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Model Predictive Adaptive Cruise Control of Intelligent Electric Vehicles Based on Deep Reinforcement Learning Algorithm FWOR Driver Characteristics

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Abstract

This paper presents a novel model predictive adaptive cruise control strategy of intelligent electric vehicles based on deep reinforcement learning algorithm for driver characteristics. Firstly, the influence mechanism of factors such as inter-vehicle distance, relative speed and time headway (THW) on the driver’s behavior in the process of car following is analyzed by the correlation coefficient method. Then, the driver behavior in the process of car following is learned from the natural driving data, the car following model is established by the deep deterministic policy gradient (DDPG) algorithm, and the output acceleration of the DDPG model is used as the reference trajectory of the ego vehicle’s acceleration. Next, in order to reflect the driver behavior and achieve multi performance objective optimization of adaptive cruise control of intelligent electric vehicles, the model predictive controller (MPC) is designed and used for tracking the desired acceleration produced by the car following DDPG model. Finally, the performance of the proposed adaptive cruise control strategy is evaluated by the experimental tests, and the results demonstrate the effectiveness of proposed control strategy.

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References

  • An, S., Xu, L., Chen, G. and Shi, Z. (2020). A new car-following model on complex road considering driver’s characteristics. Modern Physics Letters B 34, 16 2050182.

    Article  MathSciNet  Google Scholar 

  • Bender, A., Agamennoni, G., Ward, J. R., Worrall, S. and Nebot, E. M. (2015). An unsupervised approach for inferring driver behavior from naturalistic driving data. IEEE Trans. Intelligent Transportation Systems 16, 6 3325–3336.

    Article  Google Scholar 

  • Ghadirzadeh, A., Chen, X., Yin, W., Yi, Z., Björkman, M. and Kragic, D. (2020). Human-centered collaborative robots with deep reinforcement learning. IEEE Robotics and Automation Letters 6, 2 566–571.

    Article  Google Scholar 

  • Guo, J., Li, W., Wang, J., Luo, Y. and Li, K. (2021). Safe and energy-efficient car-following control strategy for intelligent electric vehicles considering regenerative braking. IEEE Trans. Intelligent Transportation Systems 23, 7 7070–7081.

    Article  Google Scholar 

  • Guo, J., Luo, Y. and Li, K. (2017). Integrated adaptive dynamic surface car-following control for nonholonomic autonomous electric vehicles. Science China Technological Sciences 60, 8 1221–1230.

    Article  Google Scholar 

  • Guo, J., Luo, Y., Hu, C., Tao, C. and Li, K. (2019). Robust combined lane keeping and direct yaw moment control for intelligent electric vehicles with time delay. Int. J. Automotive Technology 20, 2 289–296.

    Article  Google Scholar 

  • Guo, J., Wang, J., Li, K. and Luo, Y. (2020). Adaptive nonlinear coordinated optimal dynamic platoon control of connected autonomous distributed electric vehicles on curved roads. IET Intelligent Transport Systems 14, 12 1626–1637.

    Article  Google Scholar 

  • Haroon, Z., Khan, B., Farid, U., Ali, S. M. and Mehmood, C. A. (2019). Switching control paradigms for adaptive cruise control system with stop-and-go scenario. Arabian J. Science and Engineering, 44, 2103–2113.

    Article  Google Scholar 

  • Kim, T. and Jerath, K. (2021). Congestion-aware cooperative adaptive cruise control for mitigation of self-organized traffic jams. IEEE Trans. Intelligent Transportation Systems 23, 7 6621–6632.

    Article  Google Scholar 

  • Lefèvre, S., Carvalho, A. and Borrelli, F. (2015). A learning-based framework for velocity control in autonomous driving. IEEE Trans. Automation Science and Engineering 13, 1 32–42.

    Article  Google Scholar 

  • Mirabilio, M., Iovine, A., De Santis, E., Di Benedetto, M. D. and Pola, G. (2020). A microscopic human-inspired adaptive cruise control for eco-driving. European Control Conf. (ECC), St. Petersburg, Russia.

  • Mohtavipour, S. M., Mollajafari, M. and Naseri, A. (2019). A guaranteed-comfort and safe adaptive cruise control by considering driver’s acceptance level. Int. J. Dynamics and Control, 7, 966–980.

    Article  MathSciNet  Google Scholar 

  • Moon, S. and Yi, K. (2008). Human driving data-based design of a vehicle adaptive cruise control algorithm. Vehicle System Dynamics 46, 8 661–690.

