AIMC Topic: Risk-Taking

Clear Filters Showing 11 to 20 of 26 articles

Virtual agents and risk-taking behavior in adolescence: the twofold nature of nudging.

Scientific reports
Peer pressure can influence risk-taking behavior and it is particularly felt during adolescence. With artificial intelligence (AI) increasingly present in a range of everyday human contexts, including virtual environments, it is important to examine ...

Supporting Adolescent Engagement with Artificial Intelligence-Driven Digital Health Behavior Change Interventions.

Journal of medical Internet research
Understanding and optimizing adolescent-specific engagement with behavior change interventions will open doors for providers to promote healthy changes in an age group that is simultaneously difficult to engage and especially important to affect. For...

Behavioural Models of Risk-Taking in Human-Robot Tactile Interactions.

Sensors (Basel, Switzerland)
Touch can have a strong effect on interactions between people, and as such, it is expected to be important to the interactions people have with robots. In an earlier work, we showed that the intensity of tactile interaction with a robot can change ho...

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data.

PloS one
Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensor...

The Robot Made Me Do It: Human-Robot Interaction and Risk-Taking Behavior.

Cyberpsychology, behavior and social networking
Empirical evidence has shown that peer pressure can impact human risk-taking behavior. With robots becoming ever more present in a range of human settings, it is crucial to examine whether robots can have a similar impact. Using the balloon analogue ...

Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting.

International journal of environmental research and public health
Real-time recognition of risky driving behavior and aggressive drivers is a promising research domain, thanks to powerful machine learning algorithms and the big data provided by in-vehicle and roadside sensors. However, since the occurrence of aggre...

FastEmbed: Predicting vulnerability exploitation possibility based on ensemble machine learning algorithm.

PloS one
In recent years, the number of vulnerabilities discovered and publicly disclosed has shown a sharp upward trend. However, the value of exploitation of vulnerabilities varies for attackers, considering that only a small fraction of vulnerabilities are...

Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework.

International journal of environmental research and public health
The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing r...

Pedestrian's risk-based negotiation model for self-driving vehicles to get the right of way.

Accident; analysis and prevention
Negotiations among drivers and pedestrians are common on roads, but it is still challenging for a self-driving vehicle to negotiate for its right of way with other human road users, especially pedestrians. Currently, the self-driving vehicles are pro...