A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 9 Issue 12
Dec.  2022

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
W. Hu, Z. J. Deng, D. P. Cao, B. J. Zhang, A. Khajepour, L. Zeng, and Y. Wu, “Probabilistic lane-change decision-making and planning for autonomous heavy vehicles,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2161–2173, Dec. 2022. doi: 10.1109/JAS.2022.106049
Citation: W. Hu, Z. J. Deng, D. P. Cao, B. J. Zhang, A. Khajepour, L. Zeng, and Y. Wu, “Probabilistic lane-change decision-making and planning for autonomous heavy vehicles,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2161–2173, Dec. 2022. doi: 10.1109/JAS.2022.106049

Probabilistic Lane-Change Decision-Making and Planning for Autonomous Heavy Vehicles

doi: 10.1109/JAS.2022.106049
Funds:  This work was supported by the National Natural Science Foundation of China (5187051675)
More Information
  • To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index (AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.


  • loading
  • [1]
    D. P. Cao, X. B. Song, and M. Ahmadian, “Editors’ perspectives: Road vehicle suspension design, dynamics, and control,” Vehicle Syst. Dyn., vol. 49, no. 1–2, pp. 3–28, Jan. 2011. doi: 10.1080/00423114.2010.532223
    Y. Shi, Y. W. Huang, and Y. Chen, “Trajectory planning of autonomous trucks for collision avoidance with rollover prevention,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 7, pp. 8930–8939, Jul. 2022. doi: 10.1109/TITS.2021.3088293
    S. Moridpour, M. Sarvi, G. Rose, and E. Mazloumi, “Lane-changing decision model for heavy vehicle drivers,” J. Intell. Transp. Syst., vol. 16, no. 1, pp. 24–35, Feb. 2012. doi: 10.1080/15472450.2012.639640
    D. J. Chen, S. Ahn, S. Bang, and D. Noyce, “Car-following and lane-changing behavior involving heavy vehicles,” Transp. Res. Rec.: J. Transp. Res. Board, vol. 2561, no. 1, pp. 89–97, Jan. 2016. doi: 10.3141/2561-11
    W. Hu, F. Ding, J. Zhang, B. J. Zhang, N. Zhang, and A. Qin, “Robust adaptive backstepping sliding mode control for motion mode decoupling of two-axle vehicles with active kinetic dynamic suspension systems,” Int. J. Robust Nonlinear Control, vol. 30, no. 8, pp. 3110–3133, May 2020. doi: 10.1002/rnc.4927
    K. Aghabayk, S. Moridpour, W. Young, M. Sarvi, and Y. B. Wang, “Comparing heavy vehicle and passenger car lane-changing maneuvers on arterial roads and freeways,” Transp. Res. Rec.: J. Transp. Res. Board, vol. 2260, no. 1, pp. 94–101, Jan. 2011. doi: 10.3141/2260-11
    F. Zhang, R. F. Xia, and X. X. Chen, “An optimal trajectory planning algorithm for autonomous trucks: Architecture, algorithm, and experiment,” Int. J. Adv. Robot. Syst., vol. 17, no. 2, p. 172988142090960, Mar.–Apr. 2020.
    J. Nilsson, J. Silvlin, M. Brannstrom, E. Coelingh, and J. Fredriksson, “If, when, and how to perform lane change maneuvers on highways,” IEEE Intell. Transp. Syst. Mag., vol. 8, no. 4, pp. 68–78, Oct. 2016. doi: 10.1109/MITS.2016.2565718
    B. Lu, G. F. Li, H. L. Yu, H. Wang, J. Q. Guo, D. Cao, and H. W. He, “Adaptive potential field-based path planning for complex autonomous driving scenarios,” IEEE Access, vol. 8, pp. 225294–225305, Dec. 2020. doi: 10.1109/ACCESS.2020.3044909
    Z. Y. Huang, J. D. Wu, and C. Lv, “Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 8, pp. 10239–10251, Aug. 2022. doi: 10.1109/TITS.2021.3088935
    Z. D. Zheng, “Recent developments and research needs in modeling lane changing,” Transp. Res. Part B: Methodol., vol. 60, pp. 16–32, Feb. 2014. doi: 10.1016/j.trb.2013.11.009
    W. S. Wang, X. X. Na, D. Cao, J. W. Gong, J. Q. Xi, Y. Xing, and F.-Y. Wang, “Decision-making in driver-automation shared control: A review and perspectives,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1289–1307, Sep. 2020.
    T. Toledo, H. N. Koutsopoulos, and M. Ben-Akiva, “Integrated driving behavior modeling,” Transp. Res. Part C: Emerg. Technol., vol. 15, no. 2, pp. 96–112, Apr. 2007. doi: 10.1016/j.trc.2007.02.002
    Y. F. Ma, Z. Y. Wang, H. Yang, and L. Yang, “Artificial intelligence applications in the development of autonomous vehicles: A survey,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 315–329, Mar. 2020. doi: 10.1109/JAS.2020.1003021
    A. Kesting, M. Treiber, and D. Helbing, “General lane-changing model MOBIL for car-following models,” Transp. Res. Rec.: J. Transp. Res. Board, vol. 1999, no. 1, pp. 86–94, Jan. 2007. doi: 10.3141/1999-10
    L. Chen, X. M. Hu, W. Tian, H. Wang, D. Cao, and F. Y. Wang, “Parallel planning: A new motion planning framework for autonomous driving,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 236–246, Jan. 2019. doi: 10.1109/JAS.2018.7511186
