An Adaptive Affective Interaction Design Framework for Smart Cockpit Human–Machine Interfaces and Ambient Lighting in Urban Traffic Congestion Scenarios

Urban traffic congestion can progressively accumulate driver irritation and stress and can promote attentional disengagement. Human-machine interfaces (HMIs) and ambient lighting in intelligent cockpits currently serve information feedback and spatial-atmosphere functions, respectively; however, they are rarely coordinated in terms of affective goals, intervention timing, and state transitions. This study employs a narrative literature review, comparative analysis, and design translation. It prioritizes authoritative studies and current standards published between 2016 and 2025, synthesizes research on driver affect, adaptive HMI, cognitive load, and in-vehicle dynamic lighting, and translates the findings into design requirements for stage-specific adaptation, functional differentiation, safety priority, low-stimulation feedback, and user control. On this basis, a rule-based, three-stage adaptive affective interaction framework is proposed, comprising initial congestion regulation, prolonged standstill regulation, and attention restoration with task re-engagement. The framework specifies differentiated roles for HMI and ambient lighting, finite-state transitions, safety interruption, and user-exit logic. A system architecture, low-fidelity concepts, and strategy-mapping tables are provided to support subsequent prototyping. All temporal and visual parameters are treated as conceptual reference values rather than validated optima. No user-experiment results are reported; the contribution is an implementable framework for balancing affective support and task vigilance in low-speed urban congestion.