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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Liang Chen & Jiming Xie & Simin Wu & Fengxiang Guo & Zheng Chen & Wenqi Tan, 2021. "Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions," Energies, MDPI, vol. 14(24), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8429-:d:702214
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    References listed on IDEAS

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    2. Taylor, Jeffrey & Zhou, Xuesong & Rouphail, Nagui M. & Porter, Richard J., 2015. "Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 59-80.
    3. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
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    Cited by:

    1. Marek Guzek & Rafał S. Jurecki & Wojciech Wach, 2022. "Vehicle and Traffic Safety," Energies, MDPI, vol. 15(13), pages 1-4, June.

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