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Driver-centric data-driven robust model predictive control for mixed vehicular platoon

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Abstract

The penetration rate of automated vehicles (AVs) may remain unsaturated for a long time, resulting in the coexistence of AVs and human-driven vehicles (HDVs). The non-ideal driving behavior has inherent uncertainties and randomness that would cause traffic congestion and oscillation. To improve the driving safety and comfort of mixed vehicular platoon (MVP) consisting of HDVs and AVs, this paper proposes a driver-centric data-driven robust model predictive control (DDRMPC) strategy. This strategy involves two new elements of a data-driven robust model predictive controller and the personalized driving policy. A data-driven MVP model is established with subspace identification to alleviate the adverse effects of uncertain dynamics. To provide a driver-centric driving experience, a personalized driving policy with a flexible spacing corridor and soft constraints is designed in terms of different driving styles. In this way, a tube-based robust model predictive controller is integrated with the data-driven MVP model to develop the DDRMPC strategy, and its stability is proven. Moreover, four experiments with thirty-seven drivers are carried out on a self-developed MVP platform. Finally, several experiments with MVP demonstrate the effectiveness of the proposed DDRMPC strategy.

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Data availability

The data that support the findings of this study are available from the corresponding author, [Qiaoni Han], upon reasonable request.

References

  1. Di Vaio, M., Fiengo, G., Petrillo, A., Salvi, A., Santini, S., Tufo, M.: Cooperative shock waves mitigation in mixed traffic flow environment. IEEE Trans. Intell. Transp. Syst. 20(12), 4339–4353 (2019)

    Article  Google Scholar 

  2. Di, X., Shi, R.: A survey on autonomous vehicle control in the era of mixed-autonomy: from physics-based to AI driving policy learning. Transp. Res. Part C: Emerging Technol. 125, 103008 (2021)

    Article  Google Scholar 

  3. Huang, Y., Shen, Y., Wang, J., Zhang, X.: A platoon-centric multi-channel access scheme for hybrid traffic. IEEE Trans. Veh. Technol. 70(6), 5404–5418 (2021)

    Article  Google Scholar 

  4. Zheng, L., Tian, C., Sun, D., Liu, W.: A new car-following model with consideration of anticipation driving behavior. Nonlinear Dyn. 70, 1205–1211 (2012)

    Article  MathSciNet  Google Scholar 

  5. Song, Z., Ding, H.: Modeling car-following behavior in heterogeneous traffic mixing human-driven, automated and connected vehicles: considering multitype vehicle interactions. Nonlinear Dyn. 111(12), 11115–11134 (2023)

    Article  Google Scholar 

  6. Tampère, C.M.: Human-kinetic multiclass traffic flow theory and modelling. Ph.D Thesis, Delft University of Technology (2004)

  7. Sun, B., Zhang, Q., Zou, C., et al.: Research on microscopic traffic flow modeling and energy characteristics in the energy-saving driving environment. Nonlinear Dyn. 111, 14365–14378 (2023). https://doi.org/10.1007/s11071-023-08582-9

  8. Gao, W., Jiang, Z.P., Ozbay, K.: Data-driven adaptive optimal control of connected vehicles. IEEE Trans. Intell. Transp. Syst. 18(5), 1122–1133 (2016)

    Article  Google Scholar 

  9. Gao, B., Cai, K., Qu, T., Hu, Y., Chen, H.: Personalized adaptive cruise control based on online driving style recognition technology and model predictive control. IEEE Trans. Veh. Technol. 69(11), 12482–12496 (2020)

    Article  Google Scholar 

  10. Xiao, J., Ma, M., Liang, S., et al.: The non-lane-discipline-based car-following model considering forward and backward vehicle information under connected environment. Nonlinear Dyn. 107, 2787–2801 (2022). https://doi.org/10.1007/s11071-021-06999-8

  11. Nisar, K.S., Farman, M., Abdel Aty, M., Cao, J.: A review on epidemic models in sight of fractional calculus. Alex. Eng. J. 75, 81–113 (2023)

    Article  Google Scholar 

  12. Ma, Y., Wang, J.: Energetic impacts evaluation of eco-driving on mixed traffic with driver behavioral diversity. IEEE Trans. Intell. Transp. Syst. 23(4), 3406–3417 (2022)

    Article  Google Scholar 

  13. Angkititrakul, P., Miyajima, C., Takeda, K.: Modeling and adaptation of stochastic driver-behavior model with application to car following. In: 2011 IEEE Intelligent Vehicles Symposium, pp. 814–819. IEEE (2011)

  14. Qu, T., Yu, S., Shi, Z., Chen, H.: Modeling driver’s car-following behavior based on hidden Markov model and model predictive control: A cyber-physical system approach. In: 2017 11th Asian Control Conference, pp. 114–119. IEEE (2017)

