Two-level Hierarchical Architecture Research Articles

Automated Driving Control in Highway Scenarios Through a Two-Level Hierarchical Architecture

Automated Driving Control in Highway Scenarios Through a Two-Level Hierarchical Architecture

Safety-Critical and Flexible Cooperative On-Ramp Merging Control of Connected and Automated Vehicles in Mixed Traffic

Cooperative on-ramp merging control for connected and automated vehicles (CAVs) can effectively improve traffic throughput and vehicle fuel efficiency at highway on-ramp merging bottlenecks. However, in the mixed traffic scenario where CAVs and human-driven vehicles (HDVs) coexist, the uncertain maneuvers of human drivers pose a major challenge to merging control in terms of safety and flexibility. To this end, this paper proposes a hierarchical cooperative on-ramp merging control strategy for CAVs to optimize flexible trajectories with safety guarantees in mixed traffic. First, the on-ramp merging control problem for CAVs is considered in the case of a three-vehicle coordination, resulting in an optimal control problem (OCP) coordinating on-ramp and main-lane CAVs for efficient operation while satisfying multiple safety-critical constraints. Second, a two-level hierarchical control architecture is developed to solve the OCP with mixed state-control constraints. The upper-level planner solves an unconstrained OCP with Pontryagin’s Minimum Principle to calculate an expected merging position, which is embedded in the variable time headway of safe merging constraints in the lower-level controller. Then, the controller converts the nonlinear OCP with safety-critical constraints to a quadratic programing (QP) problem by exploiting Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). By solving the QP efficiently, the time and energy efficient trajectory for each CAV is obtained. In addition, a receding horizon control framework is employed, which enables CAVs to determine flexible merging opportunity and tackle the disturbances caused by HDVs. Finally, comprehensive simulation results show that the proposed cooperative on-ramp merging strategy has potential in enabling merging flexibility, improving traffic efficiency and energy economy in real time.

Elastically Isotropic Truss-Plate-Hybrid Hierarchical Microlattices with Enhanced Modulus and Strength.

Bioinspired hierarchical design principles have been employed to create advanced architected materials. Here, a new type of truss-plate-hybrid two-level hierarchical architecture is created, referred to as the ISO-COP hierarchical lattice (isotropic truss at the first level and cubic+octet plate at the second level), in which truss-based unit cells are arranged according to the topology of the plate-based unit cell. Finite element analyses reveal that the ISO-COP hierarchical lattice outperforms the best existing octet-truss hierarchical lattices based on fractal geometries in achieving elastic isotropy and enhanced moduli. According to the designed architecture, ISO-COP and several other comparison hierarchical microlattices are fabricated via projection microstereolithography. In situ compression tests demonstrate that the fabricated ISO-COP microlattices exhibit elastic isotropy and enhanced moduli, as predicted from finite element simulations, and superior strength compared with existing fractal octet-truss hierarchical lattices. Theoretical models are further developed to predict the dependence of modulus and failure modes on two design parameters of the hierarchical lattices, with results in good agreement with those from experiments. This study relates mechanical properties of ISO-COP hierarchical lattices to their architectures at each level of hierarchy and exemplifies a route to harnessing hierarchical design principles to create architected materials with desired mechanical properties.

Autonomous path planning with obstacle avoidance for smart assistive systems

Given the increased interest in smart assistive technologies and autonomous robot vehicles, path planning has emerged as one of the most researched and challenging topics in navigation. Moving to partially known or unknown environment, an assistive navigation system should be able to extract spatiotemporal information and dynamically identify objects and adjust the route. Current approaches typically rely on external services to perform high demanding computations and employ a plethora of overlapping sensors to accurately scan the surrounding environment. This increases their energy demands, size and weight, while incommodes their use in real time applications making their application to wearable assistive systems, such as smart glasses, a challenge. Aiming to provide a comfortable and computationally efficient wearable solution that can be used by human or robotic assistive systems, in this study we propose a novel two-level hierarchical architecture combining global and local path planning. The macroscale navigation involves the construction of the initial global path while the microscale navigation includes the local path planning with obstacle detection and avoidance. The methodology consists of: (i) a novel chaotic ant colony optimization algorithm with fuzzy logic (CACOF) for path construction; (ii) powerful and light weight deep convolutional neural networks for obstacle detection; and (iii) a Bug-like algorithm enhanced with fuzzy rules for obstacle avoidance in case of static objects. A vast experimental evaluation was conducted to test the proposed methodologies in a simulation environment based on the topology of real area. The results proved the computational efficiency and ability of the proposed path planning algorithms to address effectively multi-objective global and path planning problems which make them suitable for real time applications.

Human-aware motion reshaping using dynamical systems

In this work, we present a real-time approach for human-aware motion replanning using a two-level hierarchical architecture. The lower level leverages stable dynamical systems to generate motor commands and to online reshape the robot trajectories. The reshaping strategy modifies the velocity of the robot to match three requirements: i) to increase the human safety in case of close interaction with the robot, ii) to guarantee the correct task execution in case of unforeseen obstacles (including the human), and iii) to replan online the current task taking into account the human behavior. The lower level has to execute all needed computations in real-time. To this end, we also propose a novel approach that leverages depth space structure and parallel programming to rapidly and accurately estimate the robot-obstacle distances. The higher level of the architecture monitors the human and provides to the lower level information about the human status. The proposed approach has been tested in a human robot interaction scenario, showing promising results in terms of safe human-robot coexistence.

