Personality trait prediction by machine learning using physiological data and driving behavior

Evin, Morgane; Hidalgo‐Muñoz, Antonio R.; Béquet, Adolphe J.; Moreau, Fabien; Tattegrain, Hélène; Berthelon, Catherine; Fort, Alexandra; Jallais, Christophe

doi:10.1016/j.mlwa.2022.100353

Cited by 22 publications

(8 citation statements)

References 41 publications

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“…In future work, it could be useful to test the result, first on a larger sample and second to better understand the feelings induced: are they integrated in a basic trend or are they temporary, and how can they be explained? This work can be improved by considering for example a STAI-Y-Trait (state–trait anxiety inventory) which allows assessing the susceptibility to feel stress or anxiety ( 39 ).…”

Section: Resultsmentioning

confidence: 99%

Dream and Reality for Autonomous Vehicles: Results and Lessons from an In Situ Experiment

Souche

Laroche

Crispim-Junior

2023

Transportation Research Record: Journal of the Transportation R

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The concept of the autonomous vehicle (AV) is becoming a credible alternative for the coming decades. Interest in AVs has been mirrored by a strong increase in the number of academic papers on the subject. However, few studies have considered a genuinely autonomous vehicle or similar in a realistic environment. They are limited to autonomous shuttles and restricted roads to test attitudes to and acceptance of AVs. This paper reports an exploratory experiment carried out on AV use in real conditions. The objective is to analyze the evolution of AV representation by participants ( n = 30) before and after the experiment, between dreams about AVs and reality. The feelings and attitudes induced by AVs and the activities that could be performed in them are considered. The findings show that many of the participants were initially in favor of AVs and were very excited about the idea of the test. Most of them did not feel any stress and the level of trust was high. After the experiment, feelings such as stress and fear increased and the level of trust decreased. Finally, the two main activities declared by the participants before the experiment were similar to the declaration after, being, respectively “watching the road” and “watching through windows.”

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Section: Resultsmentioning

confidence: 99%

Dream and Reality for Autonomous Vehicles: Results and Lessons from an In Situ Experiment

Souche

Laroche

Crispim-Junior

2023

Transportation Research Record: Journal of the Transportation R

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“…The concept of creating interfaces that are based on personality was developed by Amichai Hamburger (2002) [ 42 ]. The link and prediction ability between personality traits and physiological response has been previously studied [ 45 , 47 , 48 , 49 , 50 ], specifically in the AV setting [ 51 ]. Engagement with the technology and attention to it were critical to induce adequate interaction [ 52 ] and elevate levels of trust in and acceptance of it [ 53 ].…”

Section: Sensing Users and Tailoring The Experience Of The Autonomous...mentioning

confidence: 99%

“…Their study evaluated risky urban situation behaviors simultaneously with EDA activity. The machine learning protocols employed in the study resulted in 0.968 to 0.974 prediction levels with better detection of neuroticism, extroversion, and conscientiousness [ 48 ]. In a study by Childs, White, and De Wit (2014), individuals with high negative emotionality exhibited considerably more significant emotional distress and lower blood pressure responses to the Trier Social Stress Test [ 47 ].…”

Section: Sensing Users and Tailoring The Experience Of The Autonomous...mentioning

confidence: 99%

Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles

Giron,

Sela,

Barenboim

et al. 2024

Sensors

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The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional “one size fits all” approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger’s personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human–Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles.

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“…Inferred Attributes. With the appropriate ML algorithm [40], [100], [80], the discussed attributes above can reveal demographics [3] and other related sensitive attributes such as emotions [145], physical and mental health [62], [68], wealth, and political or sexual orientation or preferences over different users or products [41], [57], among others [33]. Users may also unintentionally or voluntarily selfdisclose such information or additional biographical data (e.g., age, home address, education, social status, work history, etc.)…”

Section: Vr Attributesmentioning

confidence: 99%

SoK: Data Privacy in Virtual Reality

Garrido¹,

Nair²,

Song³

2023

Preprint

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The adoption of virtual reality (VR) technologies has rapidly gained momentum in recent years as companies around the world begin to position the so-called "metaverse" as the next major medium for accessing and interacting with the internet. While consumers have become accustomed to a degree of data harvesting on the web, the real-time nature of data sharing in the metaverse indicates that privacy concerns are likely to be even more prevalent in the new "Web 3.0." Research into VR privacy has demonstrated that a plethora of sensitive personal information is observable by various wouldbe adversaries from just a few minutes of telemetry data. On the other hand, we have yet to see VR parallels for many privacy-preserving tools aimed at mitigating threats on conventional platforms. This paper aims to systematize knowledge on the landscape of VR privacy threats and countermeasures by proposing a comprehensive taxonomy of data attributes, protections, and adversaries based on the study of 68 collected publications. We complement our qualitative discussion with a statistical analysis of the risk associated with various data sources inherent to VR in consideration of the known attacks and defenses. By focusing on highlighting the clear outstanding opportunities, we hope to motivate and guide further research into this increasingly important field.

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Personality trait prediction by machine learning using physiological data and driving behavior

Cited by 22 publications

References 41 publications

Dream and Reality for Autonomous Vehicles: Results and Lessons from an In Situ Experiment

Dream and Reality for Autonomous Vehicles: Results and Lessons from an In Situ Experiment

Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles

SoK: Data Privacy in Virtual Reality

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