  15. Farman, M., Sarwar, R., Askar, S., Ahmad, H., Sultan, M., Akram, M.M.: Fractional order model to study the impact of planting genetically modified trees on the regulation of atmospheric carbon dioxide with analysis and modeling. Results Phys. 48, 106409 (2023)

    Article  Google Scholar 

  16. Jamil, S., Farman, M., Akgül, A.: Qualitative and quantitative analysis of a fractal fractional HIV/AIDS model. Alex. Eng. J. 76, 167–177 (2023)

    Article  Google Scholar 

  17. Zhou, P., Song, H., Wang, H., Chai, T.: Data-driven nonlinear subspace modeling for prediction and control of molten iron quality indices in blast furnace ironmaking. IEEE Trans. Control Syst. Technol. 25(5), 1761–1774 (2016)

    Article  Google Scholar 

  18. Jiang, B., Fei, Y.: Vehicle speed prediction by two-level data driven models in vehicular networks. IEEE Trans. Intell. Transp. Syst. 18(7), 1793–1801 (2016)

    Article  Google Scholar 

  19. Guo, L., Jia, Y.: Inverse model predictive control based modeling and prediction of human-driven vehicles in mixed traffic. IEEE Trans. Intell. Veh. 6(3), 501–512 (2020)

    Article  Google Scholar 

  20. Van Overschee, P., De Moor, B.: Subspace Identification for Linear Systems: Theory Implementation Applications. Springer (2012)

  21. Anubi, O.M., Konstantinou, C.: Enhanced resilient state estimation using data-driven auxiliary models. IEEE Trans. Ind. Inf. 16(1), 639–647 (2019)

    Article  Google Scholar 

  22. Li, Y., Qian, L., Chen, G., Huang, W.: Multiple clearance robustness optimization of a chain ramming machine based on a data-driven model. Nonlinear Dyn. 111(15):1–22. https://doi.org/10.1007/s11071-023-08589-2

  23. Xu, B., Lu, X.: A data-driven spatiotemporal model predictive control strategy for nonlinear distributed parameter systems. Nonlinear Dyn. 108(2), 1269–1281 (2022). https://doi.org/10.1007/s11071-022-07273-1

  24. Ma, R., Basumallik, S., Eftekharnejad, S., Kong, F.: A data-driven model predictive control for alleviating thermal overloads in the presence of possible false data. IEEE Trans. Ind. Appl. 57(2), 1872–1881 (2021)

    Article  Google Scholar 

  25. Torrente, G., Kaufmann, E., Föhn, P., Scaramuzza, D.: Data-driven MPC for quadrotors. IEEE Robot. Autom. Lett. 6(2), 3769–3776 (2021)

    Article  Google Scholar 

  26. Lan, J., Zhao, D., Tian, D.: Data-driven robust predictive control for mixed vehicle platoons using noisy measurement. IEEE Trans. Intell. Transp. Syst. 4(3), 1–11 (2021)

    Google Scholar 

  27. Berberich, J., Köhler, J., Müller, M.A., Allgöwer, F.: Data-driven tracking MPC for changing setpoints. IFAC-PapersOnLine 53(2), 6923–6930 (2020)

    Article  Google Scholar 

  28. Martinez, C.M., Heucke, M., Wang, F., Gao, B., Cao, D.: Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey. IEEE Trans. Intell. Transp. Syst. 19(3), 666–676 (2017)

    Article  Google Scholar 

  29. Kang, Y., Sun, D., Yang, S.: A new car-following model considering driver’s individual anticipation behavior. Nonlinear Dyn. 82, 1293–1302 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  30. Butakov, V.A., Ioannou, P.: Personalized driver vehicle lane change models for ADAS. IEEE Trans. Veh. Technol. 64(10), 4422–4431 (2014)

    Article  Google Scholar 

  31. Chu, D., Deng, Z., He, Y., Wu, C., Sun, C., Lu, Z.: Curve speed model for driver assistance based on driving style classification. IET Intel. Transp. Syst. 11(8), 501–510 (2017)

    Article  Google Scholar 

  32. Ghavidel, H.F., Mousavi, G.S.M.: Observer-based type-2 fuzzy approach for robust control and energy management strategy of hybrid energy storage systems. Int. J. Hydr. Energy 47(33), 14983–15000 (2022)

    Article  Google Scholar 

  33. Zheng, Y., Li, S.E., Li, K., Borrelli, F., Hedrick, J.K.: Distributed model predictive control for heterogeneous vehicle platoons under unidirectional topologies. IEEE Trans. Control Syst. Technol. 25(3), 899–910 (2016)

