Accurate vehicle rating prediction can facilitate designing and configuring good vehicles. This prediction allows vehicle designers and manufacturers to optimize and improve their designs in a timely manner, enhance their product performance, and effectively attract consumers. However, most of the existing data-driven methods rely on data from a single mode, e.g., text, image, or parametric data, which results in a limited and incomplete exploration of the available information. These methods lack comprehensive analyses and exploration of data from multiple modes, which probably leads to inaccurate conclusions and hinders progress in this field. To overcome this limitation, we propose a multi-modal learning model for more comprehensive and accurate vehicle rating predictions. Specifically, the model simultaneously learns features from the parametric specifications, text descriptions, and images of vehicles to predict five vehicle rating scores, including the total score, critics score, performance score, safety score, and interior score. We compare the multi-modal learning model to the corresponding unimodal models and find that the multi-modal model's explanatory power is 4% - 12% higher than that of the unimodal models. On this basis, we conduct sensitivity analyses using SHAP to interpret our model and provide design and optimization directions to designers and manufacturers. Our study underscores the importance of the data-driven multi-modal learning approach for vehicle design, evaluation, and optimization. We have made the code publicly available at http://decode.mit.edu/projects/vehicleratings/.

翻译：准确的车辆评分预测有助于设计与配置优质车辆。该预测能力使车辆设计师与制造商能够及时优化和改进设计方案，提升产品性能，并有效吸引消费者。然而，现有数据驱动方法大多依赖单一模态数据（如文本、图像或参数数据），导致对可用信息的探索存在局限性与不完整性。这类方法缺乏对多模态数据的综合分析，可能引发结论偏差，阻碍该领域发展。为突破这一局限，我们提出一种用于车辆评分预测的多模态学习模型，旨在实现更全面准确的结果。具体而言，该模型同步学习车辆的参数规格、文本描述与图像特征，以预测包括总分、专家评分、性能分、安全分与内饰分在内的五项车辆评分。我们将多模态模型与对应单模态模型进行对比，发现多模态模型的解释力比单模态模型高出4%-12%。在此基础上，我们采用SHAP方法进行敏感性分析以解释模型，为设计师与制造商提供设计优化方向。本研究揭示了数据驱动多模态学习方法在车辆设计、评估与优化中的重要性。相关代码已开源至http://decode.mit.edu/projects/vehicleratings/。