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10. SLAS: Speed and Lane Advisory System for Highway Navigation

Author

Tariq, Faizan M., Isele, David, Baras, John S., and Bae, Sangjae

Subjects
FOS: Computer and information sciences, Computer Science - Robotics, FOS: Electrical engineering, electronic engineering, information engineering, Systems and Control (eess.SY), Robotics (cs.RO), Electrical Engineering and Systems Science - Systems and Control
Abstract

This paper proposes a hierarchical autonomous vehicle navigation architecture, composed of a high-level speed and lane advisory system (SLAS) coupled with low-level trajectory generation and trajectory following modules. Specifically, we target a multi-lane highway driving scenario where an autonomous ego vehicle navigates in traffic. We propose a novel receding horizon mixed-integer optimization based method for SLAS with the objective to minimize travel time while accounting for passenger comfort. We further incorporate various modifications in the proposed approach to improve the overall computational efficiency and achieve real-time performance. We demonstrate the efficacy of the proposed approach in contrast to the existing methods, when applied in conjunction with state-of-the-art trajectory generation and trajectory following frameworks, in a CARLA simulation environment., Presented at the IEEE 61st Conference on Decision and Control (CDC), Cancun, Mexico, 2022

Published
2022
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18. A Robust Mean-field Game of Boltzmann-Vlasov-like Traffic Flow

Author

Tirumalai, Amoolya and Baras, John S.

Subjects
Optimization and Control (math.OC), FOS: Mathematics, FOS: Electrical engineering, electronic engineering, information engineering, Systems and Control (eess.SY), Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control
Abstract

Historically, traffic modelling approaches have taken either a particle-like (microscopic) approach, or a gas-like (meso- or macroscopic) approach. Until recently with the introduction of mean-field games to the controls community, there has not been a rigorous framework to facilitate passage between controls for the microscopic models and the macroscopic models. We begin this work with a particle-based model of autonomous vehicles subject to drag and unknown disturbances, noise, and a speed limit in addition to the control. We formulate a robust stochastic differential game on the particles. We pass formally to the infinite-particle limit to obtain a robust mean-field game PDE system. We solve the mean-field game PDE system numerically and discuss the results. In particular, we obtain an optimal control which increases the bulk velocity of the traffic flow while reducing congestion., Omission of a line corrected in this version. 6 pages; 3 figures; 1 algorithm; Submitted to ACC 2022

Published
2022
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49. Value of Information in Feedback Control: Quantification.

Author

Soleymani, Touraj, Baras, John S., and Hirche, Sandra

Subjects
*INFORMATION resources management, *DATA packeting, *DATA transmission systems, *SYMMETRIC functions, *COST effectiveness, *PSYCHOLOGICAL feedback
Abstract

Although transmission of a data packet containing sensory information in a networked control system improves the quality of regulation, it has indeed a price from the communication perspective. It is, therefore, rational that such a data packet be transmitted only if it is valuable in the sense of a cost-benefit analysis. Yet, the fact is that little is known so far about this valuation of information and its connection with traditional event-triggered communication. In the present article, we study this intrinsic property of networked control systems by formulating a rate-regulation trade-off between the packet rate and the regulation cost with an event trigger and a controller as two distributed decision makers, and show that the valuation of information is conceivable and quantifiable grounded on this trade-off. In particular, we characterize an equilibrium in the rate-regulation trade-off, and quantify the value of information ${\rm{VoI}}_k$ there as the variation in a so-called value function with respect to a piece of sensory information that can be communicated to the controller at each time $k$. We prove that, for a multi-dimensional Gauss--Markov process, ${\rm{VoI}}_k$ is a symmetric function of the discrepancy between the state estimates at the event trigger and the controller, and that a data packet containing sensory information at time $k$ should be transmitted to the controller only if ${\rm{VoI}}_k$ is nonnegative. Moreover, we discuss that ${\rm{VoI}}_k$ can be computed with arbitrary accuracy, and that it can be approximated by a closed-form quadratic function with a performance guarantee. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Feedback Control Over Noisy Channels: Characterization of a General Equilibrium.

Author

Soleymani, Touraj, Baras, John S., Hirche, Sandra, and Johansson, Karl H.

Subjects
*ADDITIVE white Gaussian noise channels, *FEEDBACK control systems, *PSYCHOLOGICAL feedback
Abstract

In this article, we study an energy-regulation tradeoff that delineates the fundamental performance bound of a feedback control system over a noisy channel in an unreliable communication regime. The channel and the process are modeled by an additive white Gaussian noise channel with fading and a partially observable Gauss–Markov process, respectively. Moreover, the feedback control loop is constructed by designing an encoder with a scheduler and a decoder with a controller. The scheduler and the controller are the decision makers deciding about the transmit power and the control input at each time, respectively. Associated with the energy-regulation tradeoff, we characterize an equilibrium at which neither the scheduler nor the controller has a unilateral incentive to deviate from its policy. We argue that this equilibrium is a general one as it attains global optimality without any restrictions on the information structure or the policy structure, despite the presence of signaling and dual effects. [ABSTRACT FROM AUTHOR]

Published
2022
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