Research on the identification of DCT vehicle driver’s starting intention based on LSTM neural network and multi-sensor data fusion

Abstract

Currently, the objective evaluation of the DCT vehicle drivability requires the accurate identification of the driver’s intention and vehicle state as well as the selection of the targeted evaluation indicators. The existing identification methods usually cannot divide the driver’s intentions in detail and make full use of the characteristics of time-series signals. Simultaneously, external kinematic sensors are more commonly used than the sensors of vehicle powertrain, which impacts the recognition effect. This paper proposes a new method for identifying the DCT vehicle driver’s starting intentions based on an LSTM neural network and multi-sensor data fusion. The DCT vehicle driver’s starting intentions are subdivided and defined based on human–vehicle interaction analysis and K-means clustering. The input of the model consists of 11-dimensional variables that include motion parameters of the vehicle collected by the external sensors and the powertrain parameters collected by onboard sensors. The method proposed in this paper first establishes a recognition window, which is utilized to extract the starting process samples from the DCT vehicle driving data. Second, the 11 variables of each sample are used as one set of multi-dimensional time-series signals, which are preprocessed through wavelet denoising. Finally, the LSTM network is used to identify the samples. The identification results indicate that the highest recognition accuracy of the proposed algorithm is 94.27%, which is approximately 5% higher than conventional methods, such as fully connected neural networks and support vector machines. Furthermore, the model with 11 input variables outperforms the model with fewer input variables. The effectiveness and superiority of the identification model have been demonstrated.

Optimal Torque Control of the Launching Process with AMT Clutch for Heavy-Duty Vehicles

“…Furthermore, the driver's intention [9,10], engine torque and speed [11], clutch slipping speed [12], and clutch engagement time [8] are taken into account in the optimization process to finally obtain the optimal clutch engagement law. Xu et al [13] propose a DCT vehicle driver's launching intention recognition strategy based on a long short-term memory (LSTM) neural network and multi-sensor data fusion to improve clutch launching performance. Feng et al [14] propose a dual clutch transmission launching control strategy based on pseudo-spectral optimization and data-driven control to respond to time-varying launching intentions, reduce friction and jerk, and improve launching quality.…”