Cooperative Adaptive Research Articles

Blockchain-Integrated Multiagent Deep Reinforcement Learning for Securing Cooperative Adaptive Cruise Control

Connected and Autonomous Vehicles (CAVs) are an emerging solution to the issues of safe and sustainable transportation systems in the future. One major transport technology for CAVs is Cooperative Adaptive Cruise Control (CACC), for which unsignalized autonomous intersection crossing is a growing use case. CACC relies heavily on inter-vehicular communication and is thus vulnerable to message forgery and jamming attacks. Most solutions for CACC focus exclusively on enhancing efficiency or security but do not offer an integrated framework for achieving both on a large scale. In this paper, we propose a Blockchain-integrated Multi-Agent Deep Reinforcement Learning (Block-MADRL) architecture for enhancing the efficiency of CACC while cooperatively detecting attacks, reducing the fuel efficiency of identified attackers and securely notifying the overall network. Our approach uses multi-agent deep reinforcement learning to find fuel and throughput optimizing solutions for CACC and a cooperative verification mechanism based on Extended Isolation Forest (EIF) for attack detection. Attacker data is securely stored in a Road Side Unit (RSU) level blockchain, and we design a low-latency, high throughput consensus protocol for speedy and secure data dissemination. Simulation results indicate over 29.5% better lane throughput with our approach during acceleration forgery attack, up to 23% induced reduction in fuel efficiency of malicious vehicles, 17.6% higher blockchain throughput through our consensus protocol and over 8% improvement in attack detection rate compared to the state-of-the-art.

Impact of Cooperative Adaptive Cruise Control on Traffic Stability

Cooperative adaptive cruise control (CACC) is one of the popular connected and automated vehicle (CAV) applications for cooperative driving automation with combined connectivity and automation technologies to improve string stability. This study aimed to derive the string stability conditions of a CACC controller and analyze the impacts of CACC on string stability for both a fleet of homogeneous CAVs and for heterogeneous traffic with human-driven vehicles (HDVs), connected vehicles (CVs) with connectivity technologies only, and autonomous vehicles (AVs) with automation technologies only. We mathematically analyzed the impact of CACC on string stability for both homogeneous and heterogeneous traffic flow. We adopted parameters from literature for HDVs, CVs, and AVs for the heterogeneous traffic case. We found there was a minimum constant time headway required for each parameter design to ensure stability in homogeneous CACC traffic. In addition, the constant time headway and the length of control time interval had positive correlation with stability, but the control parameter had a negative correlation with stability. The numerical analysis also showed that CACC vehicles could maintain string stability better than CVs and AVs under low HDV market penetration rates for the mixed traffic case.

Real-Time Distributed Cooperative Adaptive Cruise Control Model Considering Time Delays and Actuator Lag

Real-time control of a fleet of Connected and Automated Vehicles (CAV) for Cooperative Adaptive Cruise Control (CACC) is a challenging problem concerning time delays (from sensing, communication, and computation) and actuator lag. This paper proposes a real-time predictive distributed CACC control framework that addresses time delays and actuator lag issues in the real-time networked control systems. We first formulate a Kalman Filter-based real-time current driving state prediction model to provide more accurate initial conditions for the distributed CACC controller by compensating time delays using sensing data from multi-rate onboard sensors (e.g., Radar, GPS, wheel speed, and accelerometer), and status-sharing and intent-sharing data in BSM via V2V communication. We solve the prediction model using a sequential Kalman Filter update process for multi-rate sensing data to improve computational efficiency. We propose a real-time distributed MPC-based CACC controller with actuator lag and intent-sharing information for each CAV with the delay-compensated predicted current driving states as initial conditions. We implement the real-time predictive distributed CACC control algorithms and conduct numerical analyses to demonstrate the benefits of intent-sharing-based distributed computing, delay compensation, and actuator lag consideration on string stability under various traffic dynamics.

Distributed Formation Control Algorithm for Improved String Stability in Heterogenic Vehicle Platoons

Achieving speed, safety and energy optimization in vehicle platoons is among the important topics of recent years. In this context, this work focuses on improving the string stability (SS) of heterogeneous platoons. Better SS allows for smaller gap between vehicles, which means shorter time headway for Cooperative Adaptive Cruise Control (CACC) or inter-vehicle following-distance for platooning. Shorter time headway or inter-vehicle distance results in better road use and less fuel consumption. Rather than compensating for dynamic differences by means of low level control schemes by implementing pre-compensators at vehicle level, this work takes a different approach, where a higher level of control is preferred to improve SS: The platoon formation. To achieve this, it is important to ensure that each vehicle is in its optimum position in the platoon based on its dynamics for highest string stability. This generally leads to the vehicle with highest inertia to lead the platoon and the lowest one as the last follower. In this study, a platoon formation algorithm is proposed to run on each vehicle, leading to an optimum overall string structure. The efficiency of the algorithm is demonstrated by simulations in Matlab(R) for a four-vehicle platoon.

