Abstract
To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. The resulting long-term movement predictions demonstrate improved accuracy relative to offline learning alone, in terms of both intent and trajectory prediction. By embedding these predictions within a chance-constrained motion planner, trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware experiments demonstrate that this approach can accurately predict motion patterns from onboard sensor/perception data and facilitate robust navigation within a dynamic environment.
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References
Aoude, G.S., Luders, B.D., Joseph, J.M., Roy, N., How, J.P.: Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns. Auton. Robots 35(1), 51–76 (2013)
Bandyopadhyay, T., Jie, C.Z., Hsu, D., Ang Jr, M.H., Rus, D., Frazzoli, E.: Intention-aware pedestrian avoidance. Experimental Robotics, pp. 963–977. Springer, New York (2013)
Basseville, M., Nikiforov, I.V.: Detection of abrupt changes: theory and applications. J. R. Stat. Soc.-Ser. A Stat. Soc. 158(1), 185 (1995)
Bennewitz, M., Burgard, W., Cielniak, G., Thrun, S.: Learning motion patterns of people for compliant robot motion. Int. J. Robot. Res. 24(1), 31–48 (2005)
Deisenroth, M.P., Huber, M.F., Hanebeck, U.D.: Analytic moment-based Gaussian process filtering. In: Bouttou, L., Littman, M. (eds.) International Conference on Machine Learning (ICML), June 2009, pp. 225–232. Omnipress, Montreal, Canada (2009)
Ellis, D., Sommerlade, E., Reid, I.: Modelling pedestrian trajectory patterns with gaussian processes. In: IEEE International Conference on Computer Vision, pp. 1229–1234 (2009)
Fulgenzi, C., Tay, C., Spalanzani, A., Laugier, C.: Probabilistic navigation in dynamic environment using rapidly-exploring random trees and gaussian processes. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2008, pp. 1056–1062. Nice, France (2008)
Girard, A., Rasmussen, C.E., Quintero-Candela, J., Murray-smith, R.: Gaussian process priors with uncertain inputs—application to multiple-step ahead time series forecasting. In: Advances in Neural Information Processing Systems, pp. 529–536. MIT Press, Cambridge (2003)
Grande, R.C.: Computationally efficient Gaussian process changepoint detection and regression. Master’s thesis, Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Cambridge, MA, June 2014
Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)
How, J.P., Bethke, B., Frank, A., Dale, D., Vian, J.: Real-time indoor autonomous vehicle test environment. IEEE Control Syst. Mag. 28(2), 51–64 (2008)
Ikeda, T., Chigodo, Y., Rea, D., Zanlungo, F., Shiomi, M., Kanda, T.: Modeling and prediction of pedestrian behavior based on the sub-goal concept. In: Robotics: Science and Systems (2012)
Joseph, J., Doshi-Velez, F., Huang, A.S., Roy, N.: A Bayesian nonparametric approach to modeling motion patterns. Auton. Robots 31(4), 383–400 (2011)
Kelley, R., Nicolescu, M., Tavakkoli, A., King, C., Bebis, G.: Understanding human intentions via hidden markov models in autonomous mobile robots. In: ACM/IEEE International Conference on Human-Robot Interaction, pp. 367–374 (2008)
Kuwata, Y., Teo, J., Karaman, S., Fiore, G., Frazzoli, E., How, J.P.: Motion planning in complex environments using closed-loop prediction. In: AIAA Guidance, Navigation, and Control Conference (GNC), August 2008, Honolulu, HI (2008) (AIAA-2008-7166)
Luders, B., Kothari, M., How, J.P.: Chance constrained RRT for probabilistic robustness to environmental uncertainty. In: AIAA Guidance, Navigation, and Control Conference (GNC), August 2010, Toronto, Canada (2010) (AIAA-2010-8160)
Michini, B., Cutler, M., How, J.P.: Scalable reward learning from demonstration. In: IEEE International Conference on Robotics and Automation (ICRA) (2013)
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3 (2009)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. The MIT Press, Cambridge (2005)
Rusu, R.B., Cousins, S.: 3d is here: Point cloud library (PCL). In: IEEE International Conference on Robotics and Automation, pp. 1–4 (2011)
Vasquez, D., Fraichard, T., Laugier, C.: Incremental learning of statistical motion patterns with growing hidden markov models. IEEE Trans. Intell. Transp. Syst. 10(3), 403–416 (2009)
Zhu, Q.: Hidden markov model for dynamic obstacle avoidance of mobile robot navigation. IEEE Trans. Robot. Autom. 7(3), 390–397 (1991)
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Research supported by Ford Motor Company (James McBride, Ford Project Manager) and The Boeing Company.
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Ferguson, S., Luders, B., Grande, R.C., How, J.P. (2015). Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions. In: Akin, H., Amato, N., Isler, V., van der Stappen, A. (eds) Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-16595-0_10
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