The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications

This paper presents the CAT Vehicle (Cognitive and Autonomous Test Vehicle) Testbed: a research testbed comprised of a distributed simulation-based autonomous vehicle, with straightforward transition to hardware in the loop testing and execution, to support research in autonomous driving technology. The evolution of autonomous driving technology from active safety features and advanced driving assistance systems to full sensor-guided autonomous driving requires testing of every possible scenario. However, researchers who want to demonstrate new results on a physical platform face difficult challenges, if they do not have access to a robotic platform in their own labs. Thus, there is a need for a research testbed where simulation-based results can be rapidly validated through hardware in the loop simulation, in order to test the software on board the physical platform. The CAT Vehicle Testbed offers such a testbed that can mimic dynamics of a real vehicle in simulation and then seamlessly transition to reproduction of use cases with hardware. The simulator utilizes the Robot Operating System (ROS) with a physics-based vehicle model, including simulated sensors and actuators with configurable parameters. The testbed allows multi-vehicle simulation to support vehicle to vehicle interaction. Our testbed also facilitates logging and capturing of the data in the real time that can be played back to examine particular scenarios or use cases, and for regression testing. As part of the demonstration of feasibility, we present a brief description of the CAT Vehicle Challenge, in which student researchers from all over the globe were able to reproduce their simulation results with fewer than 2 days of interfacing with the physical platform.Comment: In Proceedings SCAV 2018, arXiv:1804.0340

The CAT Vehicle Testbed: A Simulator with Hardware in the Loop for Autonomous Vehicle Applications

This paper presents the CAT Vehicle (Cognitive and Autonomous Test Vehicle) Testbed: a research testbed comprised of a distributed simulation-based autonomous vehicle, with straightforward transition to hardware in the loop testing and execution, to support research in autonomous driving technology. The evolution of autonomous driving technology from active safety features and advanced driving assistance systems to full sensor-guided autonomous driving requires testing of every possible scenario. However, researchers who want to demonstrate new results on a physical platform face difficult challenges, if they do not have access to a robotic platform in their own labs. Thus, there is a need for a research testbed where simulation-based results can be rapidly validated through hardware in the loop simulation, in order to test the software on board the physical platform. The CAT Vehicle Testbed offers such a testbed that can mimic dynamics of a real vehicle in simulation and then seamlessly transition to reproduction of use cases with hardware. The simulator utilizes the Robot Operating System (ROS) with a physics-based vehicle model, including simulated sensors and actuators with configurable parameters. The testbed allows multi-vehicle simulation to support vehicle to vehicle interaction. Our testbed also facilitates logging and capturing of the data in the real time that can be played back to examine particular scenarios or use cases, and for regression testing. As part of the demonstration of feasibility, we present a brief description of the CAT Vehicle Challenge, in which student researchers from all over the globe were able to reproduce their simulation results with fewer than 2 days of interfacing with the physical platform.National Science Foundation; Air Force Office of Scientific Research [1253334, 1262960, 1419419, 1446435, 1446690, 1446702, 1446715 1521617]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities

Traffic smoothing using explicit local controllers

The dissipation of stop-and-go waves attracted recent attention as a traffic management problem, which can be efficiently addressed by automated driving. As part of the 100 automated vehicles experiment named MegaVanderTest, feedback controls were used to induce strong dissipation via velocity smoothing. More precisely, a single vehicle driving differently in one of the four lanes of I-24 in the Nashville area was able to regularize the velocity profile by reducing oscillations in time and velocity differences among vehicles. Quantitative measures of this effect were possible due to the innovative I-24 MOTION system capable of monitoring the traffic conditions for all vehicles on the roadway. This paper presents the control design, the technological aspects involved in its deployment, and, finally, the results achieved by the experiment.Comment: 21 pages, 1 Table , 9 figure

Attention-Based Graph Neural Network for Label Propagation in Single-Cell Omics

Single-cell data analysis has been at forefront of development in biology and medicine since sequencing data has been made available. An important challenge in single-cell data analysis is the identification of cell types. Several methods have been proposed for cell-type identification. However, these methods do not capture the higher-order topological relationship between different samples. In this thesis, we propose an attention-based graph neural network that captures the higher-order topological relationship between different samples and performs transductive learning for predicting cell types. Evaluation of our method on publicly available datasets demonstrates the superiority of our method scAGN in terms of prediction accuracy. In addition, our method works best for highly sparse datasets in terms of F1 score, precision score, recall score, and Matthew's correlation coefficient as well. Further, our method's runtime complexity is consistently faster compared to other methods.Release after 05/19/202

