In mixed-traffic environments, where autonomous vehicles (AVs) interact with diverse human-driven vehicles (HVs), unpredictable intentions and heterogeneous behaviors make safe and efficient lane change maneuvers highly challenging. Existing methods often oversimplify these interactions by assuming uniform patterns. We propose an intention-driven lane change framework that integrates driving-style recognition, cooperation-aware decision-making, and coordinated motion planning. A deep learning classifier trained on the NGSIM dataset identifies human driving styles in real time. A cooperation score with intrinsic and interactive components estimates surrounding drivers' intentions and quantifies their willingness to cooperate with the ego vehicle. Decision-making combines behavior cloning with inverse reinforcement learning to determine whether a lane change should be initiated. For trajectory generation, model predictive control is integrated with IRL-based intention inference to produce collision-free and socially compliant maneuvers. Experiments show that the proposed model achieves 94.2\% accuracy and 94.3\% F1-score, outperforming rule-based and learning-based baselines by 4-15\% in lane change recognition. These results highlight the benefit of modeling inter-driver heterogeneity and demonstrate the potential of the framework to advance context-aware and human-like autonomous driving in complex traffic environments.

翻译：在混合交通环境中，自动驾驶车辆与多样化的人类驾驶车辆交互时，不可预测的意图和异构行为使得安全高效的换道操作极具挑战性。现有方法常通过假设统一模式过度简化这些交互。我们提出一种意图驱动的换道框架，该框架整合了驾驶风格识别、协作感知决策和协同运动规划。基于NGSIM数据集训练的深度学习分类器可实时识别人类驾驶风格。包含内在与交互分量的协作评分用于估计周边驾驶员的意图并量化其与主车协作的意愿。决策模块结合行为克隆与逆强化学习以判定是否应发起换道。在轨迹生成方面，模型预测控制与基于逆强化学习的意图推断相结合，以生成无碰撞且符合社会规范的机动动作。实验表明，所提模型在换道识别任务中达到94.2%的准确率与94.3%的F1分数，较基于规则和基于学习的基线方法提升4-15%。这些结果凸显了建模驾驶员间异质性的优势，并证明了该框架在推动复杂交通环境中情境感知类人自动驾驶方面的潜力。