In-vehicle Technologies, Advanced Driver Assistance Systems and Driver Distraction: Research challenges

Source: OAI


Technological advances in motor vehicles have provided drivers with both increased safety and access to information. Drivers can receive phone calls, be provided with navigational and real time traffic information, and be notified about impending collisions and excessive speed. However, these devices also increase the potential for a driver to be distracted, as each device demands a certain level of the driver’s attention in order to provide a benefit. A growing body of research is currently assessing driver distraction levels in order to determine what impact such devices have on road safety. However, very little research has focused specifically on the combined impact of multiple in-vehicle devices within the driving situation. As a result, this paper provides a review of current research that has examined the effect of in-vehicle technologies and Advanced Driving Assistance Systems (ADAS) on driver distraction, as well as identifying possible directions for future research that will incorporate human distraction within the design.

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Available from: A. Rakotonirainy, Sep 02, 2015
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    • "Wang et al apud Burns [4] stated that 13% of accidents with vehicles in US were related to visual distraction and part of this could be related to the use of IVIS. Brooks [3] also states that the higher the system's visual demand the more dangerous the driving task. "
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    ABSTRACT: Advanced Driver Assistance (ADA) systems currently operate within vehicles, offering drivers assistance to either avoid hazardous situations, or information to make travelling easier. However, these devices have the potential to contribute to driver distraction as they require a certain level of driver attention in order to provide a benefit, taking cognitive, visual, auditory, and manual resources away from the main driving task. As these systems become more prolific in the market, the potential number of devices that can operate within a vehicle at any one time increases. Therefore, this paper presents a new in-vehicle architecture to unobtrusively reduce ADA system related distraction. Our approach consists of sensing and assessing the current driving context. The context is gathered by an in-vehicle system which senses and articulates relevant information about the environment, driver and vehicle. We use Bayesian Networks in our architecture to assess driving distraction and to identify an optimal way to interact with the driver. This paper will address the assessment of driver distraction based on contextual information in relation to the vehicle, the environment, and the driver.
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