    D. X. Yu, C. L. Chen, and H. Xu, “Intelligent decision making and bionic movement control of self-organized swarm,” IEEE Trans. Ind. Electron., vol. 68, no. 7, pp. 6369–6378, Jul. 2021. doi: 10.1109/TIE.2020.2998748
    M. Morsali, E. Frisk, and J. Åslund, “Real-time velocity planning for heavy duty truck with obstacle avoidance,” in Proc. IEEE Intelligent Vehicles Symp., Los Angeles, USA, 2017, pp. 109–114.
    Y. Gao, D. Cao, and Y. H. Shen, “Path-following control by dynamic virtual terrain field for articulated steer vehicles,” Vehicle Syst. Dyn., vol. 58, no. 10, pp. 1528–1552, Jul. 2019.
    Z. J. Deng, D. F. Chu, C. Z. Wu, S. D. Liu, C. Sun, T. Liu, and D. Cao, “A probabilistic model for driving-style-recognition-enabled driver steering behaviors,” IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 3, pp. 1838–1851, Mar. 2020.
    M. Fanti, A. M. Mangini, A. Favenza, and G. Difilippo, “An Eco-Route planner for heavy duty vehicles,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 37–51, Jan. 2021. doi: 10.1109/JAS.2020.1003456
    T. Zhang, W. J. Song, M. Y. Fu, Y. Yang, and M. L. Wang, “Vehicle motion prediction at intersections based on the turning intention and prior trajectories model,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1657–1666, Oct. 2021. doi: 10.1109/JAS.2021.1003952
    C. Y. Zu, C. Yang, J. Wang, W. B. Gao, D. Cao, and F.-Y. Wang, “Simulation and field testing of multiple vehicles collision avoidance algorithms,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1045–1063, Jul. 2020. doi: 10.1109/JAS.2020.1003246
    J. W. Zhang, G. F. Li, Z. J. Deng, H. L. Yu, J. Huissoon, and D. Cao, “Interaction-aware cut-in behavior prediction and risk assessment for autonomous driving,” IFAC-PapersOnLine, vol. 53, no. 5, pp. 656–663, Dec. 2020.
    S. Noh and K. An, “Decision-making framework for automated driving in highway environments,” IEEE Trans. Intell. Transp. Syst., vol. 19, no. 1, pp. 58–71, Jan. 2018. doi: 10.1109/TITS.2017.2691346
    G. F. Li, Y. F. Yang, T. R. Zhang, X. D. Qu, D. Cao, B. Cheng, and K. Q. Li, “Risk assessment based collision avoidance decision-making for autonomous vehicles in multi-scenarios,” Transp. Res. Part C: Emerg. Technol., vol. 122, p. 102820, Jan. 2021. doi: 10.1016/j.trc.2020.102820
    H. Wang, B. Lu, J. Li, T. Liu, Y. Xing, C. Lv, D. P. Cao, J. X. Li, J. W. Zhang, and E. Hashemi, “Risk assessment and mitigation in local path planning for autonomous vehicles with LSTM based predictive model,” IEEE Trans. Autom. Sci. Eng., DOI: 10.1109/TASE.2021.3075773.
    S. Shalev-Shwartz, S. Shammah, and A. Shashua, “On a formal model of safe and scalable self-driving cars,” arXiv preprint arXiv: 1708.06374, 2017.
    M. Abroshan, R. Hajiloo, E. Hashemi, and A. Khajepour, “Model predictive-based tractor-trailer stabilisation using differential braking with experimental verification,” Vehicle Syst. Dyn., vol. 59, no. 8, pp. 1190–1213, Mar. 2021. doi: 10.1080/00423114.2020.1744024
    M. Abroshan, “Integrated stability and tracking control system for autonomous vehicle-trailer systems,” Ph.D. dissertation, Mechanical and Mechatronics Eng., Univ. Waterloo, Waterloo, 2021.
    D. P. Cao, S. Rakheja, and C. Y. Su, “Dynamic analyses of roll plane interconnected hydro-pneumatic suspension systems,” Int. J. Veh. Des., vol. 47, no. 1–4, pp. 51–80, Oct. 2008.
    H. H. Huang, R. K. Yedavalli, and D. A. Guenther, “Active roll control for rollover prevention of heavy articulated vehicles with multiple-rollover-index minimisation,” Vehicle Syst. Dyn., vol. 50, no. 3, pp. 471–493, Jul. 2012. doi: 10.1080/00423114.2011.597863
    M. T. Wolf and J. W. Burdick, “Artificial potential functions for highway driving with collision avoidance,” in Proc. IEEE Int. Conf. Robotics and Automation, Pasadena, USA, 2008, pp. 3731–3736.
    R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highD dataset: A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems,” in Proc. 21st Int. Conf. Intelligent Transportation Systems, Maui, USA, 2018, pp. 2118–2125.


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(11)  / Tables(2)

    Article Metrics

    Article views (455) PDF downloads(73) Cited by()


    • A hierarchical probabilistic decision-making and trajectory planning frame-work is developed for enhancing the safety and stability of the autonomous heavy trucks
    • An aggressiveness index (AI) is proposed to quantify the asymmetrical risks to the road user with the vehicle size and mass considered
    • To plan the dynamically feasible and more truck-friendly lane-change trajectory, the lateral and roll dynamics stabilities are considered in the trajectory planning module


    DownLoad:  Full-Size Img  PowerPoint