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Karimi Shahri, Pouria
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Negotiating the Steering Control Authority within Haptic Shared Control Framework

University of North Carolina Charlotte-Vahid Izadi, Amir H. Ghasemi, Pouria Karimi Shahri
  • Technical Paper
  • 2020-01-1031
To be published on 2020-04-14 by SAE International in United States
Communication and cooperation among team members can be enhanced significantly with physical interaction. Successful collaboration requires the integration of the individual partners' intentions into a shared action plan, which may involve a continuous negotiation of intentions and roles. This project aims to explore the underlying process of intention integration and develop models for consensus reaching in a haptic shared control framework. We pay particular attention to the role of impedance modulation as a mechanism for negotiation of intentions across the physical or haptic channel. We present an optimal control-based methodology for an automation system to modulate its impedance to either gain or yield the authority to the human driver.
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Modelling and Control of Multi-Vehicle Traffic Networks Using an Integrated VISSIM-Matlab Simulation Platform

University of North Carolina Charlotte-Shubhankar Chintamani Shindgikar, Pouria Karimi Shahri, Amir H. Ghasemi
  • Technical Paper
  • 2020-01-0887
To be published on 2020-04-14 by SAE International in United States
This paper aims to develop a platform for integrating PTV VISSIM and MATLAB Simulink to design and analyze the flow of traffic in an urban traffic network. We model a non-signalized traffic network in VISSIM. From VISSIM, we take the inflow and outflow rates data and send them to the controller in MATLAB through a VISSIM Component Object (COM) interface. By employing an extremum seeking approach, an optimal velocity is determined and sent back to VISSIM through the COM interface. Numerical simulation demonstrates the effectiveness of the platform for testing different traffic control approaches and optimizing flow.
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Traffic Control Strategies for Congested Heterogeneous Multi-Vehicle Networks

University of North Carolina Charlotte-Pouria Karimi Shahri, Amir H. Ghasemi, Vahid Izadi
  • Technical Paper
  • 2020-01-0086
To be published on 2020-04-14 by SAE International in United States
The primary goal of this paper is to pioneer and develop robust and adaptive algorithms for controlling autonomous vehicles in heterogeneous networks with the aim of maximizing the performance (in terms of mobility) and minimizing variation in the network. While the fundamental approaches and models proposed in this research can be applied to any heterogeneous multi-agent system, we select heterogeneous traffic networks as a set-up for exploring the proposed research. We consider the heterogeneity in the system in the form of a mix of autonomous and human-driven vehicles (different levels of autonomous vehicle penetration). We propose a two-level hierarchical controller wherein the upper-level controller, an optimization problem using the concept of macroscopic fundamental diagram is formulated to deal with the traffic demand balance problem. At the lower level, using the microscopic models of the network, the control actions for each vehicle will be determined such that he optimal flow received from the upper-level controllers can be tracked.