The role of trust, opportunism and adaptation in coordination and contract cooperation: evidence from construction projects

PurposeThe purpose of this paper is to examine the influence of clients' trust, opportunism and adaptation on contractual (non)cooperation with a mediating role of coordination in the construction industry.Design/methodology/approachA questionnaire was used to collect data from employees of the Pakistani construction industry. Smart partial least square (SmartPLS) has been used for analyzing the data of 270 respondents from construction projects.FindingsThe results of the SmartPLS indicate that (1) Trust and contract coordination positively while opportunism negatively influence contractor's contractual cooperation. (2) Contract adaptation and contract coordination positively influence the noncontractual cooperation of the contractor. (3) Moreover, contract coordination positively mediates the relationship between trust and noncontractual cooperation, but negatively mediates the relationship between opportunism and contract adaptation and noncontractual cooperation.Practical implicationsThe findings of this research suggest several policy implications for administrative authorities, project managers and policymakers. These authorities need to focus on clients' trust, opportunism and adaptation because these factors significantly influence contract coordination and cooperation in the construction industry. Emphasizing these factors will enable project managers to gain economies of scale and mitigate project failure.Originality/valueTo the best of the authors’ search and knowledge, they did not find any study examining the mediating role of coordination between trust, opportunism, adaptation and cooperation in the construction industry. Hence, the present study advances their understanding in the field of project management and construction business.

Adaptive Cooperative Control of Multiple Urban Rail Trains with Position Output Constraints

This paper studies the distributed adaptive cooperative control of multiple urban rail trains with position output constraints and uncertain parameters. Based on an ordered set of trains running on the route, a dynamic multiple trains movement model is constructed to capture the dynamic evolution of the trains in actual operation. Aiming at the position constraints and uncertainties in the system, different distributed adaptive control algorithms are designed for all trains by using the local information about the position, speed and acceleration of the train operation, so that each train can dynamically adjust its speed through communicating with its neighboring trains. This control algorithm for each train is designed to track the desired position and speed curve, and the headway distance between any two neighboring trains is stable within a preset safety range, which guarantee the safety of tracking operation of multiple urban rail trains. Finally, the effectiveness of the designed scheme is verified by numerical examples.

Adaptive Cooperative Control for Human-Robot Load Manipulation

In this letter, we propose a control strategy for human-robot cooperative manipulation under the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ambiguous</i> collaboration of a human agent. To cope with this uncertainty, an adaptive update law inferring the human contribution to the system dynamics from basic perception feedback through the human arm stiffness is used. Furthermore, the robustness and accuracy of the approach is enhanced by redundantly tracking the shared load references and its associated end-effector position references. To validate the control strategy, both theoretical Lyapunov stability analysis and experimental results –employing two robot manipulators with 6 degrees of freedom under external disturbances– are provided.

Generating and Reporting Electronic Clinical Quality Measures from Electronic Health Records: Strategies from EvidenceNOW Cooperatives.

Electronic clinical quality measures (eCQMs) from electronic health records (EHRs) are a key component of quality improvement (QI) initiatives in small-to-medium size primary care practices, but using eCQMs for QI can be challenging. Organizational strategies are needed to effectively operationalize eCQMs for QI in these practice settings. This study aimed to characterize strategies that seven regional cooperatives participating in the EvidenceNOW initiative developed to generate and report EHR-based eCQMs for QI in small-to-medium size practices. A qualitative study comprised of 17 interviews with representatives from all seven EvidenceNOW cooperatives was conducted. Interviewees included administrators were with both strategic and cooperative-level operational responsibilities and external practice facilitators were with hands-on experience helping practices use EHRs and eCQMs. A subteam conducted 1-hour semistructured telephone interviews with administrators and practice facilitators, then analyzed interview transcripts using immersion crystallization. The analysis and a conceptual model were vetted and approved by the larger group of coauthors. Cooperative strategies consisted of efforts in four key domains. First, cooperative adaptation shaped overall strategies for calculating eCQMs whether using EHRs, a centralized source, or a "hybrid strategy" of the two. Second, the eCQM generation described how EHR data were extracted, validated, and reported for calculating eCQMs. Third, practice facilitation characterized how facilitators with backgrounds in health information technology (IT) delivered services and solutions for data capture and quality and practice support. Fourth, performance reporting strategies and tools informed QI efforts and how cooperatives could alter their approaches to eCQMs. Cooperatives ultimately generated and reported eCQMs using hybrid strategies because they determined neither EHRs alone nor centralized sources alone could operationalize eCQMs for QI. This required cooperatives to devise solutions and utilize resources that often are unavailable to typical small-to-medium-sized practices. The experiences from EvidenceNOW cooperatives provide insights into how organizations can plan for challenges and operationalize EHR-based eCQMs.

