12 research outputs found
Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects
Connected and automated vehicles (CAVs) have emerged as a potential solution
to the future challenges of developing safe, efficient, and eco-friendly
transportation systems. However, CAV control presents significant challenges,
given the complexity of interconnectivity and coordination required among the
vehicles. To address this, multi-agent reinforcement learning (MARL), with its
notable advancements in addressing complex problems in autonomous driving,
robotics, and human-vehicle interaction, has emerged as a promising tool for
enhancing the capabilities of CAVs. However, there is a notable absence of
current reviews on the state-of-the-art MARL algorithms in the context of CAVs.
Therefore, this paper delivers a comprehensive review of the application of
MARL techniques within the field of CAV control. The paper begins by
introducing MARL, followed by a detailed explanation of its unique advantages
in addressing complex mobility and traffic scenarios that involve multiple
agents. It then presents a comprehensive survey of MARL applications on the
extent of control dimensions for CAVs, covering critical and typical scenarios
such as platooning control, lane-changing, and unsignalized intersections. In
addition, the paper provides a comprehensive review of the prominent simulation
platforms used to create reliable environments for training in MARL. Lastly,
the paper examines the current challenges associated with deploying MARL within
CAV control and outlines potential solutions that can effectively overcome
these issues. Through this review, the study highlights the tremendous
potential of MARL to enhance the performance and collaboration of CAV control
in terms of safety, travel efficiency, and economy
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
The ongoing amalgamation of UAV and ML techniques is creating a significant
synergy and empowering UAVs with unprecedented intelligence and autonomy. This
survey aims to provide a timely and comprehensive overview of ML techniques
used in UAV operations and communications and identify the potential growth
areas and research gaps. We emphasise the four key components of UAV operations
and communications to which ML can significantly contribute, namely, perception
and feature extraction, feature interpretation and regeneration, trajectory and
mission planning, and aerodynamic control and operation. We classify the latest
popular ML tools based on their applications to the four components and conduct
gap analyses. This survey also takes a step forward by pointing out significant
challenges in the upcoming realm of ML-aided automated UAV operations and
communications. It is revealed that different ML techniques dominate the
applications to the four key modules of UAV operations and communications.
While there is an increasing trend of cross-module designs, little effort has
been devoted to an end-to-end ML framework, from perception and feature
extraction to aerodynamic control and operation. It is also unveiled that the
reliability and trust of ML in UAV operations and applications require
significant attention before full automation of UAVs and potential cooperation
between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure
Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms
This book is a reprint of the Special Issue “Intelligent Autonomous Decision-Making and Cooperative Control Technology of High-Speed Vehicle Swarms”,which was published in Applied Sciences
Intelligent Transportation Systems, Hybrid Electric Vehicles, Powertrain Control, Cooperative Adaptive Cruise Control, Model Predictive Control
Information obtainable from Intelligent Transportation Systems (ITS) provides the possibility of improving the safety and efficiency of vehicles at different levels. In particular, such information has the potential to be utilized for prediction of driving conditions and traffic flow, which allows us to improve the performance of the control systems in different vehicular applications, such as Hybrid Electric Vehicles (HEVs) powertrain control and Cooperative Adaptive Cruise Control (CACC). In the first part of this work, we study the design of an MPC controller for a Cooperative Adaptive Cruise Control (CACC) system, which is an automated application that provides the drivers with extra benefits, such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as interfering vehicles cutting-into the CACC platoons or hard braking by leading cars. To address this problem, we first propose a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme. Then, the predicted trajectory of each vehicle in the adjacent lanes is used to estimate the probability of that vehicle cutting-into the CACC platoon. To consider the calculated probability in control system decisions, a Stochastic Model Predictive Controller (SMPC) needs to be designed which incorporates this cut-in probability, and enhances the reaction against the detected dangerous cut-in maneuver. However, in this work, we propose an alternative way of solving this problem. We convert the SMPC problem into modeling the CACC as a Stochastic Hybrid System (SHS) while we still use a deterministic MPC controller running in the only state of the SHS model. Finally, we find the conditions under which the designed deterministic controller is stable and feasible for the proposed SHS model of the CACC platoon. In the second part of this work, we propose to improve the performance of one of the most promising realtime powertrain control strategies, called Adaptive Equivalent Consumption Minimization Strategy (AECMS), using predicted driving conditions. In this part, two different real-time powertrain control strategies are proposed for HEVs. The first proposed method, including three different variations, introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. The factor is proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy by 4\%. Finally, we integrate the recent development in reinforcement learning to design a novel multi-level power distribution control. The proposed controller reacts in two levels, namely high-level and low-level control. The high-level control decision estimates the most probable driving profile matched to the current (and near future) state of the vehicle. Then, the corresponding low-level controller of the selected profile is utilized to distribute the requested power between Electric Motor (EM) and Internal Combustion Engine (ICE). This is important because there is no other prior work addressing this problem using a controller which can adjust its decision to the driving pattern. We proposed to use two reinforcement learning agents in two levels of abstraction. The first agent, selects the most optimal low-level controller (second agent) based on the overall pattern of the drive cycle in the near past and future, i.e., urban, highway and harsh. Then, the selected agent by the high-level controller (first agent) decides how to distribute the demanded power between the EM and ICE. We found that by carefully designing a training scheme, it is possible to effectively improve the performance of this data-driven controller. Simulation results show up to 6\% improvement in fuel economy compared to the AECMS
