Lane change plays a critical role in autonomous driving and directly affects
traffic safety and efficiency. Although deep learning-based lane-change
decision-making frameworks have achieved promising results, they still face
fundamental challenges in producing human-consistent and trustworthy behavior,
mainly due to: 1) Inadequate psychology-informed personalization, as most
frameworks focus on physical variables but neglect psychological factors (e.g.,
risk tolerance, urgency), limiting their ability to capture individual
differences in lane-change motivations. 2) Limited holistic understanding of
traffic context, most frameworks lack consideration of high-level and
interpretable indicators (e.g., traffic pressure) in comprehensively assessing
dynamic traffic scenarios, limiting their capacity for human-like contextual
understanding. 3) Lack of transparent and interpretable decision logic, as many
frameworks operate as black boxes with opaque reasoning processes, hindering
human-aligned explanation, weakening user trust, reducing accident traceability,
and impeding model refinement. To this end, a policy-oriented
contextual-reasoning fuzzy neural network (POCR-FNN) is proposed as a deep
learning-based decision-making framework for personalized and interpretable
autonomous lane-change. First, we develop a psychology-informed driving style
classification by learning distinct fuzzy membership functions to enable
style-specific policy learning. Second, we design a human-inspired local
interaction-aware module that estimates traffic tension by combining interaction
salience and contextual risk, enhancing contextual understanding. Finally, we
integrate fuzzy logic with a deep learning-based policy network to enable
rule-level decision reasoning with real-time interpretability and transparent
traceability. Extensive experiments on multiple public highway and urban
datasets demonstrate that POCR-FNN achieves state-of-the-art performance while
significantly improving personalization and interpretability across various
driving styles and scenarios.