Design and Synthesis of Controllers for Societal-Scale Cyber-Physical Systems

In this dissertation, a unifying framework for controller design, synthesis, and validation for societal-scale Cyber-Physical Systems (CPS) is proposed. We use vehicular CPS as a case study of societal-scale CPS. These systems require large-scale simulation to reduce the need for physical tests or field experiments. Such large-scale simulations demand reproducibility and repeatability of results---or else the use of simulation provides no insights into the overall system's dynamics. Many current simulation tools for CPS lack the properties of reproducibility and repeatability. Our proposed approach for \Rahul{scalability} and repeatability of existing simulation tools includes offloading dynamics of systems using federated models, message handling and synchronization to operate a simulator at slower than real-time, or synchronously with another system. The approaches do not require rewriting those simulation tools, thus permitting assembly of tools at their interfaces. With such simulation tools, it is now possible to use model-based design and code-generation techniques to deploy the same implementation in simulation that would be deployed in an experiment. Such approaches may be inaccessible for simulation tools that do not support real-time behavior. Our approach permits the validation of novel controllers and algorithms not only through software-in-the-loop (SWIL) or hardware-in-the-loop (HWIL) simulation but also transfer seamlessly for real-world testing. In this way, both model and simulator can be improved iteratively by feeding data from the physical environment. Large-scale CPS are producing a massive amount of data in real-time which is being used for decision-making and control that engage with infrastructure and humans. For Vehicular CPS, data comes in the form of multiple modalities such as CAN bus, GPS, dashcam, LiDAR, etc. We further propose a generic timeseries tool to work with such vehicular data that allows researchers to gain novel insights about driving behavior, discover rare events, and facilitate data-driven applications for vehicular CPS. We present three case studies: (i) Followerstopper, (ii) deployment of a reinforcement learning controller, and (iii) dual-ring-barrier traffic signal controller. The first two case studies are related to Lagrangian control of an autonomous vehicle (AV) in mixed urban traffic consisting of some highly automated vehicles among mostly human-driven vehicles. The third case study presents a co-simulation of an infrastructure controller using SUMO and ROS whose development takes an approach of model-based design. These use cases provide an engineering solution to improving a controller candidate for societal-scale CPS through a data-driven approach, deterministic and repeatable simulation, and their deployment in the real world.Release after 07/15/202

Optimal Receiver Design for Quantum Communication

An important problem in quantum information theory is finding the best possible performance of the optical communication channel employing suitable codewords, receiver design, and constellation optimization techniques. Many receiver designs have been studied in the past to discriminate Phase-Shift Keying (PSK) quantum states that are used to encode information before transmitting over the communication channel. Among many types of quantum states, there has been significant work on the use of coherent states for encoding information. Previous work has sought to improve the communication performance in terms of various metrics such as error probability of state discrimination and capacities by employing a number of quantum states such as coherent states and squeezed-displaced states. In this thesis, we provide optimal receiver design employing coherent states and squeezed-displaced states to maximize the mutual information and lower the error probability of state discrimination. In the case of pure coherent states, we derive an alternative channel capacity of phase-shift keying coherent state with a realizable displacement receiver by maximizing mutual information over symbol priors and pre-detection displacement. We find that the capacity is higher than the capacity achieved by maximizing mutual information over symbol prior but with zero displacements. The overall scheme demonstrates designing an improved, yet easy-to-implement receiver for better communication performance by tuning it at different photon number regime. We also explore the use of squeezing operations with a displacement receiver for state discrimination. Our calculation demonstrates that we see no performance improvement in terms of the probability of error of state discrimination or mutual information using displacement receivers when optimal squeezing on the transmitter side is used. In addition, we also study the receiver design scheme for QPSK modulation where squeezing is employed at the receiver side. We find that using the squeezing operation on the receiver side provides an advantage in terms of increased mutual information for the low-photon number regime compared to when no squeezing is used. In the later part of the thesis, we study entanglement-assisted communication using two-mode squeezing vacuum. The use of pre-shared entanglement in entanglement-assisted communication provides a superior alternative to classical communication specifically in the low brightness regime and highly noisy environment. In this thesis, we analyze the performance of a few low-complexity receivers that employ optical parametric amplifiers. In the simulation, we demonstrate that receiver designs with an entanglement-assisted scheme using phase-shift-keying modulation can outperform classical capacities. We describe a newly proposed 2x2 optical hybrid receiver for entanglement-assisted communication whose performance is roughly 10% better in terms of error probability as compared to previously proposed optical parametric amplifier-based receivers. Further, we find that using unequal priors for BPSK provides approximately three times the advantage over equal priors in terms of information rate