Conversational In-Vehicle Dialog Systems: The past, present, and future

By Fuliang Weng, Pongtep Angkititrakul, Elizabeth Shriberg, Larry Heck, S. Peters, John H. L. Hansen. 2016

53 citations recorded by the source corpus.

Automotive technology rapidly advances with increasing connectivity and automation. These advancements aim to assist safe driving and improve user travel experience. Before the realization of a full automation, in-vehicle dialog systems may reduce the driver distraction from many services available through connectivity. Even when a full automation is realized, in-vehicle dialog systems still play a special role in assisting vehicle occupants to perform various tasks. On the other hand, in-vehicle use cases need to address very different user conditions, environments, and industry requirements than other uses. This makes the development of effective and efficient in-vehicle dialog systems challenging; it requires multidisciplinary expertise in automatic speech recognition, spoken language understanding, dialog management (DM), natural language generation, and application management, as well as field system and safety testing. In this article, we review research and development (R&D) activities for in-vehicle dialog systems from both academic and industrial perspectives, examine findings, discuss key challenges, and share our visions for voice-enabled interaction and intelligent assistance for smart vehicles over the next decade.

Subfields: Artificial Intelligence, Natural Language Processing, Dialogue & Conversational AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

In Dialogue & Conversational AI

Citations in the corpus

Ethical Challenges in Data-Driven Dialogue Systems (2017, background): “dy for this. In addition to the challenges faced by general-purpose dialog systems, in-vehicle dialog systems face additional concerns regarding the added variables of the driver and the environment (Weng et al. 2016). As the focus and safety of the driver is paramount, dialog systems must be cogniz”
Driver Modeling for Detection and Assessment of Driver Distraction: Examples from the UTDrive Test Bed (2017, background): “132 IEEE SIgnal ProcESSIng MagazInE | July 2017 | A recent IEEE Signal Processing Magazine special issue, “Smart Vehicle Technologies: Signal Processing on the Move” [2], considered a range of topics for smart vehicles advancements that included driver behavior modeling using on-road driving data [3”