Evaluation of a semi-Autonomous lane departure correction system using naturalistic driving data

Ding Zhao; Wenshuo Wang; David J. LeBlanc

doi:10.1109/IVS.2017.7995834

Evaluation of a semi-Autonomous lane departure correction system using naturalistic driving data

Ding Zhao^*, Wenshuo Wang, David J. LeBlanc

^*Corresponding author for this work

School of Mechanical Engineering

University of Michigan, Ann Arbor

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	IV 2017 - 28th IEEE Intelligent Vehicles Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	926-932
Number of pages	7
ISBN (Electronic)	9781509048045
DOIs	https://doi.org/10.1109/IVS.2017.7995834
Publication status	Published - 28 Jul 2017
Event	28th IEEE Intelligent Vehicles Symposium, IV 2017 - Redondo Beach, United States Duration: 11 Jun 2017 → 14 Jun 2017

Publication series

Name	IEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference	28th IEEE Intelligent Vehicles Symposium, IV 2017
Country/Territory	United States
City	Redondo Beach
Period	11/06/17 → 14/06/17

Keywords

Performance evaluation
bounded Gaussian mixture model
lane departure correction system
stochastic driver model

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10.1109/IVS.2017.7995834

Cite this

Zhao, D., Wang, W., & LeBlanc, D. J. (2017). Evaluation of a semi-Autonomous lane departure correction system using naturalistic driving data. In IV 2017 - 28th IEEE Intelligent Vehicles Symposium (pp. 926-932). Article 7995834 (IEEE Intelligent Vehicles Symposium, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2017.7995834

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title = "Evaluation of a semi-Autonomous lane departure correction system using naturalistic driving data",

abstract = "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.",

keywords = "Performance evaluation, bounded Gaussian mixture model, lane departure correction system, stochastic driver model",

author = "Ding Zhao and Wenshuo Wang and LeBlanc, {David J.}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 28th IEEE Intelligent Vehicles Symposium, IV 2017 ; Conference date: 11-06-2017 Through 14-06-2017",

year = "2017",

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doi = "10.1109/IVS.2017.7995834",

language = "English",

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Zhao, D, Wang, W & LeBlanc, DJ 2017, Evaluation of a semi-Autonomous lane departure correction system using naturalistic driving data. in IV 2017 - 28th IEEE Intelligent Vehicles Symposium., 7995834, IEEE Intelligent Vehicles Symposium, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 926-932, 28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11/06/17. https://doi.org/10.1109/IVS.2017.7995834

Evaluation of a semi-Autonomous lane departure correction system using naturalistic driving data. / Zhao, Ding; Wang, Wenshuo; LeBlanc, David J.
IV 2017 - 28th IEEE Intelligent Vehicles Symposium. Institute of Electrical and Electronics Engineers Inc., 2017. p. 926-932 7995834 (IEEE Intelligent Vehicles Symposium, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - LeBlanc, David J.

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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.

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Evaluation of a semi-Autonomous lane departure correction system using naturalistic driving data

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