Matching Items (2)

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Investigating the embodied effect in drivers' safe headway learning

Description

Safe headway learning plays a core role in driving education. Traditional safe headway education just use the oral and literal methods to educate drivers the concept of safe headway time,

Safe headway learning plays a core role in driving education. Traditional safe headway education just use the oral and literal methods to educate drivers the concept of safe headway time, while with the limitation of combining drivers subject and situational domains for drivers to learn. This study investigated that whether using ego-moving metaphor to embody driver's self-awareness can help to solve this problem. This study used multiple treatments (ego-moving and time-moving instruction of safe time headway) and controls with pretest experimental design to investigate the embody self-awareness effect in a car-following task. Drivers (N=40) were asked to follow a lead car at a 2-seconds safe time headway. Results found that using embodied-based instructions in safe headway learning can help to improve driver's headway time accuracy and performance stability in the car-following task, which supports the hypothesis that using embodied-based instructions help to facilitate safe headway learning. However, there are still some issues needed to be solved using embodied-based instructions for the drivers' safe headway education. This study serves as a new method for the safe headway education while providing empirical evidence for the embodied theories and their applications.

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Created

Date Created
  • 2016

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Human inspired control system for an unmanned ground vehicle

Description

In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem

In this research work, a novel control system strategy for the robust control of an unmanned ground vehicle is proposed. This strategy is motivated by efforts to mitigate the problem for scenarios in which the human operator is unable to properly communicate with the vehicle. This novel control system strategy consisted of three major components: I.) Two independent intelligent controllers, II.) An intelligent navigation system, and III.) An intelligent controller tuning unit. The inner workings of the first two components are based off the Brain Emotional Learning (BEL), which is a mathematical model of the Amygdala-Orbitofrontal, a region in mammalians brain known to be responsible for emotional learning. Simulation results demonstrated the implementation of the BEL model to be very robust, efficient, and adaptable to dynamical changes in its application as controller and as a sensor fusion filter for an unmanned ground vehicle. These results were obtained with significantly less computational cost when compared to traditional methods for control and sensor fusion. For the intelligent controller tuning unit, the implementation of a human emotion recognition system was investigated. This system was utilized for the classification of driving behavior. Results from experiments showed that the affective states of the driver are accurately captured. However, the driver's affective state is not a good indicator of the driver's driving behavior. As a result, an alternative method for classifying driving behavior from the driver's brain activity was explored. This method proved to be successful at classifying the driver's behavior. It obtained results comparable to the common approach through vehicle parameters. This alternative approach has the advantage of directly classifying driving behavior from the driver, which is of particular use in UGV domain because the operator's information is readily available. The classified driving mode was used tune the controllers' performance to a desired mode of operation. Such qualities are required for a contingency control system that would allow the vehicle to operate with no operator inputs.

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Agent

Created

Date Created
  • 2015