Ford Motor Company

About: Ford Motor Company is a company organization based out in Dearborn, Michigan, United States. It is known for research contribution in the topics: Internal combustion engine & Signal. The organization has 36123 authors who have published 51450 publications receiving 855200 citations. The organization is also known as: Ford Motor & Ford Motor Corporation.

Functional MRI-BOLD of brainstem structures during visually triggered migraine

Abstract: Background: Previously, hyperoxia and blood volume increase were reported in the red nucleus and substantia nigra during spontaneous migraine with aura. Objective: To further understand the pathophysiologic role of these centers, activation of brainstem structures was investigated in patients with visually triggered migraine. Methods: Twenty-six patients with migraine (23 with aura and 3 without aura), and 10 normal control subjects were studied with blood oxygen level–dependent (BOLD) fMRI during repeated checkerboard visual stimulation. Three axial image sections, which covered the occipital cortex and brainstem, were acquired 224 times with a temporal resolution of 3.5 seconds. Results: Repetitive visual stimulation triggered symptoms in 12 patients; four who had migraine with aura developed both visual symptoms and headaches, and six who had migraine with aura and two who had migraine without aura had headaches only. Four patients who had migraine with aura experienced the onset of their usual aura or onset of their typical headache either during the experiment or immediately after. In the remaining 10 patients with migraine, and all control subjects, visual stimulation failed to trigger symptoms at any time. In 75% of the patients who developed symptoms during stimulation, baseline T2*-weighted MR signal intensities increased in the red nucleus and substantia nigra before occipital cortex signal elevation or the onset of visually triggered symptoms. Conclusion: Activation (hyperoxia and blood volume increase) of the red nucleus and substantia nigra in association with visually triggered symptoms of migraine suggest that these brainstem structures are a part of a neuronal network activated during an attack.

Identification of driver state for lane-keeping tasks

Abstract: Identification of driver state is a desirable element of many proposed vehicle active safety systems (e.g., collision detection and avoidance, automated highway, and road departure warning systems). In the paper, driver state assessment is considered in the context of a road departure warning and intervention system. A system identification approach, using vehicle lateral position as the input and steering wheel position as the output, is used to develop a model and to update its parameters during driving. Preliminary driving simulator results indicate that changes in the bandwidth and/or parameters of such a model may be useful indicators of driver fatigue. The approach is then applied to data from 12 2-h highway driving runs conducted in a full-vehicle driving simulator. The identified model parameters (/spl zeta/ /spl omega//sub n/, and DC gain) do not exhibit the trends expected as lane keeping performance deteriorates, despite having acceptably white residuals. As an alternative, model residuals are compared in a process monitoring approach using a model fit to an early portion of the 2-h driver run. Model residuals show the expected trends and have potential in serving as the basis for a driver state monitor.

A human-like game theory-based controller for automatic lane changing

Abstract: Lane changing is a critical task for autonomous driving, especially in heavy traffic. Numerous automatic lane-changing algorithms have been proposed. However, surrounding vehicles are usually treated as moving obstacles without considering the interaction between vehicles/drivers. This paper presents a game theory-based lane-changing model, which mimics human behavior by interacting with surrounding drivers using the turn signal and lateral moves. The aggressiveness of the surrounding vehicles/drivers is estimated based on their reactions. With this model, the controller is capable of extracting information and learning from the interaction in real time. As such, the optimal timing and acceleration for changing lanes with respect to a variety of aggressiveness in target lane vehicle behavior are found accordingly. The game theory-based controller was tested in Simulink and dSPACE. Scenarios were designed so that a vehicle controlled by a game theory-based controller could interact with vehicles controlled by both robot and human drivers. Test results show that the game theory-based controller is capable of changing lanes in a human-like manner and outperforms fixed rule-based controllers.

Method and apparatus for maintaining catalyst efficiency of a NOx trap

Abstract: An engine control computer estimates the cumulative NOx storage in a NOx trap based on engine operating conditions and enters a NOx purge mode of operation when a maximum storage capacity limit, based on trap temperature, is exceeded. The purge mode is terminated when NOx storage level decreases below a minimum storage capacity limit. The rate of NOx dissipation is also based on trap temperature.