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An IOHMM-based look-ahead driver model considering upcoming road grade

Author

Listed:
  • Lin, Nan
  • Chen, Boan
  • Jia, Suhua
  • Yue, Jinghan
  • Shi, Shuming

Abstract

Heavy-duty truck drivers can scan the road ahead to identify critical road grade features and make ecological velocity control on mountainous highways. In order to accurately describe and understand this longitudinal driving behavior, this paper proposes a data-driven look-ahead driver model with an interpretable input output hidden Markov Model (IOHMM) framework. A set of real heavy-duty truck driving data has been collected for the model’s unsupervised training and evaluation. The results show that the proposed model can accurately predict future vehicle velocity and acceleration. The RMSE in the task of velocity prediction over the next 30 seconds is less than 0.9 m/s, which performs better than the baseline methods. Additionally, based on the model structural parameters and state probability distributions, the proposed model can explain how drivers use vehicle and road grade information to adjust the driving intention and velocity control response.

Suggested Citation

  • Lin, Nan & Chen, Boan & Jia, Suhua & Yue, Jinghan & Shi, Shuming, 2025. "An IOHMM-based look-ahead driver model considering upcoming road grade," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 671(C).
    • Handle: RePEc:eee:phsmap:v:671:y:2025:i:c:s0378437125003188
    DOI: 10.1016/j.physa.2025.130666
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    References listed on IDEAS

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