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

Zhang, Lipeng; Zhu, Zaobei; Wang, Xiangyu; Yang, Yiyong; Chao, Yang; Song, Jian

doi:10.1177/0954407015611294

Cited by 24 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhun et al [5] proposed a speed control strategy with excellent robustness. Li et al [6] trained neural network through Broyden Fltecher Goldfarb Shanno algorithm to achieve AMT stationary control. G. kong used displacement control method to optimize the gearshift process [7].…”

Section: Built Gearshift Model Of Amt Bymentioning

confidence: 99%

Variable Weight Coefficient Optimization of Gearshift Actuator With Direct-Driving Automated Transmission

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Direct-Driving Automated Mechanical Transmission (DAMT) gearshift actuator is a kind of electromagnetic linear actuator. In practical application, the actual gearshift force in each steady states is significantly different from the electromagnetic force model. For the optimization of gear shifting actuator with undetermined volume constraint, this paper presents a variable weight coefficient optimization method of electromagnetic linear actuator which equates the output forces under different steady states in the fitness function. These are intended to adjust the relationships among the optimized objects by using the fitness function to improve the overall optimization effect. In order to obtain the fitness value of each combination parameter accurately, finite element analysis (FEA) is adopted in this paper, to improve the efficiency of the algorithm, and a more efficient simulated annealing-particle swarm optimization algorithm (SA-PSO) is used by the proposed optimization method. Finally, experiment is carried out based on prototype of gearshift actuator to verify the optimization results. The results show that the maximum electromagnetic force and terminal electromagnetic force are both increased by more than 20%, and the volatility of gearshift force is reduced 26.4%, which ensures the structure compactness and fast response. The proposed method decreases the volatility by 27.5% compared with the constant weight coefficients and improves the gearshift force in each stage. INDEX TERMS DAMT, gearshift actuator, variable weight coefficient optimization, experiment.

show abstract

Section: Built Gearshift Model Of Amt Bymentioning

confidence: 99%

Variable Weight Coefficient Optimization of Gearshift Actuator With Direct-Driving Automated Transmission

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Driving is a process of operation and control by the driver. 16 During this process, the driver takes certain actions on the accelerator pedal and the brake pedal based on the operating state of the vehicle, the environment and his or her driving habits. In the driver–vehicle–road closed-loop system, the driver can be seen as an adaptive intelligent sensor.…”

Section: Modellingmentioning

confidence: 99%

A novel multi-parameter coordinated shift control strategy for an automated manual transmission based on fuzzy inference

Zhang

Zhao

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

For a vehicle equipped with an automatic transmission, the shift control strategy should reflect the driver’s intention in the dynamic performance and the economy performance of the vehicle. However, the driver’s intention is difficult to identify and involve in the shift strategy because of the complexity of driving environments, the diversity of powertrain parameters and the randomness of the driver’s behaviour. Therefore, in this paper, by considering a vehicle equipped with an automated manual transmission as the study object, a novel multi-parameter coordinated shift control strategy is proposed on the basis of identification of the driver’s intention. First, in order to predict the intention of the driver more effectively, the relative opening degree of the accelerator is defined on the basis of the dynamic analysis. Then, the characteristics of the driver’s expected acceleration, which involve the influence of the driving environment, are proposed. They can be classified into five categories, namely stop, deceleration, keep, acceleration and urgent acceleration. Next, a fuzzy control system is designed to identify the driver’s acceleration characteristics in real time. This considers the vehicle speed, the rate of change in the opening degree of the accelerator and the relative opening degree of the accelerator as the inputs and the quantitative intention of the driver as the output. Finally, the novel multi-parameter coordinated shift control strategy is formulated on the basis of the vehicle speed, the opening degree of the accelerator and the quantitative intention of the driver. The designed shift strategy is compared with conventional methods using simulations and is verified by road tests. The results show that the shift control strategy can make the vehicle shift much more effective.

show abstract

“…Hua and Jiang proposed a driver's steering intention identification method using principal component analysis (PCA) [23]. Li and Zhu built a driver's starting intention identification method based on an artificial error back-propagation neutral network [24]. Kim and Bong proposed a lane change driving intention classification method using support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Identification of Driver Braking Intention Based on Long Short-Term Memory (LSTM) Network

Wang

Zhao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Driving intention identification is a key technology which can improve the adaptability of the intelligent driver assistance systems and the energy efficiency of electric vehicles. This article proposes a novel method for identifying the driver braking intention. In order to improve the identification accuracy of driving intention, a braking intention identification model based on Long Short-Term Memory (LSTM) Network is constructed. The data of slight braking, normal braking and hard braking that can use for offline training are obtained through tests on real vehicle at Chang'an University vehicle performance testing ground. Support vector machine-recursive feature elimination (SVM-RFE) algorithm is used to select the characteristic parameter of braking intention identification model. The random search is subsequently used to optimize the hyper-parameters of LSTM. LSTM-based and General Hidden Markov Model (GHMM)-based model under different time window are used to identify braking intention of slight braking, normal braking and hard braking respectively. The results show that the Precision, Recall, F-measure, Accuracy of the braking intention identification model which propose in this paper based on LSTM are better than that of the braking intention identification model based on GHMM. Moreover, the Recall and Accuracy of the LSTM-based braking intention identification models are above 0.95, indicating the good ability of intention identification.

show abstract

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

Cited by 24 publications

References 27 publications

Variable Weight Coefficient Optimization of Gearshift Actuator With Direct-Driving Automated Transmission

Variable Weight Coefficient Optimization of Gearshift Actuator With Direct-Driving Automated Transmission

A novel multi-parameter coordinated shift control strategy for an automated manual transmission based on fuzzy inference

Identification of Driver Braking Intention Based on Long Short-Term Memory (LSTM) Network

Contact Info

Product

Resources

About