DNC-IMM：基于驾驶上下文信息的神经校准式早期变道意图识别
DNC-IMM: Early Lane-Change Intention Recognition via Neural Calibration Based on Driving Context Information
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中文总结 AI 辅助
本文提出DNC-IMM模型，通过神经网络校准传统IMM的转移概率矩阵与测量似然，在highD数据集上实现了车道跨越前的早期变道意图识别，尤其在2-3秒预测 horizon 下性能突出。
中文摘要 AI 辅助
早期变道意图识别对自动驾驶和高级驾驶辅助系统的主动决策至关重要。本文提出了双神经校准交互多模型（DNC-IMM），在保留传统交互多模型（IMM）的概率结构与可解释性的同时，提升对驾驶上下文的适应性。该方法利用神经网络编码驾驶上下文信息，包括目标车辆运动、与周围车辆的间隙及相对速度，用于校准转移概率矩阵与测量似然；最终意图由校准后的IMM模式后验确定，而非单独的直接分类器。在highD数据集上的实验表明，该方法可在车道跨越前可靠识别变道意图，尤其在更早的2-3秒预测 horizon 下表现强劲。
英文摘要
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.
发表机构
- Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
- CCS Graduate School of Mobility（CCS移动研究生院）
机构由 AI 辅助整理，请以论文原文为准。
