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Active Learning with Dual Model Predictive Path-Integral Control for Interaction-Aware Autonomous Highway On-ramp Merging (2310.07840v1)

Published 11 Oct 2023 in cs.RO and math.OC

Abstract: Merging into dense highway traffic for an autonomous vehicle is a complex decision-making task, wherein the vehicle must identify a potential gap and coordinate with surrounding human drivers, each of whom may exhibit diverse driving behaviors. Many existing methods consider other drivers to be dynamic obstacles and, as a result, are incapable of capturing the full intent of the human drivers via this passive planning. In this paper, we propose a novel dual control framework based on Model Predictive Path-Integral control to generate interactive trajectories. This framework incorporates a Bayesian inference approach to actively learn the agents' parameters, i.e., other drivers' model parameters. The proposed framework employs a sampling-based approach that is suitable for real-time implementation through the utilization of GPUs. We illustrate the effectiveness of our proposed methodology through comprehensive numerical simulations conducted in both high and low-fidelity simulation scenarios focusing on autonomous on-ramp merging.

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Authors (7)
  1. Jacob Knaup (4 papers)
  2. Jovin D'sa (7 papers)
  3. Behdad Chalaki (31 papers)
  4. Tyler Naes (2 papers)
  5. Hossein Nourkhiz Mahjoub (22 papers)
  6. Ehsan Moradi-Pari (16 papers)
  7. Panagiotis Tsiotras (110 papers)
Citations (3)
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