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Found 7 papers, 3 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.
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.
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.
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).
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.
Multimodal Fusion Using Deep Learning Applied to Driver's Referencing of Outside-Vehicle Objects
We demonstrate the practical limitations of each modality when used for a natural form of referencing, specifically inside the car.
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.
