TY - GEN
T1 - Quantitative driving safety assessment using interaction design benchmarking
AU - Gaffar, Ashraf
AU - Kouchak, Shokoufeh Monjezi
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/6/26
Y1 - 2018/6/26
N2 - Driver distraction is an important and challenging problem with significant cost on human lives. It has been researched for many years and the research has intensified in the last decade. The focus has been on identifying the distractive tasks and measuring the respective harm level. As in-vehicle technology advances with digital infotainment systems and web connectivity, crash risk grows along with the growing list of distractive activities. Additionally, these distractive activities become more common and more complicated, especially regarding recent In-Car Interactive System, ICIS, which is the main focus of this work. Most of the car cockpit functions rely heavily on interaction with the driver, and several studies suggest that driver distraction level leading to serious injuries or fatalities is still high and that there is an urgent need for a better interaction design. Multimodal Interaction Design can be used to provide a rich interaction environment inside the car cockpit. Following NHTSA guidelines, our experiment identifies design benchmarks that can lead to reducing driver distraction in a Multi-modal Interaction Design framework. The main goal of our MMI approach is to enable the driver to be more attentive to driving tasks while spending less time fiddling with distractive tasks inside the cockpit. In this experiment, an engineering based method is used to measure driver distraction using advanced driving simulator. This method uses metrics like Reaction Time, Acceleration, and Lane Departure data obtained from our test cases.
AB - Driver distraction is an important and challenging problem with significant cost on human lives. It has been researched for many years and the research has intensified in the last decade. The focus has been on identifying the distractive tasks and measuring the respective harm level. As in-vehicle technology advances with digital infotainment systems and web connectivity, crash risk grows along with the growing list of distractive activities. Additionally, these distractive activities become more common and more complicated, especially regarding recent In-Car Interactive System, ICIS, which is the main focus of this work. Most of the car cockpit functions rely heavily on interaction with the driver, and several studies suggest that driver distraction level leading to serious injuries or fatalities is still high and that there is an urgent need for a better interaction design. Multimodal Interaction Design can be used to provide a rich interaction environment inside the car cockpit. Following NHTSA guidelines, our experiment identifies design benchmarks that can lead to reducing driver distraction in a Multi-modal Interaction Design framework. The main goal of our MMI approach is to enable the driver to be more attentive to driving tasks while spending less time fiddling with distractive tasks inside the cockpit. In this experiment, an engineering based method is used to measure driver distraction using advanced driving simulator. This method uses metrics like Reaction Time, Acceleration, and Lane Departure data obtained from our test cases.
KW - Driver Distraction
KW - Human-Car Interaction
KW - Human-Computer Interaction
KW - Infotainment Design
KW - Multimodal Interaction
KW - Speech Recognition
UR - http://www.scopus.com/inward/record.url?scp=85050251045&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050251045&partnerID=8YFLogxK
U2 - 10.1109/UIC-ATC.2017.8397626
DO - 10.1109/UIC-ATC.2017.8397626
M3 - Conference contribution
AN - SCOPUS:85050251045
T3 - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
SP - 1
EP - 8
BT - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017
Y2 - 4 April 2017 through 8 April 2017
ER -