Driver Behavior Assessment in Case of Critical Driving Situations

Oussama DERBEL; René LANDRY, Jr.

doi:10.1587/transfun.E100.A.491

Driver Behavior Assessment in Case of Critical Driving Situations

Oussama DERBEL, René LANDRY, Jr.

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Driver behavior assessment is a hard task since it involves distinctive interconnected factors of different types. Especially in case of insurance applications, a trade-off between application cost and data accuracy remains a challenge. Data uncertainty and noises make smart-phone or low-cost sensor platforms unreliable. In order to deal with such problems, this paper proposes the combination between the Belief and Fuzzy theories with a two-level fusion based architecture. It enables the propagation of information errors from the lower to the higher level of fusion using the belief and/or the plausibility functions at the decision step. The new developed risk models of the Driver and Environment are based on the accident statistics analysis regarding each significant driving risk parameter. The developed Vehicle risk models are based on the longitudinal and lateral accelerations (G-G diagram) and the velocity to qualify the driving behavior in case of critical events (e.g. Zig-Zag scenario). In case of over-speed and/or accident scenario, the risk is evaluated using our new developed Fuzzy Inference System model based on the Equivalent Energy Speed (EES). The proposed approach and risk models are illustrated by two examples of driving scenarios using the CarSim vehicle simulator. Results have shown the validity of the developed risk models and the coherence with the a-priori risk assessment.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.2 pp.491-498

Publication Date: 2017/02/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E100.A.491

Type of Manuscript: Special Section PAPER (Special Section on Intelligent Transport Systems)

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Authors

Oussama DERBEL
University of Quebec
René LANDRY, Jr.
University of Quebec

Keyword

fuzzy theory, risk assessment, G-G diagram, belief theory, Pay How You Drive, Pay Where You Drive

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Oussama DERBEL, René LANDRY, Jr., "Driver Behavior Assessment in Case of Critical Driving Situations" in IEICE TRANSACTIONS on Fundamentals, vol. E100-A, no. 2, pp. 491-498, February 2017, doi: 10.1587/transfun.E100.A.491.
Abstract: Driver behavior assessment is a hard task since it involves distinctive interconnected factors of different types. Especially in case of insurance applications, a trade-off between application cost and data accuracy remains a challenge. Data uncertainty and noises make smart-phone or low-cost sensor platforms unreliable. In order to deal with such problems, this paper proposes the combination between the Belief and Fuzzy theories with a two-level fusion based architecture. It enables the propagation of information errors from the lower to the higher level of fusion using the belief and/or the plausibility functions at the decision step. The new developed risk models of the Driver and Environment are based on the accident statistics analysis regarding each significant driving risk parameter. The developed Vehicle risk models are based on the longitudinal and lateral accelerations (G-G diagram) and the velocity to qualify the driving behavior in case of critical events (e.g. Zig-Zag scenario). In case of over-speed and/or accident scenario, the risk is evaluated using our new developed Fuzzy Inference System model based on the Equivalent Energy Speed (EES). The proposed approach and risk models are illustrated by two examples of driving scenarios using the CarSim vehicle simulator. Results have shown the validity of the developed risk models and the coherence with the a-priori risk assessment.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.491/_p

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@ARTICLE{e100-a_2_491,
author={Oussama DERBEL, René LANDRY, Jr., },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Driver Behavior Assessment in Case of Critical Driving Situations},
year={2017},
volume={E100-A},
number={2},
pages={491-498},
abstract={Driver behavior assessment is a hard task since it involves distinctive interconnected factors of different types. Especially in case of insurance applications, a trade-off between application cost and data accuracy remains a challenge. Data uncertainty and noises make smart-phone or low-cost sensor platforms unreliable. In order to deal with such problems, this paper proposes the combination between the Belief and Fuzzy theories with a two-level fusion based architecture. It enables the propagation of information errors from the lower to the higher level of fusion using the belief and/or the plausibility functions at the decision step. The new developed risk models of the Driver and Environment are based on the accident statistics analysis regarding each significant driving risk parameter. The developed Vehicle risk models are based on the longitudinal and lateral accelerations (G-G diagram) and the velocity to qualify the driving behavior in case of critical events (e.g. Zig-Zag scenario). In case of over-speed and/or accident scenario, the risk is evaluated using our new developed Fuzzy Inference System model based on the Equivalent Energy Speed (EES). The proposed approach and risk models are illustrated by two examples of driving scenarios using the CarSim vehicle simulator. Results have shown the validity of the developed risk models and the coherence with the a-priori risk assessment.},
keywords={},
doi={10.1587/transfun.E100.A.491},
ISSN={1745-1337},
month={February},}

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TY - JOUR
TI - Driver Behavior Assessment in Case of Critical Driving Situations
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 491
EP - 498
AU - Oussama DERBEL
AU - René LANDRY
AU - Jr.
PY - 2017
DO - 10.1587/transfun.E100.A.491
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2017
AB - Driver behavior assessment is a hard task since it involves distinctive interconnected factors of different types. Especially in case of insurance applications, a trade-off between application cost and data accuracy remains a challenge. Data uncertainty and noises make smart-phone or low-cost sensor platforms unreliable. In order to deal with such problems, this paper proposes the combination between the Belief and Fuzzy theories with a two-level fusion based architecture. It enables the propagation of information errors from the lower to the higher level of fusion using the belief and/or the plausibility functions at the decision step. The new developed risk models of the Driver and Environment are based on the accident statistics analysis regarding each significant driving risk parameter. The developed Vehicle risk models are based on the longitudinal and lateral accelerations (G-G diagram) and the velocity to qualify the driving behavior in case of critical events (e.g. Zig-Zag scenario). In case of over-speed and/or accident scenario, the risk is evaluated using our new developed Fuzzy Inference System model based on the Equivalent Energy Speed (EES). The proposed approach and risk models are illustrated by two examples of driving scenarios using the CarSim vehicle simulator. Results have shown the validity of the developed risk models and the coherence with the a-priori risk assessment.
ER -