Autonomous system control in unknown operating conditions
Autonomous systems have become an interconnected part of everyday life with the
recent increases in computational power available for both onboard computers and
offline data processing. The race by car manufacturers for level 5 (full) autonomy in
self-driving cars is well underway and new flying taxi service startups are emerging
every week, attracting billions in investments. Two main research communities,
Optimal Control and Reinforcement Learning stand out in the field of autonomous
systems, each with a vastly different perspective on the control problem. Controllers
from the optimal control community are based on models and can be rigorously
analyzed to ensure the stability of the system is maintained under certain operating
conditions. Learning-based control strategies are often referred to as model-free and
typically involve training a neural network to generate the required control actions
through direct interactions with the system. This greatly reduces the design effort
required to control complex systems. One common problem both learning- and model-
based control solutions face is the dependency on a priori knowledge about the system
and operating conditions such as possible internal component failures and external
environmental disturbances. It is not possible to consider every possible operating
scenario an autonomous system can encounter in the real world at design time. Models
and simulators are approximations of reality and can only be created for known
operating conditions. Autonomous system control in unknown operating conditions,
where no a priori knowledge exists, is still an open problem for both communities and
no control methods currently exist for such situations.
Multiple model adaptive control is a modular control framework that divides the
control problem into supervisory and low-level control, which allows for the
combination of existing learning- and model-based control methods to overcome the
disadvantages of using only one of these. The contributions of this thesis consist of
five novel supervisory control architectures, which have been empirically shown to
improve a system’s robustness to unknown operating conditions, and a novel low-
level controller tuning algorithm that can reduce the number of required controllers
compared to traditional tuning approaches. The presented methods apply to any
autonomous system that can be controlled using model-based controllers and can
be integrated alongside existing fault-tolerant control systems to improve robustness
to unknown operating conditions. This impacts autonomous system designers by
providing novel control mechanisms to improve a system’s robustness to unknown
operating conditions
Advanced Techniques for Design and Manufacturing in Marine Engineering
Modern engineering design processes are driven by the extensive use of numerical simulations; naval architecture and ocean engineering are no exception. Computational power has been improved over the last few decades; therefore, the integration of different tools such as CAD, FEM, CFD, and CAM has enabled complex modeling and manufacturing problems to be solved in a more feasible way. Classical naval design methodology can take advantage of this integration, giving rise to more robust designs in terms of shape, structural and hydrodynamic performances, and the manufacturing process.This Special Issue invites researchers and engineers from both academia and the industry to publish the latest progress in design and manufacturing techniques in marine engineering and to debate the current issues and future perspectives in this research area. Suitable topics for this issue include, but are not limited to, the following:CAD-based approaches for designing the hull and appendages of sailing and engine-powered boats and comparisons with traditional techniques;Finite element method applications to predict the structural performance of the whole boat or of a portion of it, with particular attention to the modeling of the material used;Embedded measurement systems for structural health monitoring;Determination of hydrodynamic efficiency using experimental, numerical, or semi-empiric methods for displacement and planning hulls;Topology optimization techniques to overcome traditional scantling criteria based on international standards;Applications of additive manufacturing to derive innovative shapes for internal reinforcements or sandwich hull structures
Proceedings of the Cardiff University Engineering Research Conference 2023
The conference was established for the first
time in 2023 as part of a programme to sustain the research
culture, environment, and dissemination activities of the
School of Engineering at Cardiff University in the United
Kingdom. The conference served as a platform to celebrate
advancements in various engineering domains researched
at our School, explore and discuss further advancements in
the diverse fields that define contemporary engineering
The research on mechanical properties and compressive behavior of graphene foam with multi-scale model?
Computational simulation is an effective method to study the deformation
mechanism and mechanical behaviour of graphene-based porous materials.
However, due to limitations in computational methods and costs, existing
research model deviate significantly from the real material in terms of the
scale of structure. Therefore, building a highly accurate computational model
and maintaining an appropriate cost is both necessary and challenging. This
paper proposed a multi-scale modelling approach for finite element (FE)
analysis based on the concept of structural hierarchy. The stochastic feature
of the microstructure of porous materials are also considered. The simulation
results of the regular structure model and the Voronoi tessellation model are
compared to investigate the effect of regularity on the material properties.
Despite some shortcomings, other microstructural features of porous
graphene materials can be gradually introduced to improve the material
model step by step. Thus the developed multiscale model has great potential
to simulate the properties of materials with mesoscopic size structure such as
graphene foam (GF)
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments