Develop In-Vehicle Information Dissemination Mechanisms to Reduce Cognitive Burden in the Information-Rich Driving Environment

Develop In-Vehicle Information Dissemination Mechanisms to Reduce Cognitive Burden in the Information-Rich Driving Environment

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Srinivas Peeta
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Principal Investigator(s):

Srinivas Peeta, Hockema Professor of Civil Engineering – Purdue University

Project Abstract:
In-vehicle information systems (IVIS) are poised to provide drivers with several types of information under the connected and autonomous travel information. Such real-time information can include traffic conditions, weather forecasts, warning/emergency alerts, and infotainment and commercial services. Such an information-rich environment can increase driver cognitive workload in the inherently multitasking driving context, further exacerbated by the multiple sources of information (such as variable message signs, personal devices, GPS, radio, etc.). This can reduce safety and decrease the effectiveness of the disseminated information, especially if the information delivery mechanisms are not well-designed. This study will conduct interactive driving simulator-based experiments to understand the impacts of real-time information characteristics and multiple dissemination sources on driver cognition, and its effects on the driver decision-making process and ability to comprehend information safely. Data will be collected on driver route choice behavior and physiological factors (such as eye movements, brain electrical activity and heart rate) under real-time information provision. These physiological factors will be used to determine cognitive effects (such as cognitive workload, distraction, and level of engagement). The collected data will be used to develop behavior models to investigate the impacts of cognitive effects induced by real-time traffic information, situational factors (such as trip purpose and traffic congestion), real-time travel information characteristics (such as amount, content and source) and individual driver characteristics (such as age, gender and education). These models will be used to design safe and effective information dissemination mechanisms.

Institution(s): Purdue University

Award Year: 2017

Research Thrust(s): Control & Operations, Modeling & Implementation

Project Form(s):
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