  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/.
