Understanding User Requirements for Smart Cockpit of New Energy Vehicles: A Natural Language Process Approach 2022-01-7075

The smart cockpit has become an irreplaceable element for many new automobile brands, particularly New Energy Vehicles (NEV) of “new forces”. Since the cockpit is a direct interface for the interactions between users and the intelligent and connected functions of the vehicle, any improvements would be easily perceived by users and thus would directly affect user experiences. It would be most important to capture, collect, and understand what users need for a smart cockpit.

Users’ online comments on existing smart cockpits contain information on users’ requirements. However, the current user comment text data is too massive, tanglesome, and sparse to process. How to efficiently mine valuable information from these data is non-trivial. This paper focuses on applying the Natural Language Process (NLP) technology for design, development, improvement, and update of a vehicle company’s smart cockpit. By obtaining user comment data from various sources such as eco-system Applications (APP), forums, posts, Questions and Answers(Q&A), customer services, etc., we aim to mine and quantify user demand for the smart cockpit. A deep learning NLP model named Bidirectional Encoder Representations from Transformers (BERT) is developed. In addition, the incremental pre-trained BERT is proposed to predict the mentioned cockpit feature, user’s intention, and emotion from a comment, with one year’s data from our cooperated NEV company for model training. Experiment results showed that our model outperforms the conventional BERT in terms of predictive ability and consumed time. Applications in the cooperated company were discussed.