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Visteon Automotive Systems has developed an Electro-Hydraulic Power Assist Steering (EHPAS) System. This low-cost system uses conventional hydraulic power steering components with an electrically-driven and electronically-controlled power steering pump. This paper presents the Visteon EHPAS system and its development process. This process began with analytical modeling of the EHPAS system and integration of these models with a two degree of freedom (2DOF) vehicle model. These models were critical for system analysis and control strategy design. The EHPAS system sizing procedure and control strategy performance optimization were verified with the use of a real-time computer designed by Ford Motor Company, and by specially-designed Visteon test benches. Finally, EHPAS equipped test vehicles were tuned for high performance, providing better feel and fuel economy than conventionally equipped base line vehicles.

Increasingly sophisticated vehicle automation can perform steering and speed control, allowing the driver to disengage from driving. However, vehicle automation may not be capable of handling all roadway situations and driver intervention may be required in such situations. The typical approach is to indicate vehicle capability through displays and warnings, but control algorithms can also signal capability. Psychophysical methods can be used to link perceptual experiences to physical stimuli. In this situation, trust is an important perceptual experience related to automation capability that is revealed by the physical stimuli produced by different control algorithms. For instance, precisely centering the vehicle in the lane may indicate a highly capable system, whereas simply keeping the vehicle within lane boundaries may signal diminished capability.

Distracted driving remains a serious risk to motorists in the US and worldwide. Over 3,000 people were killed in 2013 in the US because of distracted driving; and over 420,000 people were injured. A system that can accurately detect distracted driving would potentially be able to alert drivers, bringing their attention back to the primary driving task and potentially saving lives. This paper documents an effort to develop an algorithm that can detect visual distraction using vehicle-based sensor signals such as steering wheel inputs and lane position. Additionally, the vehicle-based algorithm is compared with a version that includes driving-based signals in the form of head tracking data. The algorithms were developed using machine learning techniques and combine a Random Forest model for instantaneous detection with a Hidden Markov model for time series predictions.