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Position Estimation in Urban U-Turn Section for Autonomous Vehicles Using Multiple Vehicle Model and Interacting Multiple Model Filter

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Abstract

A positioning system estimates the position and orientation of a vehicle. Autonomous driving systems plan paths and control the vehicle based on the information from the positioning system. Recently, methods of estimating the position in urban areas have been actively studied. In U-turn sections, which are common in urban areas, vehicles perform a rotation to reverse the direction of travel. Through these sections, drivers can reduce the travel distance and save time but with a high risk of an accident. Despite there being a need for the development of autonomous driving schemes for U-turn sections, the existing research is limited. This study proposes an interacting multiple model (IMM) filter-based position estimation algorithm for urban U-turn sections. To reflect the dynamic characteristics of a vehicle during U-turn maneuvers, a multiple vehicle model was used. This model includes kinematic and dynamic vehicle models. The state estimates of the vehicle model and gyroscope are combined using an IMM filter. The position estimation algorithm developed in this study is verified via experiments. The experimental results indicate that, during urban U-turn maneuvers, the position estimation accuracy of the IMM filter-based algorithm is improved than that of the single vehicle model.

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Abbreviations

v i :

velocity of the vehicle, m/s

\({{\dot v}_i}\) :

acceleration along the axis of the vehicle, m/s2

r i :

rotation radius, m

d i :

diameter of the wheel, m

w i :

angular velocity of the wheel, rad/s

l i :

distance from the center of gravity to the axle, m

β :

sideslip angle of the vehicle, rad

δ :

steering angle of the wheel, rad

γ :

yaw rate, rad/s

γ :

yaw acceleration, rad/s2

F veh,i :

force acting on the vehicle, N

F tire,i :

tire force, N

m :

vehicle mass, kg

α i :

acceleration of the vehicle, m/s2

I z :

yaw moment of inertia, kg m2

X :

position of the vehicle along x coordinate, m

Y :

position of the vehicle along y coordinate, m

φ :

heading angle of the vehicle, rad

T :

sampling time, s

C σ :

longitudinal stiffness of the tire, N

C a :

cornering stiffness of the tire, N/rad

μ friction :

friction coefficient of the tire

F z :

normal force of the tire, N

σ :

slip ratio

α :

slip angle, rad

π ji :

transition matrix

μ i :

model probability

\({{\tilde x}^i}\) :

interacted state estimates

\({{\tilde P}^i}\) :

interacted covariance of state estimates

\({{\hat x}^i}\) :

predicted state estimates

\({{\hat P}^i}\) :

predicted covariance of state estimates

Λi :

likelihood function

References

  • Blom, H. A. P. and Bar-shalom, Y. (1988). The interacting multiple model algorithm for systems with markovian switching coefficients. IEEE Trans. Automatic Control 33, 8, 780–783.

    Article  Google Scholar 

  • Dugoff H., Fancher, P. S. and Segel, L. (1969). Tire performance characteristics affecting vehicle response to steering and braking control inputs. Contract CST-460, Office of Vehicle Systems Research, US National Bureau of Standards.

  • Javanmardi, E., Gu, Y., Javanmardi, M. and Kamijo, S. (2019). Autonomous vehicle self-localization based on abstract map and multi-channel LiDAR in urban area. IATSS Research 43, 1, 1–13.

    Article  Google Scholar 

  • Jo, K., Chu, K. and Sunwoo, M. (2012). Interacting multiple model filter-based sensor fusion of GPS with in-vehicle sensors for real-time vehicle positioning. IEEE Trans. Intelligent Transportation Systems 13, 1, 329–343.

    Article  Google Scholar 

  • Julier, S. J. and Durrant-Whyte, H. F. (2003). On the role of process models in autonomous land vehicle navigation systems. IEEE Trans. Robotics and Automation 19, 1, 1–14.

    Article  Google Scholar 

  • Kuutti, S., Fallah, S., Katsaros, K., Dianati, M., Mccullough, F. and Mouzakitis, A. (2018). A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications. IEEE Internet of Things J. 5, 2, 829–846.

    Article  Google Scholar 

  • Li, X. and Xu, Q. (2017). A reliable fusion positioning strategy for land vehicles in GPS-denied environments based on low-cost sensors. IEEE Trans. Industrial Electronics 64, 4, 3205–3215.

    Article  Google Scholar 

  • Liu, H., Ye, Q., Wang, H., Chen, L. and Yang, J. (2019). A precise and robust segmentation-based LiDAR localization system for automated urban driving. Remote Sensing 11, 11, 1348.

    Article  Google Scholar 

  • Melendez-Pastor, C., Ruiz-Gonzalez, R. and Gomez-Gil, J. (2017). A data fusion system of GNSS data and on-vehicle sensors data for improving car positioning precision in urban environments. Expert Systems with Applications, 80, 28–38.

    Article  Google Scholar 

  • Rajamani, R. (2011). Vehicle Dynamics and Control. 2nd edn. Springer Science & Business Media. New York, NY, USA.

    MATH  Google Scholar 

  • Toledo-Moreo, R., Zamora-Izquierdo, M. A., Ubeda-Minarro, B., and Gomez-Skarmeta, A. F. (2007). High-integrity IMM-EKF-based road vehicle navigation with low-cost GPS/SBAS/INS. IEEE Trans. Intelligent Transportation Systems 8, 3, 491–511.

    Article  Google Scholar 

  • Wang, C., Huang, H., Ji, Y., Wang, B. and Yang, M. (2019). Vehicle localization at an intersection using a traffic light map. IEEE Trans. Intelligent Transportation Systems 20, 4, 1432–1441.

    Article  Google Scholar 

  • Wang, L. and Li, S. (2018). Enhanced multi-sensor data fusion methodology based on multiple model estimation for integrated navigation system. Int. J. Control Automation and Systems 16, 1, 295–305.

    Article  Google Scholar 

  • Xu, Q., Li, X. and Chan, C. Y. (2017). A cost-effective vehicle localization solution using an interacting multiple model unscented kalman filters (IMM-UKF) algorithm and grey neural network. Sensors 17, 6, 1431.

    Article  Google Scholar 

  • Xu, Q., Li, X. and Chan, C. Y. (2018). Enhancing localization accuracy of MEMS-INS/GPS/In-Vehicle sensors integration during GPS outages. IEEE Trans. Instrumentation and Measurement 67, 8, 1966–1978.

    Article  Google Scholar 

  • Zhang, Y. (2019). A fusion methodology to bridge GPS outages for INS/GPS integrated navigation system. IEEE Access, 7, 61296–61306.

    Article  Google Scholar 

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Acknowledgement

This research was supported by a grant (20AUDP-B121595-05) from Architecture & Urban Development Research Program funded by the Ministry of Land, Infrastructure and Transport of the Korean Government.

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Authors and Affiliations

  1. Department of Automotive Convergence, Graduate of School, Korea University, Seoul, 02841, Korea

    Suyoung Choi

  2. Department of Mechanical Engineering, Korea University, Seoul, 02841, Korea

    Daehie Hong

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  1. Suyoung Choi
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  2. Daehie Hong
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Correspondence to Daehie Hong.

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Choi, S., Hong, D. Position Estimation in Urban U-Turn Section for Autonomous Vehicles Using Multiple Vehicle Model and Interacting Multiple Model Filter. Int.J Automot. Technol. 22, 1599–1607 (2021). https://doi.org/10.1007/s12239-021-0138-8

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  • DOI: https://doi.org/10.1007/s12239-021-0138-8

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