Towards leveraging the driver's mobile device for an intelligent, sociable in-car robotic assistant

Williams, Kenton; Peters, Joshua; Breazeal, Cynthia

doi:10.1109/ivs.2013.6629497

Cited by 37 publications

(17 citation statements)

References 9 publications

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“…Driving context recognition is useful especially for multiple ADASs to select and suggest appropriate driving assistance for the driver and is also a driving assistant with artificial intelligence agent [18]. Further data gathering to improve recognition accuracy and on-line processing to keep supporting the driver during driving are important future work.…”

Section: Discussionmentioning

confidence: 99%

Generating contextual description from driving behavioral data

Bando

Takenaka

Nagasaka

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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This paper presents an automatic translation method from time-series driving behavior into natural language with contextual information. Nowadays, various advanced driver-assistance systems (ADASs) have been developed to reduce the number of traffic accidents and multiple ADASs are required to reduce further accidents. For such multiple ADASs, considering the context of driving and selecting appropriate assistance is key because the systems have to handle extremely complicated driving situations consisting of drivers (and their intents and maneuvers), environments (including other traffic participants such as vehicles and pedestrians), and vehicles dynamics. In this paper, time-series driving behavior is segmented into typical driving situation symbols, and the natural language expression of each situation is generated via the behavioral feature distribution observed in each situation. Owing to the symbolization of the driving behavior, the generated behavioral descriptions can be associated with their causes and results not on an actual time axis but on a situation-symbol axis as contextual descriptions, e.g., "letting up on gas pedal to pass tollgate." The effectiveness of the proposed method was evaluated by using an actual data set of more than eight hours over a distance of 300 km in total. Although contextual expressions are very diverse even among human drivers, the proposed method obtained an agreement of more than 70%.

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Section: Discussionmentioning

confidence: 99%

Generating contextual description from driving behavioral data

Bando

Takenaka

Nagasaka

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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“…The majority of the collected segments lies in the [0, 1] (43.80% for valence and 46.22% for arousal) and [1,2] (31.28% for valence and 45.33% for arousal) value ranges. There is little coverage of the extreme values (−3 and 3) and the negative spectrum of either dimensions.…”

Section: Emotion Label Distributionmentioning

confidence: 99%

“…Further, researchers and scholars are putting the effort into real life applications through the construction of complex and emotionally advanced system, in particular spoken dialogue systems, e.g. Sensitive Artificial Listener [1], personable in-car assistant [2], and an embodied conversational companion [3]. This increasing interest in the topic is partly owing to the challenges held globally.…”

Section: Introductionmentioning

confidence: 99%

Construction of Spontaneous Emotion Corpus from Indonesian TV Talk Shows and Its Application on Multimodal Emotion Recognition

Lubis

Lestari

Sakti

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARY As interaction between human and computer continues to develop to the most natural form possible, it becomes increasingly urgent to incorporate emotion in the equation. This paper describes a step toward extending the research on emotion recognition to Indonesian. The field continues to develop, yet exploration of the subject in Indonesian is still lacking. In particular, this paper highlights two contributions: (1) the construction of the first emotional audio-visual database in Indonesian, and (2) the first multimodal emotion recognizer in Indonesian, built from the aforementioned corpus. In constructing the corpus, we aim at natural emotions that are corresponding to real life occurrences. However, the collection of emotional corpora is notably labor intensive and expensive. To diminish the cost, we collect the emotional data from television programs recordings, eliminating the need of an elaborate recording set up and experienced participants. In particular, we choose television talk shows due to its natural conversational content, yielding spontaneous emotion occurrences. To cover a broad range of emotions, we collected three episodes in different genres: politics, humanity, and entertainment. In this paper, we report points of analysis of the data and annotations. The acquisition of the emotion corpus serves as a foundation in further research on emotion. Subsequently, in the experiment, we employ the support vector machine (SVM) algorithm to model the emotions in the collected data. We perform multimodal emotion recognition utilizing the predictions of three modalities: acoustic, semantic, and visual. When compared to the unimodal result, in the multimodal feature combination, we attain identical accuracy for the arousal at 92.6%, and a significant improvement for the valence classification task at 93.8%. We hope to continue this work and move towards a finer-grain, more precise quantification of emotion.

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“…Over the years, this issue continues to be addressed. This results in the development in the field of affective computing, through the construction of complex and emotionally advanced systems such as Sensitive Artificial Listener [1], personable in-car assistant [2], and even system that helps with emotional memory [3]. However, the majority of the advancements in affective computing are in English.…”

Section: Introductionmentioning

confidence: 99%

Construction and analysis of Indonesian Emotional Speech Corpus

Lubis

Lestari

Purwarianti

et al. 2014

2014 17th Oriental Chapter of the International Committee for the Co-Ordination and Standardization of Speech Databases and Ass

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In this paper we present Indonesian Emotional Speech Corpus (IDESC), the first ever corpus in Indonesian that contains various emotion contents. As interaction between human and computer makes its way to the most natural form possible, it becomes more and more urgent to incorporate emotion in the equation. However, in Indonesian, this aspect is yet to be explored. The acquisition of an emotion corpus serves as a foundation in further research regarding the subject. In constructing IDESC, we aim at natural and real emotion that is applicable to humancomputer interaction. The corpus consists of three episodes of Indonesian talk show in different genres: politics, humanity, and entertainment. Each episode is carefully segmented and labeled based on its emotion content, resulting in 2179 segments worth 1 hour, 34 minutes, and 49.7 seconds of speech. The corpus is still in its early stage of development, yielding exciting possibilities of future works.

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Towards leveraging the driver's mobile device for an intelligent, sociable in-car robotic assistant

Cited by 37 publications

References 9 publications

Generating contextual description from driving behavioral data

Generating contextual description from driving behavioral data

Construction of Spontaneous Emotion Corpus from Indonesian TV Talk Shows and Its Application on Multimodal Emotion Recognition

Construction and analysis of Indonesian Emotional Speech Corpus

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