Conflict Management Considering a Smooth Transition of Aircraft Into Adjacent Airspace

A new framework for conflict resolution in air traffic control is studied using air traffic complexity, which ensures conflict avoidance among aircraft and smooth transition of aircraft into adjacent airspace. The air traffic complexity is modeled as deviations in heading angle or speed of the aircraft inside a sector to resolve any conflict induced by the entering aircraft. For conflict management in multisector planning, a two-level hierarchical architecture is proposed. In the higher level, the maneuver constraints of the aircraft are constructed to minimize complexity for the neighboring sectors. In the lower level, the conflict avoidance problem is formulated as a mixed integer linear programming considering the maneuver constraints, which are defined in the higher level. With the two-level hierarchical architecture, the aircraft can find the optimal solution to resolve conflicts among the aircraft and reduce the air traffic complexity of the neighboring sectors. The performance of the proposed conflict management is demonstrated using numerical simulations.

Modified LEACH-DT Algorithm with Hierarchical Extension for Wireless Sensor Networks

The selection of cluster heads is of immense importance in the cluster based protocols which are widely used in wireless sensor networks. However, for the random selection of cluster heads when the distance of cluster heads is also taken into account such as in LEACH-DT algorithm, the lifetime of the network is improved. In this paper, a new algorithm for selection of cluster heads is proposed where the residual energy of the cluster heads is also considered along with the distance to the base station. The proposed approach ensures better energy balance of the network nodes. The performance of the proposed algorithm is measured at various stages of network lifetime and a significant improvement over LEACH-DT has been observed. Also, a two-level heterogeneous hierarchical architecture of the proposed algorithm is investigated which further enhances the performance of the proposed algorithm.

Real-time network-wide traffic signal optimization considering long-term green ratios based on expected route flows

We propose a novel real-time network-wide traffic signal control scheme which is (1) applicable under modern data technologies, (2) flexible in response to variations of traffic flows due to its non-cyclic feature, (3) operable on a network-wide and real-time basis, and (4) capable of considering expected route flows in the form of long-term green time ratios for intersection movement. The proposed system has a two-level hierarchical architecture: (1) strategy level and (2) control level. Considering the optimal states for a long-term period found in the strategy level, the optimal signal timings for a short-term period are calculated in the control level which consists of two steps: (1) queue weight update and (2) signal optimization. Based on the ratio of the cumulative green time to the desired green time is the first step to update the queue weights, which are then used in the optimization to find signal timings for minimum total delay. A parametric queue weight function is developed, discussed and evaluated. Two numerical experiments were given. The first demonstrated that the proposed system performs effectively, and the second shows its capability in a real-world network.

Toward Elastic Distributed SDN/NFV Controller for 5G Mobile Cloud Management Systems

Future 5G mobile network architecture is expected to offer capacities to accommodate the inexorable rise in mobile data traffic and to meet further stringent latency and reliability requirements to support diverse high data rate applications and services. Mobile cloud computing (MCC) in 5G has emerged as a key paradigm, promising to augment the capability of mobile devices through provisioning of computational resources on demand, and enabling resource-constrained mobile devices to offload their processing and storage requirements to the cloud infrastructure. Follow-me cloud (FMC), in turn, has emerged as a concept that allows seamless migration of services according to the corresponding users mobility. Meanwhile, software-defined networking (SDN) is a new paradigm that permits the decoupling of the control and data planes of traditional networks and provides programmability and flexibility, allowing the network to dynamically adapt to change traffic patterns and user demands. While the SDN implementations are gaining momentum, the control plane is still suffering from scalability and performance concerns for a very large network. In this paper, we address these scalability and performance issues in the context of 5G mobile networks by introducing a novel SDN/OpenFlow-based architecture and control plane framework tailored for MCC-based systems and more specifically for FMC-based systems where mobile nodes and network services are subject to constraints of movements and migrations. Contrary to a centralized approach with a single SDN controller, our approach permits the distribution of the SDN/OpenFlow control plane on a two-level hierarchical architecture: a first level with a Global FMC Controller (G-FMCC), and a second level with several Local FMC Controllers (L-FMCCs). Thanks to our control plane framework and Network Function Virtualization (NFV) concept, the L-FMCCs are deployed on-demand, where and when needed, depending on the global system load. Results obtained via analysis show that our solution ensures more efficient management of control plane, performance maintaining, and network resources preservation.

Stochastic model predictive power management strategy for series hydraulic hybrid vehicle

With the expectation that series configuration would maximise the fuel economy as engine is decoupled from the wheels, a forward-facing, series hydraulic hybrid vehicle (SHHV) powertrain model for medium size trucks is developed in this paper, and is employed to investigate the application of stochastic model predictive control (SMPC) methodology for developing power management strategy. For properly managing all the control variables that arise from the significant freedoms introduced by the SHHV powertrain, two-level hierarchical control architecture is proposed. Simulation results over the urban driving cycle are presented to demonstrate the effectiveness of SMPC compared with other deterministic approaches and the potential of the selected hybrid system to substantially improve vehicle fuel economy.

