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Found 13 papers, 6 papers with code
Looking for a better fit? An Incremental Learning Multimodal Object Referencing Framework adapting to Individual Drivers
The rapid advancement of the automotive industry towards automated and semi-automated vehicles has rendered traditional methods of vehicle interaction, such as touch-based and voice command systems, inadequate for a widening range of non-driving related tasks, such as referencing objects outside of the vehicle.
Toward a Surgeon-in-the-Loop Ophthalmic Robotic Apprentice using Reinforcement and Imitation Learning
Thus, we propose a simulation-based image-guided approach for surgeon-centered autonomous agents that can adapt to the individual surgeon's skill level and preferred surgical techniques during ophthalmic cataract surgery.
It's all about you: Personalized in-Vehicle Gesture Recognition with a Time-of-Flight Camera
Despite significant advances in gesture recognition technology, recognizing gestures in a driving environment remains challenging due to limited and costly data and its dynamic, ever-changing nature.
LLM-Deliberation: Evaluating LLMs with Interactive Multi-Agent Negotiation Games
There is a growing interest in using Large Language Models (LLMs) as agents to tackle real-world tasks that may require assessing complex situations.
Adaptive User-centered Neuro-symbolic Learning for Multimodal Interaction with Autonomous Systems
Recent advances in machine learning, particularly deep learning, have enabled autonomous systems to perceive and comprehend objects and their environments in a perceptual subsymbolic manner.
SynthoGestures: A Novel Framework for Synthetic Dynamic Hand Gesture Generation for Driving Scenarios
Creating a diverse and comprehensive dataset of hand gestures for dynamic human-machine interfaces in the automotive domain can be challenging and time-consuming.
Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems
Recent advances in machine learning models allowed robots to identify objects on a perceptual nonsymbolic level (e. g., through sensor fusion and natural language understanding).
Adaptive User-Centered Multimodal Interaction towards Reliable and Trusted Automotive Interfaces
With the recently increasing capabilities of modern vehicles, novel approaches for interaction emerged that go beyond traditional touch-based and voice command approaches.
In-Vehicle Interface Adaptation to Environment-Induced Cognitive Workload
Many car accidents are caused by human distractions, including cognitive distractions.
What's on your mind? A Mental and Perceptual Load Estimation Framework towards Adaptive In-vehicle Interaction while Driving
In this paper, we analyze the effects of mental workload and perceptual load on psychophysiological dimensions and provide a machine learning-based framework for mental and perceptual load estimation in a dual task scenario for in-vehicle interaction (https://github. com/amrgomaaelhady/MWL-PL-estimator).
ML-PersRef: A Machine Learning-based Personalized Multimodal Fusion Approach for Referencing Outside Objects From a Moving Vehicle
This allows for novel approaches to interaction with the vehicle that go beyond traditional touch-based and voice command approaches, such as emotion recognition, head rotation, eye gaze, and pointing gestures.
Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle
Hand pointing and eye gaze have been extensively investigated in automotive applications for object selection and referencing.
