932 research outputs found

    A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

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    Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

    Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars

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    This paper presents an adaptive high performance control method for autonomous miniature race cars. Racing dynamics are notoriously hard to model from first principles, which is addressed by means of a cautious nonlinear model predictive control (NMPC) approach that learns to improve its dynamics model from data and safely increases racing performance. The approach makes use of a Gaussian Process (GP) and takes residual model uncertainty into account through a chance constrained formulation. We present a sparse GP approximation with dynamically adjusting inducing inputs, enabling a real-time implementable controller. The formulation is demonstrated in simulations, which show significant improvement with respect to both lap time and constraint satisfaction compared to an NMPC without model learning

    Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control

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    In this paper, we describe an integrated framework for autonomous decision making in a dynamic and interactive environment. We model the interactions between the ego agent and its operating environment as a two-player dynamic game, and integrate cognitive behavioral models, Bayesian inference, and receding-horizon optimal control to define a dynamically-evolving decision strategy for the ego agent. Simulation examples representing autonomous vehicle control in three traffic scenarios where the autonomous ego vehicle interacts with a human-driven vehicle are reported.Comment: 2019 IEEE Conference on Decision and Contro

    Advances in the Hierarchical Emergent Behaviors (HEB) approach to autonomous vehicles

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    Widespread deployment of autonomous vehicles (AVs) presents formidable challenges in terms on handling scalability and complexity, particularly regarding vehicular reaction in the face of unforeseen corner cases. Hierarchical Emergent Behaviors (HEB) is a scalable architecture based on the concepts of emergent behaviors and hierarchical decomposition. It relies on a few simple but powerful rules to govern local vehicular interactions. Rather than requiring prescriptive programming of every possible scenario, HEB’s approach relies on global behaviors induced by the application of these local, well-understood rules. Our first two papers on HEB focused on a primal set of rules applied at the first hierarchical level. On the path to systematize a solid design methodology, this paper proposes additional rules for the second level, studies through simulations the resultant richer set of emergent behaviors, and discusses the communica-tion mechanisms between the different levels.Peer ReviewedPostprint (author's final draft
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