Emotion-awareness for intelligent vehicle assistants

Vogel, Hans‐Jörg; SuB, Christian; Hubregtsen, Thomas; André, Elisabeth; Schuller, Björn; Härri, Jérôme; Conradt, Jörg; Adi, Asaf; Zadorojniy, Alexander; Terken, Jacques; Beskow, Jonas; Morrison, Ann; Eng, Kynan; Eyben, Florian; Moubayed, Samer Al; Müller, Susanne; Cummins, Nicholas; Ghaderi, Viviane S.; Chadowitz, Ronee; Troncy, Raphaël; Huet, Benoît; Önen, Melek; Ksentini, Adlen

doi:10.1145/3194085.3194094

Cited by 30 publications

(20 citation statements)

References 10 publications

(9 reference statements)

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“…For instance, federated models can collectively identify, assess and countermeasure the risk of more complex vehicular scenarios than each of them in isolation [ 180 ]. Likewise, ATIS systems can learn from the preferences and habits of other users to better anticipate the preferences of the driver and act accordingly [ 181 ]. In a few words: an enhanced and more effective actionability of the data-based workflows built to undertake such tasks.…”

Section: Emerging Ai Areas Towards Actionable Itsmentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Laña

Medina

Vlahogianni

et al. 2021

Sensors

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Advances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.

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Section: Emerging Ai Areas Towards Actionable Itsmentioning

confidence: 99%

From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability

Laña

Medina

Vlahogianni

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…Intelligent environments may use mixed reality for visualization [83,84] and natural interaction metaphors [85] that let the user intuitively use the system. They identify and monitor the user [86], read intentions [87], and personalize their services. By also monitoring the environment [88], they infer circumstances, and adapt [89] their services accordingly.…”

Section: User Experience and Car Multimodal Interfacesmentioning

confidence: 99%

Ubiquitous Computing: Driving in the Intelligent Environment

et al. 2021

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In the context of hyper-connected cars and a growing heterogeneous digital ecosystem, we wish to make the most of the data available from the various sensors, devices and services that compose the ecosystem, in order to propose a proof of concept in-vehicle system that enhances the driving experience. We focus on improving the driving experience along three main directions, namely: (1) driving and trip planning, (2) health and well-being and (3) social and online activities. We approached the in-vehicle space as a smart interface to the intelligent driving environment. The digital data-producers in the ecosystem of the connected car are sources of raw data of various categories, such as data from the outside world, gathered from sensors or online services, data from the car itself and data from the driver gathered with various mobile and wearable devices, by means of observing his state and by means of his social media and online activity. Data is later processed into three information categories—driving, wellness, and social—and used to provide multi-modal interaction, namely visual, audio and gesture. The system is implemented to act in response to the trafficked information on different levels of autonomy, either in a reactive manner, by simple monitoring, or in a proactive manner. The system is designed to provide an in-vehicle system that assists the driver with planning the travel (Drive panel), by providing a comfortable environment for the driver while monitoring him (Wellness panel), and by adaptively managing interactions with their phone and the digital environment (Social panel). Heuristic evaluation of the system is performed, with respect to guidelines formulated for automated vehicles, and a SWOT analysis of the system is also presented in the paper.

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“…To help tomorrow's passenger enjoy the ride, we suggest to repurpose the technology for use in empathic assistants [4,5,6,7]. An in-car empathic assistant is a digital assistant whose goal is to make driving comfortable and safe by catering to the user's needs.…”

Section: Introductionmentioning

confidence: 99%