    Article  Google Scholar 

  34. Lu, Y., Yang, L., Yang, K., Gao, Z., Zhou, H., Meng, F., Qi, J.: A distributionally robust optimization method for passenger flow control strategy and train scheduling on an urban rail transit line. Engineering 12(3), 202–220 (2022)

    Article  Google Scholar 

  35. Carron, A., Arcari, E., Wermelinger, M., Hewing, L., Hutter, M., Zeilinger, M.N.: Data-driven model predictive control for trajectory tracking with a robotic arm. IEEE Robot. Autom. Lett. 4(4), 3758–3765 (2019)

    Article  Google Scholar 

  36. Vicente, B.A.H., James, S.S., Anderson, S.R.: Linear system identification versus physical modeling of lateral-longitudinal vehicle dynamics. IEEE Trans. Control Syst. Technol. 29(3), 1380–1387 (2021)

    Article  Google Scholar 

  37. Luspay, T., Kulcsár, B., van Wingerden, J.-W., Verhaegen, M., Bokor, J.: Linear parameter varying identification of freeway traffic models. IEEE Trans. Control Syst. Technol. 19(1), 31–45 (2011)

    Article  Google Scholar 

  38. Bando, M., Hasebe, K., Nakayama, A., Shibata, A., Sugiyama, Y.: Dynamical model of traffic congestion and numerical simulation. Phys. Rev. E 51(2), 1035–1042 (1995)

    Article  Google Scholar 

  39. Wen, S., Guo, G.: Control of leader-following vehicle platoons with varied communication range. IEEE Trans. Intell. Veh. 5(2), 240–250 (2019)

    Article  Google Scholar 

  40. Park, B.S., Yoo, S.J., Park, J.B., Choi, Y.H.: Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty. IEEE Trans. Control Syst. Technol. 17(1), 207–214 (2008)

    Article  Google Scholar 

  41. Khang, H., Arkkio, A.: Eddy current loss modeling for a form wound induction motor using circuit model. IEEE Trans. Magn. 48(2), 1059–1062 (2012)

    Article  Google Scholar 

  42. Van Overschee, P., De Moor, B.: A unifying theorem for three subspace system identification algorithms. Automatica 31(12), 1853–1864 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  43. Mayne, D.Q., Langson, W.: Robustifying model predictive control of constrained linear systems. Electron. Lett. 37(23), 1422–1423 (2001)

    Article  Google Scholar 

  44. Blanchini, F.: Set invariance in control. Automatica 35(11), 1747–1767 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  45. Tahir, F.: Efficient computation of robust positively invariant sets with linear state-feedback gain as a variable of optimization. In: 2010 7th International Conference on Electrical Engineering Computing Science and Automatic Control, pp. 199–204. IEEE (2010)

  46. Köhler, J., Müller, M.A., Allgöwer, F.: Nonlinear reference tracking: an economic model predictive control perspective. IEEE Trans. Autom. Control 64(1), 254–269 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  47. Useche, S.A., Cendales, B., Lijarcio, I., Llamazares, F.J.: Validation of the F-DBQ: a short (and accurate) risky driving behavior questionnaire for long-haul professional drivers. Transport. Res. F: Traffic Psychol. Behav. 82, 190–201 (2021)

    Article  Google Scholar 

  48. Liu, P., Kurt, A., Ozguner, U.: Distributed model predictive control for cooperative and flexible vehicle platooning. IEEE Trans. Control Syst. Technol. 27(3), 1115–1128 (2019)

    Article  Google Scholar 

  49. Zhang, Z., Zheng, L., Li, Y., Yu, Y., Qiao, X.: Model predictive control for path following of autonomous vehicle considering model parameter uncertainties. In: 2021 6th IEEE International Conference on Advanced Robotics and Mechatronics, pp. 207–212. IEEE (2021)

  50. Franzè, G., Lucia, W., Venturino, A.: A distributed model predictive control strategy for constrained multi-vehicle systems moving in unknown environments. IEEE Trans. Intell. Veh. 6(2), 343–352 (2020)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 62173243, Grant 61933014 and Grant 61803218.

Funding

The funding was provided by the National Natural Science Foundation of China (Grant nos. 62173243, 61933014, 61803218.

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Authors and Affiliations

  1. Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System, School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Yanhong Wu, Zhiqiang Zuo, Yijing Wang & Qiaoni Han

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  1. Yanhong Wu
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  2. Zhiqiang Zuo
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  4. Qiaoni Han
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Correspondence to Qiaoni Han.

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Yanhong Wu, Zhiqiang Zuo, Yijing Wang and Qiaoni Han declare that they have no conflict of interest, and the data are available.

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Wu, Y., Zuo, Z., Wang, Y. et al. Driver-centric data-driven robust model predictive control for mixed vehicular platoon. Nonlinear Dyn 111, 20975–20989 (2023). https://doi.org/10.1007/s11071-023-08971-0

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