TY - GEN
T1 - Monitoring driver's cognitive status based on integration of internal and external information
AU - Kim, Seonggyu
AU - Rammohan, Mallipeddi
AU - Lee, Minho
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/10/21
Y1 - 2015/10/21
N2 - In Advanced Driving Assistance Systems (ADASs), monitoring the driver's cognitive status during driving is considered as an important issue. Because, most of the accidents in the automotive sector occur due to the driver's misinterpretation or lack of sufficient information regarding the situation. In order to prevent these accidents, current ADASs include lane departure warning systems, vehicle detection systems, advanced cruise control systems, etc. In a particular driving scenario, the amount of information available to the driver regarding a situation can be judged by monitoring the driver's gaze (internal information) and distributions corresponding to the forward traffic (external information). Therefore, to provide sufficient information to the driver regarding a driving scenario it is essential to integrate the internal and external information which is lacking in the current ADASs. In this paper, we use 3D pose estimate algorithm (POSIT) to estimate driver's attention area. In order to estimate the distributions corresponding to the forward traffic we employ Bottom-up Saliency map. To integrate the internal and external information we use conditional mutual information.
AB - In Advanced Driving Assistance Systems (ADASs), monitoring the driver's cognitive status during driving is considered as an important issue. Because, most of the accidents in the automotive sector occur due to the driver's misinterpretation or lack of sufficient information regarding the situation. In order to prevent these accidents, current ADASs include lane departure warning systems, vehicle detection systems, advanced cruise control systems, etc. In a particular driving scenario, the amount of information available to the driver regarding a situation can be judged by monitoring the driver's gaze (internal information) and distributions corresponding to the forward traffic (external information). Therefore, to provide sufficient information to the driver regarding a driving scenario it is essential to integrate the internal and external information which is lacking in the current ADASs. In this paper, we use 3D pose estimate algorithm (POSIT) to estimate driver's attention area. In order to estimate the distributions corresponding to the forward traffic we employ Bottom-up Saliency map. To integrate the internal and external information we use conditional mutual information.
KW - ADAS system
KW - Gestalt saliency map
KW - Head pose estimate
KW - Mutual information
UR - https://www.scopus.com/pages/publications/84962915327
U2 - 10.1145/2814940.2814999
DO - 10.1145/2814940.2814999
M3 - Conference contribution
AN - SCOPUS:84962915327
T3 - HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction
SP - 287
EP - 290
BT - HAI 2015 - Proceedings of the 3rd International Conference on Human-Agent Interaction
PB - Association for Computing Machinery, Inc
T2 - 3rd International Conference on Human-Agent Interaction, HAI 2015
Y2 - 21 October 2015 through 24 October 2015
ER -