A Belief State Planner for Interactive Merge Maneuvers in Congested Traffic

Autonomous driving in urban environments requires the capability of merging into narrow gaps. In cases of high traffic density this becomes more complex since one must consider the interaction with other vehicles. We formulate the problem as a Partially Observable Markov Decision Process (POMDP) by including the surrounding drivers in the state space to realize interactive behavior. The problem is solved online by an anytime Monte Carlo sampling algorithm in combination with an efficient A* rollout heuristic. This combination makes a combined lateral and longitudinal optimization possible. The resulting policy is optimized regarding various future merge scenarios and approaches the most suitable gap while taking into account the uncertain behavior of the surrounding drivers. Therefore, we present a novel motion model representing the uncertain cooperation of other drivers. It is based on a logistic regression model estimating the probability for cooperative behavior of a human driver given a future scene. We demonstrate the performance of our algorithm by various simulated scenarios. The resulting behavior for approaching gaps, estimating the future cooperative behavior of surrounding drivers and performing merges in narrow gaps are discussed.

[1]  Matthias Althoff,et al.  Verifying the safety of lane change maneuvers of self-driving vehicles based on formalized traffic rules , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[2]  Alois Knoll,et al.  Tactical cooperative planning for autonomous highway driving using Monte-Carlo Tree Search , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[3]  Andreas Krause,et al.  Unfreezing the robot: Navigation in dense, interacting crowds , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Wei Zhan,et al.  Spatially-partitioned environmental representation and planning architecture for on-road autonomous driving , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[5]  S. Kammel,et al.  Cooperative Cognitive Automobiles , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[6]  Julius Ziegler,et al.  Optimal trajectory generation for dynamic street scenarios in a Frenét Frame , 2010, 2010 IEEE International Conference on Robotics and Automation.

[7]  Christoph Stiller,et al.  Automated Driving in Uncertain Environments: Planning With Interaction and Uncertain Maneuver Prediction , 2018, IEEE Transactions on Intelligent Vehicles.

[8]  John M. Dolan,et al.  Interactive ramp merging planning in autonomous driving: Multi-merging leading PGM (MML-PGM) , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[9]  Markus Maurer,et al.  Probabilistic online POMDP decision making for lane changes in fully automated driving , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[10]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[11]  Christoph Stiller,et al.  A generic driving strategy for urban environments , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[12]  S. Zucker,et al.  Toward Efficient Trajectory Planning: The Path-Velocity Decomposition , 1986 .

[13]  Hanna Kurniawati,et al.  An Online POMDP Solver for Uncertainty Planning in Dynamic Environment , 2013, ISRR.

[14]  Nico Kaempchen,et al.  Highly Automated Driving on Freeways in Real Traffic Using a Probabilistic Framework , 2012, IEEE Transactions on Intelligent Transportation Systems.

[15]  Edwin Olson,et al.  MPDM: Multipolicy decision-making in dynamic, uncertain environments for autonomous driving , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Jonas Fredriksson,et al.  Longitudinal and Lateral Control for Automated Yielding Maneuvers , 2016, IEEE Transactions on Intelligent Transportation Systems.

[17]  David Hsu,et al.  HyP-DESPOT: A hybrid parallel algorithm for online planning under uncertainty , 2018, Robotics: Science and Systems.

[18]  Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .

[19]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[20]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.