A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 10 Issue 2
Feb.  2023

IEEE/CAA Journal of Automatica Sinica

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Y. Rahman, A. Sharma, M. Jankovic, M. Santillo, and M. Hafner, “Driver intent prediction and collision avoidance with barrier functions,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 365–375, Feb. 2023. doi: 10.1109/JAS.2023.123210
Citation: Y. Rahman, A. Sharma, M. Jankovic, M. Santillo, and M. Hafner, “Driver intent prediction and collision avoidance with barrier functions,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 2, pp. 365–375, Feb. 2023. doi: 10.1109/JAS.2023.123210

Driver Intent Prediction and Collision Avoidance With Barrier Functions

doi: 10.1109/JAS.2023.123210
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  • For autonomous vehicles and driver assist systems, path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers. In the literature, the algorithms that provide driver intent belong to two categories: those that use physics based models with some type of filtering, and machine learning based approaches. In this paper we employ barrier functions (BF) to decide driver intent. BFs are typically used to prove safety by establishing forward invariance of an admissible set. Here, we decide if the “target” vehicle is violating one or more possibly fictitious (i.e., non-physical) barrier constraints determined based on the context provided by the road geometry. The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives. The predicted intent is then used by a control barrier function (CBF) based collision avoidance system to prevent unnecessary interventions, for either an autonomous or human-driven vehicle.

     

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  • [1]
    H. A. P. Blom, “An efficient filter for abruptly changing systems,” in Proc. 23rd IEEE Conf. Decision and Control, Las Vegas, USA, 1984, pp. 656–658.
    [2]
    L. A. Johnston and V. Krishnamurthy, “An improvement to the interacting multiple model (IMM) algorithm,” IEEE Trans. Signal Process., vol. 49, no. 12, pp. 2909–2923, Dec. 2001. doi: 10.1109/78.969500
    [3]
    R. R. Pitre, V. P. Jilkov, and X. R. Li, “A comparative study of multiple-model algorithms for maneuvering target tracking,” in Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, Orlando, USA, 2005, pp. 549–560.
    [4]
    C. Manasseh and R. Sengupta, “Predicting driver destination using machine learning techniques,” in Proc. 16th Int. IEEE Conf. Intelligent Transportation Systems, The Hague, Netherlands, 2013, pp. 142–147.
    [5]
    Y. Hou, P. Edara, and C. Sun, “Modeling mandatory lane changing using Bayes classifier and decision trees,” IEEE Trans. Intell. Transp. Syst., vol. 15, no. 2, pp. 647–655, Apr. 2014. doi: 10.1109/TITS.2013.2285337
    [6]
    F. Altchá and A. De La Fortelle, “An LSTM network for highway trajectory prediction,” in Proc. IEEE 20th Int. Conf. Intelligent Transportation Systems, Yokohama, Japan, 2017, pp. 353–359.
    [7]
    A. Zyner, S. Worrall, J. Ward, and E. Nebot, “Long short term memory for driver intent prediction,” in Proc. IEEE Intelligent Vehicles Symp., Los Angeles, USA, 2017, pp. 1484–1489.
    [8]
    T. Streubel and K. H. Hoffmann, “Prediction of driver intended path at intersections,” in Proc. IEEE Intelligent Vehicles Symp. Proc., Dearborn, USA, 2014, pp. 134–139.
    [9]
    X. H. Li, W. S. Wang, and M. Roetting, “Estimating driver’s lane-change intent considering driving style and contextual traffic,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 9, pp. 3258–3271, Sep. 2019. doi: 10.1109/TITS.2018.2873595
    [10]
    S. Prajna, A. Jadbabaie, and G. J. Pappas, “A framework for worst-case and stochastic safety verification using barrier certificates,” IEEE Trans. Autom. Control, vol. 52, no. 8, pp. 1415–1428, Aug. 2007. doi: 10.1109/TAC.2007.902736
    [11]
    Y. Rahman, M. Jankovic, and M. Santillo, “Driver intent prediction with barrier functions,” in Proc. American Control Conf., New Orleans, USA, 2021, pp. 224–230.
    [12]
    P. Wieland and F. Allgöwer, “Constructive safety using control barrier functions,” IFAC Proc. Vol., vol. 40, no. 12, pp. 462–467, 2007. doi: 10.3182/20070822-3-ZA-2920.00076
    [13]
    A. D. Ames, X. R. Xu, J. W. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs for safety critical systems,” IEEE Trans. Autom. Control, vol. 62, no. 8, pp. 3861–3876, Aug. 2017. doi: 10.1109/TAC.2016.2638961
    [14]
    M. Jankovic, “Robust control barrier functions for constrained stabilization of nonlinear systems,” Automatica, vol. 96, pp. 359–367, Oct. 2018. doi: 10.1016/j.automatica.2018.07.004
    [15]
    W. Xiao and C. Belta, “Control barrier functions for systems with high relative degree,” in Proc. IEEE 58th Conf. Decision and Control, Nice, France, 2019, pp. 474–479.
    [16]
    Q. Nguyen and K. Sreenath, “Exponential control barrier functions for enforcing high relative-degree safety-critical constraints,” in Proc. American Control Conf., Boston, USA, 2016, pp. 322–328.
    [17]
    R. Rajamani, Vehicle Dynamics and Control. Springer Science & Business Media, 2011.
    [18]
    I. Gat, M. Benady, and A. Shashua, “A monocular vision advance warning system for the automotive aftermarket,” SAE Trans., vol. 114, pp. 403–410, 2005.
    [19]
    M. R. Hafner, K. S. Zhao, A. Hsia, and Z. Rachlin, “Localization tools for benchmarking ADAS control systems,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, Budapest, Hungary, 2016, pp. 002665–002670.
    [20]
    C. Su, W. W. Deng, H. Sun, J. Wu, B. H. Sun, and S. Yang, “Forward collision avoidance systems considering driver’s driving behavior recognized by Gaussian mixture model,” in Proc. IEEE Intelligent Vehicles Symp., Los Angeles, USA, 2017, pp. 535–540.
    [21]
    W. S. Wang, J. Q. Xi, and J. K. Hedrick, “A learning-based personalized driver model using bounded generalized Gaussian mixture models,” IEEE Trans. Veh. Technol., vol. 68, no. 12, pp. 11679–11690, Dec. 2019. doi: 10.1109/TVT.2019.2948911
    [22]
    J. Ziegler, P. Bender, T. Dang, and C. Stiller, “Trajectory planning for bertha—a local, continuous method,” in Proc. IEEE Intelligent Vehicles Symp. Proc., Dearborn, USA, 2014, pp. 450–457.
    [23]
    C. K. Verginis and D. V. Dimarogonas, “Closed-form barrier functions for multi-agent ellipsoidal systems with uncertain lagrangian dynamics,” IEEE Control Syst. Lett., vol. 3, no. 3, pp. 727–732, Jul. 2019. doi: 10.1109/LCSYS.2019.2917822
    [24]
    M. Brannström, E. Coelingh, and J. Sjöberg, “Model-based threat assessment for avoiding arbitrary vehicle collisions,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 3, pp. 658–669, Sep. 2010. doi: 10.1109/TITS.2010.2048314
    [25]
    G. M. Hoffmann, C. J. Tomlin, M. Montemerlo, and S. Thrun, “Autonomous automobile trajectory tracking for off-road driving: Controller design, experimental validation and racing,” in Proc. American Control Conf., New York, USA, 2007, pp. 2296–2301.

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    Highlights

    • Driver Intent Prediction
    • Control Barrier Functions
    • Collision Avoidance Systems for Autonomous Vehicles

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