IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant

Tianbo Ji, Xuanhua Yin, Peng Cheng, Liting Zhou, Siyou Liu, Wei Bao () and Chenyang Lyu
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Tianbo Ji: School of Transportation and Civil Engineering, Nantong University, Nantong 226000, China
Xuanhua Yin: School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK
Peng Cheng: Alibaba Group, Hangzhou 311121, China
Liting Zhou: ADAPT Centre, School of Computing, Dublin City University, D09 DXA0 Dublin, Ireland
Siyou Liu: Faculty of Languages and Translation, Macao Polytechnic University, Macao, China
Wei Bao: China Electronics Standardization Institute, Beijing 101102, China
Chenyang Lyu: SFI Centre for Research Training in Machine Learning, School of Computing, Dublin City University, D09 DXA0 Dublin, Ireland

IJERPH, 2022, vol. 19, issue 23, 1-19

Abstract: An advanced driver simulator methodology facilitates a well-connected interaction between the environment and drivers. Multiple traffic information environment language processing aims to help drivers accommodate travel demand: safety prewarning, destination navigation, hotel/restaurant reservation, and so on. Task-oriented dialogue systems generally aim to assist human users in achieving these specific goals by a conversation in the form of natural language. The development of current neural network based dialogue systems relies on relevant datasets, such as KVRET. These datasets are generally used for training and evaluating a dialogue agent (e.g., an in-vehicle assistant). Therefore, a simulator for the human user side is necessarily required for assessing an agent system if no real person is involved. We propose a new end-to-end simulator to operate as a human driver that is capable of understanding and responding to assistant utterances. This proposed driver simulator enables one to interact with an in-vehicle assistant like a real person, and the diversity of conversations can be simply controlled by changing the assigned driver profile. Results of our experiment demonstrate that this proposed simulator achieves the best performance on all tasks compared with other models.

Keywords: transportation and interdisciplinary application; driver–vehicle interaction; machine learning; natural language processing; task-oriented dialogue (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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