Scalability Analysis of the Hierarchical Architecture for Distributed Virtual Environments

A distributed virtual environment (DVE) is a shared virtual environment where multiple users at their workstations interact with each other over a network. Some of these systems may support a large number of users, for example, multiplayer online games. An important issue is how well the system scales as the number of users increases. In terms of scalability, a promising system architecture is a two-level hierarchical architecture. At the lower level, multiple servers are deployed; each server interacts with its assigned users. At the higher level, the servers ensure that their copies of the virtual environment are as consistent as possible. Although the two-level architecture is believed to have good properties with respect to scalability, not much is known about its performance characteristics. In this paper, we develop a performance model for the two-level architecture and obtain analytic results on the workload experienced by each server. Our results provide valuable insights into the scalability of the architecture. We also investigate the issue of consistency and develop a novel technique to achieve weak consistency among copies of the virtual environment at the various servers. Simulation results on the consistency/scalability trade-off are presented.

Large-scale multimedia content delivery over optical networks for interactive TV services

iTVP is a system built for delivery of live TV programming, video-on-demand and audio-on-demand with interactive access over IP networks. It has a nationwide range and is designed to provide service to a high number of concurrent users. Hence, scalability is one of the most important issues. In this paper we described how scalability is achieved in a two-level hierarchical architecture of the delivery system. We present the principles of content caching mechanisms and the management of the resulting distributed content repository. The system is implemented and currently operates in a test installation. Although the range of the test system operations is limited, as far as the number of users and the size of the content repository is concerned, the experience gained with iTVP so far provides an insight into the expected prototype performance. We present a system performance evaluation and describe the experience gained during the iGrid demonstration.

High performance, high throughput turbo/SOVA decoder design

Two efficient approaches are proposed to improve the performance of soft-output Viterbi (1998) algorithm (SOVA)-based turbo decoders. In the first approach, an easily obtainable variable and a simple mapping function are used to compute a target scaling factor to normalize the extrinsic information output from turbo decoders. An extra coding gain of 0.5 dB can be obtained with additive white Gaussian noise channels. This approach does not introduce extra latency and the hardware overhead is negligible. In the second approach, an adaptive upper bound based on the channel reliability is set for computing the metric difference between competing paths. By combining the two approaches, we show that the new SOVA-based turbo decoders can approach maximum a posteriori probability (MAP)-based turbo decoders within 0.1 dB when the target bit-error rate (BER) is moderately low (e.g., BER<10/sup -4/ for 1/2 rate codes). Following this, practical implementation issues are discussed and finite precision simulation results are provided. An area-efficient parallel decoding architecture is presented in this paper as an effective approach to design high-throughput turbo/SOVA decoders. With the efficient parallel architecture, multiple times throughput of a conventional serial decoder can be obtained by increasing the overall hardware by a small percentage. To resolve the problem of multiple memory accesses per cycle for the efficient parallel architecture, a novel two-level hierarchical interleaver architecture is proposed. Simulation results show that the proposed interleaver architecture performs as well as random interleavers, while requiring much less storage of random patterns.

A new hierarchical architecture for Air Traffic Management: Optimisation of airway capacity in a Free Flight scenario

We describe a new two-level hierarchical architecture for air traffic management problems with corresponding mathematical models. The first level represents the air route network, and its solutions provide the air traffic flows on each arc of the network. This level interacts with the second one, which represents the single airway and its own air traffic flows. This latter model allows us to assign the optimal air traffic route to each aircraft and to optimise the airway's capacity. Furthermore, for the airway optimisation model we have also carried out a computational analysis, providing both exact and heuristic solutions, for problem instances based on real data. These are obtained with the Cplex solver exploiting the mixed integer mathematical formulation and with a proposed heuristic algorithm for problems of larger size, respectively. The heuristic solutions obtained are within a maximum gap of 13% from the LP relaxation.

A two-level hierarchical approach for raw material scheduling in steelworks

Raw material scheduling problems in steelworks suffer from the computational complexity arising from the interdependence and interference between various items of transport equipment. To deal effectively with such complexity, this paper proposes a two-level hierarchical architecture for the construction of predictive schedules to transport raw materials from stock yards to processing plants. The architecture allocates the raw materials required, to multiple lower-level dispatchers, each of which schedules its reclaimer to withdraw the assigned raw materials. A higher-level scheduler constructs belt-conveyor schedules for transporting the reclaimed raw materials to their processing plants. If any conflict occurs within a dispatcher during the scheduling process, the higher-level scheduler coordinates other dispatchers for conflict resolution. Based on this architecture, a scheduling expert system called RAMS was developed using G2, to automate the raw material scheduling process at a steelworks. By providing the predictive raw material transport schedule, the system is proved to relieve the burden on the human scheduler and to improve schedule performances.