DNC-IMM: Early Lane-Change Intention Recognition via Neural Calibration Based on Driving Context Information

Abstract:Early recognition of lane-change intention is essential for proactive decision-making in autonomous driving and advanced driver assistance systems. This paper proposes a Dual Neural-Calibrated Interacting Multiple Model (DNC-IMM) that improves adaptability to driving context while preserving the probabilistic structure and interpretability of a conventional IMM. The proposed method encodes driving-context information, including target-vehicle motion, gaps to surrounding vehicles, and relative velocities, with a neural network that calibrates both the transition-probability matrix and measurement likelihoods. The final intention is determined from the calibrated IMM mode posterior rather than from a separate direct classifier. Experiments on the highD dataset demonstrate that the proposed method reliably recognizes lane-change intentions before lane crossing and provides particularly strong performance at the earlier 2-3 s prediction horizons.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2609.01120 [cs.RO]

(or arXiv:2609.01120v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.01120 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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