A novel approach to improve the social acceptance of autonomous driving vehicles by recognizing the emotions of passengers

Marceddu, Antonio Costantino; Sini, Jacopo; Violante, M.; Montrucchio, Bartolomeo

doi:10.1117/12.2586417

Cited by 3 publications

(2 citation statements)

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“…For this reason, the use of categorization supergroups and the visualization of the multitude of characteristics that an object can have is extremely important for a machine learning system. The labeling process must, therefore, be carried out painstakingly, reducing the number of errors to what is humanly possible [ 51 ].…”

Section: Methodsmentioning

confidence: 99%

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A Novel Redundant Validation IoT System for Affective Learning Based on Facial Expressions and Biological Signals

Marceddu

Pugliese

Sini

et al. 2022

Sensors

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Teaching is an activity that requires understanding the class’s reaction to evaluate the teaching methodology effectiveness. This operation can be easy to achieve in small classrooms, while it may be challenging to do in classes of 50 or more students. This paper proposes a novel Internet of Things (IoT) system to aid teachers in their work based on the redundant use of non-invasive techniques such as facial expression recognition and physiological data analysis. Facial expression recognition is performed using a Convolutional Neural Network (CNN), while physiological data are obtained via Photoplethysmography (PPG). By recurring to Russel’s model, we grouped the most important Ekman’s facial expressions recognized by CNN into active and passive. Then, operations such as thresholding and windowing were performed to make it possible to compare and analyze the results from both sources. Using a window size of 100 samples, both sources have detected a level of attention of about 55.5% for the in-presence lectures tests. By comparing results coming from in-presence and pre-recorded remote lectures, it is possible to note that, thanks to validation with physiological data, facial expressions alone seem useful in determining students’ level of attention for in-presence lectures.

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Section: Methodsmentioning

confidence: 99%

“…Facial expressions prediction was achieved through the use of Emotion Detector (ED) software [ 51 , 58 ]. It was developed by recurring to: Java; Apache Maven; OpenCV [ 59 ]; DeepLearning4J (dl4j) [ 60 ].…”

Section: Methodsmentioning

confidence: 99%

A Novel Redundant Validation IoT System for Affective Learning Based on Facial Expressions and Biological Signals

Marceddu

Pugliese

Sini

et al. 2022

Sensors

Self Cite

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UnIC: Towards Unmanned Intelligent Cluster and Its Integration into Society

2022

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Towards Human-Compatible Autonomous Car: A Study of Non-Verbal Turing Test in Automated Driving With Affective Transition Modelling

Jiang

et al. 2024

IEEE Trans. Affective Comput.

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We tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario. We designed a ride experience-based version of the non-verbal Turing test for automated driving. Participants rode in autonomous cars as a passenger and judged whether the driver was human or AI. The AI driver failed to pass our test because passengers detected the AI driver above chance. We further investigated how human passengers ascribe humanness in our test. Based on Lewin's field theory, we advanced a computational model combining signal detection theory with pre-trained language models to predict passengers' humanness rating behaviour. We employed affective transition between pre-study baseline and corresponding post-stage emotions as the signal strength of our model. Results showed that the passengers' ascription of humanness would increase with the greater affective transition, indicating an important role of affective transition in passengers' ascription of humanness.

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A novel approach to improve the social acceptance of autonomous driving vehicles by recognizing the emotions of passengers

Cited by 3 publications

References 0 publications

A Novel Redundant Validation IoT System for Affective Learning Based on Facial Expressions and Biological Signals

A Novel Redundant Validation IoT System for Affective Learning Based on Facial Expressions and Biological Signals

UnIC: Towards Unmanned Intelligent Cluster and Its Integration into Society

Towards Human-Compatible Autonomous Car: A Study of Non-Verbal Turing Test in Automated Driving With Affective Transition Modelling

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