Emotion recognition for semi-autonomous vehicles framework

Izquierdo-Reyes, Javier; Ramírez-Mendoza, Ricardo A.; Bustamante-Bello, Rogelio; Pons-Rovira, Jose L.; González-Vargas, José

doi:10.1007/s12008-018-0473-9

Cited by 41 publications

(15 citation statements)

References 20 publications

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“…Our findings have implications on the design of in-vehicle emotion regulation systems. Advances in machine learning enable accurate detection of drivers' emotional states in real-time (Izquierdo-Reyes et al, 2018;Picard, 2003). For example, if the system detects that a driver is a negative emotional state, strategies such as reappraisal and distraction (Naragon-Gainey, McMahon, & Chacko, 2017) can be used to help the driver manage his or her negative emotion, resulting in better takeover performance.…”

Section: Resultsmentioning

confidence: 99%

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Zhou

Pulver

et al. 2020

SSRN Journal

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In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers' takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers' takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not.

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Section: Resultsmentioning

confidence: 99%

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Zhou

Pulver

et al. 2020

SSRN Journal

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“… LF/HF: this is the low-frequency (LF) power (0.04–0.15 Hz) modulated by the sympathetic and parasympathetic nervous system divided by the high-frequency (HF) power (0.15–0.4 Hz) associated with the parasympathetic nerve activity. This ratio captures the global sympathovagal balance [ 25 ]. A high LF/HF ratio means sympathetic dominance, which happens when driving stress is elevated.…”

Section: Methodsmentioning

confidence: 99%

“…[ 23 ]. During driving, the use of non-intrusive devices is essential so as not to affect driving performance or cause safety problems [ 24 , 25 ]. In [ 26 ], we can find a review of the solutions to monitor a driver’s psychological state.…”

Section: Introductionmentioning

confidence: 99%

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Magaña

Scherz²,

Seepold

et al. 2020

Sensors

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Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.

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“…Government, healthcare, retail, transportation, supply chain business, and many other applications are now using such emotion recognition systems for their business. For instance, automotive vehicles can use computer vision technology that helps to monitor the driver’s emotional state [ 147 ]. The sensors are used to interact with drivers to analyze emotional states, e.g., fatigue or drowsiness generate alerts for the driver.…”

Section: Current Trends and Future Directionsmentioning

confidence: 99%

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Pal

Mukhopadhyay

Suryadevara

2021

Sensors

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With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

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Emotion recognition for semi-autonomous vehicles framework

Cited by 41 publications

References 20 publications

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Development and Progress in Sensors and Technologies for Human Emotion Recognition

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