TY - GEN
T1 - Modeling driver lane changing control with the queuing network-model human processor
AU - Bi, Lu Zheng
AU - Shang, Jun Xing
AU - Gan, Guo Dong
PY - 2012
Y1 - 2012
N2 - Computational models of driving behavior developed in a cognitive architecture can provide better scientific understanding of driving, simulate driving behavior, quantitatively predict possible interference of in-vehicle tasks, and thus help develop human factors guidelines and tools for in-vehicle systems design. Driver lane changing is a common activity in driving. Therefore, modeling driver lane changing control with a cognitive architecture should be an important component of cognitive models of driving behavior. In this paper, we develop a computational model of driver lane changing control with the Queuing Network-Model Human Processor (QN-MHP) cognitive architecture based on neuroscience and psychological findings. The simulation and experimental results from lane changing on straight and curved roads show that this model can perform the control process of lane changing well and the model's control process is consistent with that of drivers.
AB - Computational models of driving behavior developed in a cognitive architecture can provide better scientific understanding of driving, simulate driving behavior, quantitatively predict possible interference of in-vehicle tasks, and thus help develop human factors guidelines and tools for in-vehicle systems design. Driver lane changing is a common activity in driving. Therefore, modeling driver lane changing control with a cognitive architecture should be an important component of cognitive models of driving behavior. In this paper, we develop a computational model of driver lane changing control with the Queuing Network-Model Human Processor (QN-MHP) cognitive architecture based on neuroscience and psychological findings. The simulation and experimental results from lane changing on straight and curved roads show that this model can perform the control process of lane changing well and the model's control process is consistent with that of drivers.
KW - Cognitive architecture
KW - Computational model
KW - Driver lane changing control
KW - QN-MHP
UR - http://www.scopus.com/inward/record.url?scp=84871551644&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2012.6359460
DO - 10.1109/ICMLC.2012.6359460
M3 - Conference contribution
AN - SCOPUS:84871551644
SN - 9781467314855
T3 - Proceedings - International Conference on Machine Learning and Cybernetics
SP - 830
EP - 834
BT - Proceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
T2 - 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Y2 - 15 July 2012 through 17 July 2012
ER -
