Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions

Liang Chen, Jiming Xie, Simin Wu, Fengxiang Guo, Zheng Chen and Wenqi Tan
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Liang Chen: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Jiming Xie: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Simin Wu: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Fengxiang Guo: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Zheng Chen: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Wenqi Tan: College of Information and Smart Electromechanical Engineering, Xiamen Huaxia University, Xiamen 361024, China

Energies, 2021, vol. 14, issue 24, 1-23

Abstract: With their advantages of high experimental safety, convenient setting of scenes, and easy extraction of control parameters, driving simulators play an increasingly important role in scientific research, such as in road traffic environment safety evaluation and driving behavior characteristics research. Meanwhile, the demand for the validation of driving simulators is increasing as its applications are promoted. In order to validate a driving simulator in a complex environment, curve road conditions with different radii are considered as experimental evaluation scenarios. To attain this, this paper analyzes the reliability and accuracy of the experimental vehicle speed of a driving simulator. Then, qualitative and quantitative analysis of the lateral deviation of the vehicle trajectory is carried out, applying the cosine similarity method. Furthermore, a data-driven method was adopted which takes the longitudinal displacement, lateral displacement, vehicle speed and steering wheel angle of the vehicle as inputs and the lateral offset as the output. Thus, a curve trajectory planning model, a more comprehensive and human-like operation, is established. Based on directional long short-term memory (Bi–LSTM) and a recurrent neural network (RNN), a multiple Bi–LSTM (Mul–Bi–LSTM) is proposed. The prediction performance of LSTM, MLP model and Mul–Bi–LSTM are compared in detail on the validation set and testing set. The results show that the Mul–Bi–LSTM model can generate a trajectory which is very similar to the driver’s curve driving and have a preferable generalization performance. Therefore, this method can solve problems which cannot be realized in real complex scenes in the simulator validation. Selecting the trajectory as the validation parameter can more comprehensively and intuitively reflect the simulator’s curve driving state. Using a speed model and trajectory model instead of a real car experiment can improve the efficiency of simulator validation and lay a foundation for the standardization of simulator validation.

Keywords: vehicle driving simulator; curve driving behavior; validation; multiple bi-directional long short-term memory (Mul–Bi–LSTM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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