Aug 2026· The Journal of The Korea Institute of Intelligent Transport Systems· 0 citations· 23 references
Computer Science
TL;DR
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 is proposed.
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.
A joint intention-and-trajectory prediction framework that combines mutual-information-guided feature selection with a hybrid convolutional neural network (CNN)–Transformer architecture is proposed to improve the accuracy, efficiency, and interpretability of intention-aware vehicle trajectory prediction.
The results indicate that the multi-task CNN-LSTM can balance macroscopic behavior prediction and microscopic risk recognition, thereby improving the active warning capability of autonomous-driving systems in complex traffic scenarios.
Tianqing Liu, Xin-Yan Huang, Li-Fang He et al.· International Conference on...· 0 citations
While intelligent driving systems have achieved notable advances in perception, prediction, and motion planning, robust high-level decision-making remains a persistent challenge in long-tail scenarios that demand semantic scene understanding, contextual reasoning, and experience-based judgment. Rulebased policies tend...
Qiu-Man Xu, Shuai Zhao· 2026 3rd International Confe...· 0 citations
The HGCA-Net framework employs a dynamic spatio-temporal graph attention network for interaction modeling and an intention recognition branch for maneuver preferences to address extended-horizon error accumulation during nonlinear highway lane changes.
Z. Wang, Ge Tong, Tao Cheng et al.· International Conference on...· 0 citations
A method for lane-change intention anticipation of drivers on highways based on kinematic features and surrounding observations that makes use of a recursive Bayesian filtering strategy which can be interpreted as a hidden Markov model is discussed and analyzed.
Dilara Kilinç, H. Kleikamp, Bruno Viti· 0 citations
A scalable Vision-AI and deep learning framework modelling driver decisions based on vehicle type, speed, acceleration, lane occupation, and signal distance is presented, showcasing the model’s robustness and potential to improve intersection safety and inform intelligent traffic management.
Athena Psalta, Aristomenis Kopsacheilis, Vasileios Tsironis et al.· European Transport Research...· 0 citations
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