    Article  Google Scholar 

  • Moser, D., Schmied, R., Waschl, H. and del Re, L. (2017). Flexible spacing adaptive cruise control using stochastic model predictive control. IEEE Trans. Control Systems Technology 26, 1 114–127.

    Article  Google Scholar 

  • Papathanasopoulou, V. and Antoniou, C. (2015). Towards data-driven car-following models. Transportation Research Part C: Emerging Technologies, 55, 496–509.

    Article  Google Scholar 

  • Su, C., Deng, W., He, R., Wu, J. and Jiang, Y. (2018). Personalized adaptive cruise control considering drivers’ characteristics. SAE Paper No. 2018-01-0591.

  • Vajedi, M. and Azad, N. L. (2015). Ecological adaptive cruise controller for plug-in hybrid electric vehicles using nonlinear model predictive control. IEEE Trans. Intelligent Transportation Systems 17, 1 113–122.

    Article  Google Scholar 

  • Wang, J., Guo, J., Luo, Y., Li, K. and Zheng, H. (2022). Design of switching controller for connected vehicles platooning with intermittent communication via mode-dependent average dwell-time approach. IEEE Internet of Things J. 10, 3 2708–2719.

    Article  Google Scholar 

  • Wang, J., Zhang, L., Zhang, D. and Li, K. (2012). An adaptive longitudinal driving assistance system based on driver characteristics. IEEE Trans. Intelligent Transportation Systems 14, 1 1–12.

    Article  Google Scholar 

  • Wang, L. and Horn, B. K. (2019). On the stability analysis of mixed traffic with vehicles under car-following and bilateral control. IEEE Trans. Automatic Control 65, 7 3076–3083.

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, X., Jiang, R., Li, L., Lin, Y., Zheng, X. and Wang, F. Y. (2017). Capturing car-following behaviors by deep learning. IEEE Trans. Intelligent Transportation Systems 19, 3 910–920.

    Article  Google Scholar 

  • Yang, D., Zhu, L., Liu, Y., Wu, D. and Ran, B. (2018). A novel car-following control model combining machine learning and kinematics models for automated vehicles. IEEE Trans. Intelligent Transportation Systems 20, 6 1991–2000.

    Article  Google Scholar 

  • Yoshimoto, T. and Fukao, T. (2018). Adaptive cruise control considering driver’s characteristics. Trans. the JSME (in Japanese) 84, 868 18–00164.

    Article  Google Scholar 

  • Yu, S., Liu, Q. and Li, X. (2013). Full velocity difference and acceleration model for a car-following theory. Communications in Nonlinear Science and Numerical Simulation 18, 5 1229–1234.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang, W. Y., Wei, Z. W., Wang, B. H. and Han, X. P. (2016). Measuring mixing patterns in complex networks by Spearman rank correlation coefficient. Physica A: Statistical Mechanics and its Applications, 451, 440–450.

    Article  Google Scholar 

  • Zhu, B., Jiang, Y., Zhao, J., He, R., Bian, N. and Deng, W. (2019). Typical-driving-style-oriented personalized adaptive cruise control design based on human driving data. Transportation Research Part C: Emerging Technologies, 100, 274–288.

    Article  Google Scholar 

  • Zhu, M., Wang, X. and Wang, Y. (2018). Human-like autonomous car-following model with deep reinforcement learning. Transportation Research Part C: Emerging Technologies, 97, 348–368.

    Article  Google Scholar 

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Acknowledgement

This work was funded by the National Basic Research Project of China (No. 2016YFB0100900), the National Natural Science Foundation of China (No. U1564208, No.61304193), and the Natural Science Foundation of Fujian Province of China (No. 2017J01100). Authors are grateful for helpful comments from referees to improve this manuscript.

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Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361005, China

    Jinghua Guo & WenChang Li

  2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China

    Jinghua Guo, Yugong Luo & Keqiang Li

Authors
  1. Jinghua Guo
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  2. WenChang Li
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  3. Yugong Luo
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  4. Keqiang Li
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Correspondence to Jinghua Guo.

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Guo, J., Li, W., Luo, Y. et al. Model Predictive Adaptive Cruise Control of Intelligent Electric Vehicles Based on Deep Reinforcement Learning Algorithm FWOR Driver Characteristics. Int.J Automot. Technol. 24, 1175–1187 (2023). https://doi.org/10.1007/s12239-023-0096-4

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  • DOI: https://doi.org/10.1007/s12239-023-0096-4

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