An eco‐driving approach for ride comfort improvement
Addressing the dual challenge of motion sickness in autonomous vehicle passengers and transportation-related carbon emissions, this paper proposes a synergistic optimization framework integrating eco-driving strategies with personalized comfort modeling. Innovatively, it introduces self-organizing maps (SOM) for the first time to jointly characterize the coupled effects of driving style on ride comfort and energy consumption. Leveraging real-world driving behavior data, it extracts multidimensional driving features to construct an interpretable comfort–eco-efficiency joint evaluation model and generates natural-language, personalized improvement recommendations. Experiments demonstrate a 57.7% improvement in mainstream comfort metrics and up to 47.1% reduction in greenhouse gas emissions—significantly outperforming baseline methods. Key contributions include: (1) an SOM-driven paradigm for modeling the coupling between ride comfort and environmental impact; (2) a data-driven framework for generating personalized, human-interpretable recommendations; and (3) a dual-objective co-optimization mechanism tailored for human–autonomous vehicle collaboration.