TY - GEN
T1 - Evaluation of a semi-Autonomous lane departure correction system using naturalistic driving data
AU - Zhao, Ding
AU - Wang, Wenshuo
AU - LeBlanc, David J.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - Evaluating the effectiveness and benefits of driver assistance systems is essential for improving the system performance. In this paper, we propose an efficient evaluation method for a semi-Autonomous lane departure correction system. To achieve this, we apply a bounded Gaussian mixture model to describe drivers stochastic lane departure behavior learned from naturalistic driving data, which can regenerate departure behaviors to evaluate the lane departure correction system. In the stochastic lane departure model, we conduct a dimension reduction to reduce the computation cost. Finally, to show the advantages of our proposed evaluation approach, we compare steering systems with and without lane departure assistance based on the stochastic lane departure model. The simulation results show that the proposed method can effectively evaluate the lane departure correction system.
AB - Evaluating the effectiveness and benefits of driver assistance systems is essential for improving the system performance. In this paper, we propose an efficient evaluation method for a semi-Autonomous lane departure correction system. To achieve this, we apply a bounded Gaussian mixture model to describe drivers stochastic lane departure behavior learned from naturalistic driving data, which can regenerate departure behaviors to evaluate the lane departure correction system. In the stochastic lane departure model, we conduct a dimension reduction to reduce the computation cost. Finally, to show the advantages of our proposed evaluation approach, we compare steering systems with and without lane departure assistance based on the stochastic lane departure model. The simulation results show that the proposed method can effectively evaluate the lane departure correction system.
KW - Performance evaluation
KW - bounded Gaussian mixture model
KW - lane departure correction system
KW - stochastic driver model
UR - http://www.scopus.com/inward/record.url?scp=85028051806&partnerID=8YFLogxK
U2 - 10.1109/IVS.2017.7995834
DO - 10.1109/IVS.2017.7995834
M3 - Conference contribution
AN - SCOPUS:85028051806
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 926
EP - 932
BT - IV 2017 - 28th IEEE Intelligent Vehicles Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th IEEE Intelligent Vehicles Symposium, IV 2017
Y2 - 11 June 2017 through 14 June 2017
ER -