Publications by authors named "D A Abbink"
Article Synopsis
- Researchers explored how visual feedback on interaction forces enhances the performance of haptic-assisted teleoperation for robotic arms in industrial tasks.
- They developed a new method for providing visual cues in a virtual environment and evaluated it alongside a head-mounted display during experiments focused on dross removal.
- Results demonstrated that both methods improved task performance, with visual cues enhancing safety and the head-mounted display significantly boosting overall performance, leading to higher user acceptance of both approaches.
A task as simple as holding a cup between your fingers generates complex motor commands to finely regulate the forces applied by muscles. These fine force adjustments ensure the stability and integrity of the object by preventing it from slipping out of grip during manipulation and by reacting to perturbations. To do so, our sensorimotor system constantly monitors tactile and proprioceptive information about the force object exerts on fingertips and the friction of the surfaces to determine the optimal grip force.
View Article and Find Full Text PDFSafe and socially acceptable interactions with human-driven vehicles are a major challenge in automated driving. A good understanding of the underlying principles of such traffic interactions could help address this challenge. Particularly, accurate driver models could be used to inform automated vehicles in interactions.
View Article and Find Full Text PDFA major challenge for autonomous vehicles is handling interactions with human-driven vehicles-for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour.
View Article and Find Full Text PDFObjective: We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers.
Background: Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes.