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

• URL: http://arxiv.org/abs/2609.01120v1
• Date: Tue, 01 Sep 2026 11:59:00 GMT
• Title: DNC-IMM: Early Lane-Change Intention Recognition via Neural Calibration Based on Driving Context Information
• Abstract summary: Early recognition of lane-change intention is essential for proactive decision-making in autonomous driving systems.<n>This paper proposes a Dual Neural-Calibrated Interacting Multiple Model (DNC-IMM) that improves adaptability to driving context.<n> Experiments on the highD dataset demonstrate that the proposed method reliably recognizes lane-change intentions before lane crossing.
• Score: 2.9220076568786335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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