Decision Making for Parallel Autonomy in Clutter: Addressing Intent, Interactions, Rules of the Road, and Safety

Decision Making for Parallel Autonomy in Clutter
Daniela Rus, Sertac Karaman

Motion planning is critical for future driver assistance systems and autonomous cars. Algorithms that are able to design high-performance and provably safe motion through cluttered, dynamics environments are required for cars to operate in complex urban environments. In this research effort, we will develop theories to understand motion through cluttered, dynamic environments; we will also develop algorithms that realize natural, safe motion through cluttered environments, with provable completeness and optimality guarantees. We envision these algorithms to:
      (i) understand the density of the “clutter,” for instance, as the number of other vehicles or pedestrians in the vicinity of the car
      (ii) devise a speed with which the car/robot navigate safely (indoors and outdoors), finding natural and safe paths as the clutter around the vehicle evolves
      (iii) design provably-safe trajectories that get the vehicle through the clutter in a natural manner, while guaranteeing safety
      (iv) develop collision avoidance and trajectory planning solution for high speeds in dense environments
      (v) trajectory planning with rules of the road and safety guarantees.

This is a continuation of the project "Decision Making for Parallel Autonomy in Clutter" by Daniela Rus and Sertac Karaman.

Publications:

  • W. Schwarting, A. Pierson, J. Alonso-Mora, S. Karaman, and D. Rus, “Social behavior for autonomous vehicles,” PNAS (accepted), Oct. 2019.

  • N. Buckman, A. Pierson, W. Schwarting, S. Karaman, and D. Rus, “Sharing is Caring: Socially-Compliant Autonomous Intersection Negotiation,” in IROS 2019 (Accepted), 2019.

  • S. G. McGill, G. Rosman, T. Ort, A. Pierson, I. Gilitschenski, B. Araki, L. Fletcher, S. Karaman, D. Rus, and J. J. Leonard, “Probabilistic Risk Metrics for Navigating Occluded Intersections,” IEEE Robot. Autom. Lett., vol. 4, no. 4, pp. 4322–4329, Oct. 2019 [Online]. Available: https://ieeexplore.ieee.org/document/8779655/
  • A. Pierson, W. Schwarting, S. Karaman, and D. Rus, “Learning Risk Level Set Parameters from Data Sets for Safer Driving,” in 2019 IEEE Intelligent Vehicles Symposium (IV), 2019 [Online]. Available: https://its.papercept.net/conferences/conferences/IV2019/program/IV2019_ContentListWeb_2.html

  • A. Pierson, C. I. Vasile, A. Gandhi, W. Schwarting, S. Karaman, and D. Rus, “Dynamic Risk Density for Autonomous Navigation in Cluttered Environments without Object Detection,” 2019 [Online]. Available: https://doi.org/10.1109/ICRA.2019.8793813

  • J. DeCastro, L. Liebenwein, C.-I. Vasile, R. Tedrake, S. Karaman, and D. Rus, “Counterexample-Guided Safety Contracts for Autonomous Driving,” in The 13th International Workshop on the Algorithmic Foundations of Robotics (WAFR 2018), 2018 [Online]. Available: https://parasol.tamu.edu/wafr/wafr2018/ppr_files/WAFR_2018_paper_57.pdf

  • A. Pierson, W. Schwarting, S. Karaman, and D. Rus, “Navigating Congested Environments with Risk Level Sets,” in ICRA 2018, 2018 [Online]. Available: https://doi.org/10.1109/ICRA.2018.8460697

  • J. Karlsson, C.-I. Vasile, J. Tumova, S. Karaman, and D. Rus, “Multi-vehicle motion planning for social optimal mobility-on-demand,” in ICRA 2018, 2018 [Online]. Available: https://doi.org/10.1109/ICRA.2018.8462968

  • L. Liebenwein, W. Schwarting, C.-I. Vasile, J. DeCastro, J. Alonso-Mora, S. Karaman, and D. Rus, “Compositional and Contract-based Verification for Autonomous Driving on Road Networks,” in International Symposium on Robotics Research 2017, Puerto Varas, Chile, 2017 [Online]. Available: https://parasol.tamu.edu/isrr/isrr2017/program.php

  • C.-I. Vasile, J. Tumova, S. Karaman, C. Belta, and D. Rus, “Minimum-violation scLTL motion planning for mobility-on-demand,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, Singapore, 2017 [Online]. Available: https://doi.org/10.1109/ICRA.2017.7989177

  • L. Paull, J. Tani, H. Ahn, J. Alonso-Mora, L. Carlone, M. Cáp, Y. F. Chen, C. Choi, J. Dusek, Y. Fang, D. Hoehener, S.-Y. Liu, M. Novitzky, I. F. Okuyama, J. Pazis, G. Rosman, V. Varricchio, H.-C. Wang, D. S. Yershov, H. Zhao, M. Benjamin, C. Carr, M. T. Zuber, S. Karaman, E. Frazzoli, D. D. Vecchio, D. Rus, J. P. How, J. J. Leonard, and A. Censi, “Duckietown: An open, inexpensive and flexible platform for autonomy education and research,” in 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, Singapore, Singapore, 2017, pp. 1497–1504 [Online]. Available: https://doi.org/10.1109/ICRA.2017.7989179

  • F. Naser, D. Dorhout, S. Proulx, S. D. Pendleton, H. Andersen, W. Schwarting, L. Paull, J. Alonso-Mora, M. H. A. Jr., S. Karaman, R. Tedrake, J. Leonard, and D. Rus, “A Parallel Autonomy Research Platform,” in 2017 IEEE Intelligent Vehicles Symposium, 2017, pp. 933–940 [Online]. Available: https://doi.org/10.1109/IVS.2017.7995835

  • A. Pierson and D. Rus, “Distributed Target Tracking in Cluttered Environments with Guaranteed Collision Avoidance,” in The 1st International Symposium on Multi-Robot and Multi-Agent Systems, Los Angeles, CA, USA, 2017 [Online]. Available: https://doi.org/10.1109/MRS.2017.8250935

 

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