi

. A neural-network-based model for the dynamic simulation of the tire/suspension system while traversing road irregularities. IEEE Trans Neural Networks 2008; 19(9): 1549–1563.

26.

Saini

Soni

. Artificial neural network based peak load forecasting using Levenberg–Marquardt and quasi-Newton methods. IEE Proc − Generation, Transmission Distribution 2002; 149(5): 578–584.

27.

Xia

J-H

Rusli

Kumta

. Feedforward neural network trained by BFGS algorithm for modeling plasma etching of silicon carbide. IEEE Trans Plasma Sci 2010; 38(2): 142–148.