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: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
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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.


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    • 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


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