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Vehicle dynamics analytics based on complex network techniques: A trajectory-based visibility graph approach

Author

Listed:
  • Hu, Junjie
  • Gao, Dongsheng
  • Wang, Lin
  • Lee, Jaeyoung Jay

Abstract

Extracting vehicle dynamics from trajectory data is essential for advancing autonomous driving technologies, as trajectory-based methods provide fine-grained, time-resolved insight into actual vehicle behavior. These methods are capable of capturing nuanced patterns of motion that are required for understanding intent, predicting maneuvers, and assessing risk in complex environments. This study introduces a novel trajectory-based visibility graph (TVG) framework featuring a tunable visibility tolerance coefficient (α), which governs edge creation by considering geometric occlusion and maneuver scale. Evaluated using the highD dataset with lane-keeping (LK) and lane-changing (LC) maneuvers, our analyses demonstrate that the TVG structures significantly differ between maneuvers. TVG-derived features support accurate classification (supervised) and meaningful clustering (unsupervised) of vehicle dynamics, while key graph metrics correlate with surrogate risk indicators. Sensitivity analysis reveals the task-dependent impact of α on performance. The adaptive TVG method offers a robust and versatile framework for representing vehicular motion, detecting maneuvers, uncovering behavior patterns, and assessing driving risk.

Suggested Citation

  • Hu, Junjie & Gao, Dongsheng & Wang, Lin & Lee, Jaeyoung Jay, 2025. "Vehicle dynamics analytics based on complex network techniques: A trajectory-based visibility graph approach," Chaos, Solitons & Fractals, Elsevier, vol. 200(P3).
    • Handle: RePEc:eee:chsofr:v:200:y:2025:i:p3:s0960077925011749
    DOI: 10.1016/j.chaos.2025.117161
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    References listed on IDEAS

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