WAVE communication based on improved NLMS adaptive filtering delay compensation method*

In order to improve the signal quality of the Wireless Access Vehicle Environment (WAVE), in this paper, the dynamic step factor is introduced into the Normalized Least Mean Square (NLMS) algorithm based on multi-step step descent, so that the algorithm has a wide dynamic adaptive range. And an improved NLMS adaptive filtering method is proposed to reduce the noise of the communication signal of the WAVE system. At the same time, by introducing the unit delay operator z −Δ of the adaptive FIR filter and the transmission delay t p of the WAVE system, a delay compensation function is constructed to solve the delay problem in the signal transmission and processing process of the WAVE system and meet the real-time requirement of the system. In this paper, the above method is simulated and tested under typical working conditions of the Cooperative Adaptive Cruise Control (CACC) system. Experimental results show that the NLMS adaptive filtering method can effectively reduce the error between the acquired signal and the expected signal and make the signal waveform smoother by introducing the dynamic step factor. By introducing the filtering delay compensation function into the signal filtering results, it can effectively reduce the adverse effects of the delay problem caused by signal transmission and signal processing on the desired target signal.

Enabling cooperative adaptive cruise control on strings of vehicles with heterogeneous dynamics and powertrains

Recent studies have shown that positive impact of Cooperative Adaptive Cruise Control (CACC) can only be guaranteed as market penetration rate increases. Removing the string homogeneity constraint is essential to encourage widespread adoption. In this work, a hierarchical architecture is proposed to enable CACC on vehicles with not only mixed dynamics but also different powertrain types. A low-level layer deals with the vehicle and powertrain dynamics to provide accurate and consistent reference speed tracking response. The high-level layer uses: (1) a Linear Parameter Varying feedback system to provide loop stability, robustness and enforce a variable time gap policy and (2) a feedforward system that processes Vehicle-to-Vehicle information to enhance string stability and response bandwidth, by dealing with the string heterogeneity. A gap management strategy is built on top of the CACC architecture to handle gap setting changes or cut-in/out situations, via a dynamics constrained time gap trajectory planning algorithm. The proposed work has been designed, developed and validated on three different real passenger vehicles on public highways and test tracks, showing the potential of the proposed algorithm to enable robust string stable CACC, despite the different dynamics and powertrains considered.

Ion-Exchange-Induced Transformation and Mechanism of Cooperative Crystal Chemical Adaptation in Sitinakite: Theoretical and Experimental Study

The microporous titanosilicate sitinakite, KNa2Ti4(SiO4)2O5(OH)·4H2O, was first discovered in the Khibiny alkaline massif. This material is also known as IONSIV IE-911 and is considered as one of the most effective sorbents for Cs+ and Sr2+ from water solutions. We investigate a mechanism of cooperative crystal chemical adaptation caused by the incorporation of La3+ ions into sitinakite structure by the combination of theoretical (geometrical–topological analysis, Voronoi migration map calculation, structural complexity calculation) and empirical methods (PXRD, SCXRD, Raman spectroscopy, scanning electron microscopy). The natural crystals of sitinakite (a = 7.8159(2), c = 12.0167(3) Å) were kept in a 1M solution of La(NO3)3 for 24 h. The ordering of La3+ cations in the channels of the ion-exchanged form La3+Ti4(SiO4)2O5(OH)·4H2O (a = 11.0339(10), b = 11.0598(8), c = 11.8430(7) Å), results in the symmetry breaking according to the group–subgroup relation P42/mcm → Cmmm.

Energy–Delay Tradeoff in Adaptive Cooperative Caching for Energy-Harvesting Ultradense Networks

Edge caching in collaborative edge computing (CEC) is a resource-friendly technique to improve energy efficiency and alleviate backhaul link congestion. Caching diverse contents based on the social features among users at energy-harvesting-powered (EH-powered) small base stations can further save on-grid energy, but it may lead to a longer delay to mobile users (MUs). In this article, we focus on an energy–delay tradeoff (EDT) problem in CEC-assisted and EH-powered ultradense networks and propose an EDT-oriented adaptive cooperative caching (EDT-ACC) scheme. We regard delay and energy as two types of cost and introduce a weighted cost function to transform the EDT problem into a cost minimization problem. An alternating optimization based on an improved quantum genetic algorithm (AO-IQGA) is proposed to solve the cost minimization problem. In AO-IQGA, the alternating optimization is utilized to divide the cost minimization problem into two subproblems (adaptive tuning weight subproblem and caching decision subproblem). We improve the quantum genetic algorithm in terms of repairing unfeasible solutions and adaptively updating quantum genes. Numerical results demonstrate the efficiency of the proposed AO-IQGA and illustrate the fundamental tradeoff between delay and energy consumption under different parameters, such as content popularity, storage size, and battery capacity.

Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic

Road traffic crashes are a major safety problem, with one of the leading factors in crashes being human error. Automated and connected vehicles (CAVs) that are equipped with Advanced Driver Assistance Systems (ADAS) are expected to reduce human error. In this paper, the Simulation of Urban MObility (SUMO) traffic simulator is used to investigate how CAVs impact road safety. In order to define the longitudinal behavior of Human Drive Vehicles (HDVs) and CAVs, car-following models, including the Krauss, the Intelligent Driver Model (IDM), and Cooperative Adaptive Cruise Control (CACC) car-following models were used to simulate CAVs. Surrogate safety measures were utilized to analyze CAVs’ safety impact using time-to-collision. Two case studies were evaluated: a signalized grid network that included nine intersections, and a second network consisting of an unsignalized intersection. The results demonstrate that CAVs could potentially reduce the number of conflicts based on each of the car following model simulations and the two case studies. A secondary finding of the research identified additional safety benefits of vehicles equipped with collision avoidance control, through the reduction in rear-end conflicts observed for the CACC car-following model.

Cooperative Adaptive Cruise Control-Based Transit Bus Operation on Signalized Corridors

Cooperative Adaptive Cruise Control-Based Transit Bus Operation on Signalized Corridors

Improving the Safety of Cooperative Adaptive Cruise Control Vehicle Platoons: A Relative Time Headway Policy

Improving the Safety of Cooperative Adaptive Cruise Control Vehicle Platoons: A Relative Time Headway Policy

A Numerical Study on Constant Spacing Policies for Starting Platoons at Oversaturated Intersections

Cooperative Adaptive Cruise Control (CACC) is considered as a key potential enabler to improve driving safety and traffic efficiency. It allows for automated vehicle following using wireless communication in addition to onboard sensors. To achieve string stability in CACC platoons, constant time gap (CTG) spacing policies have prevailed in research; namely, vehicle interspacing grows with the speed. While constant distance gap (CDG) spacing policies provide superior potential to increase traffic capacity than CTG, their major drawbacks are a smaller safety margin at high velocities and that string stability cannot be achieved using a one-vehicle look-ahead communication. In this work, we propose to apply CDG only in a few driving situations, when traffic throughput is of highest importance and safety requirements can be met due to relatively low velocities. As the most relevant situations where CDG could be applied, we identify starting platoons at signalized intersections. With this application scenario we show that applying CDG only in a few specific and crucial situation can have a major impact on traffic efficiency. Specifically, we compare CTG with CDG regarding its potential to increase the capacity of traffic lights. Starting with the elementary situation of single traffic lights we expand our scope to whole traffic networks including several thousand vehicles in simulation. Using real world data to calibrate and validate vehicle dynamics simulation and traffic simulation, the study discusses the most relevant working parameters of CDG, CTG, and the traffic system in which both are applied.

Adaptive Cooperative Tracking and Parameter Estimation of an Uncertain Leader over General Directed Graphs

This paper studies the cooperative tracking problem of heterogeneous Euler-Lagrange systems with an uncertain leader. Different from most existing works, system dynamic knowledge of the leader node is unaccessible to any follower node in our paper. Distributed adaptive observers are designed for all follower nodes, simultaneously estimate the state and parameters of the leader node. The observer design does not rely on the frequency knowledge of the leader node, and the estimation errors are shown to converge to zero exponentially. Moreover, the results are applied to general directed graphs, where the symmetry of Laplacian matrix does not hold. This is due to two newly developed Lyapunov equations, which solely depend on communication network topologies. Interestingly, using these Lyapunov equations, many results of multi-agent systems over undirected graphs can be extended to general directed graphs. Finally, this paper also advances the knowledge base of adaptive control systems by providing a main tool in the analysis of parameter convergence for adaptive observers.

Adaptive Cooperative Tracking Control of Multiple Trains With Uncertain Dynamics and External Disturbances Based on Adjacent Information

Automatic train operation is an important part of the train control system. As train operating intervals continue to shorten and train speeds continue to increase, multiple train cooperative control is currently an important technology to further improve the efficiency of train operation and line passing capacity. However, considering various factors such as the nonlinearity and uncertainty of the train dynamics model and the complexity of the line conditions, this creates even greater demands on the design of the controller. In this study, we propose an adaptive cooperative tracking control method for multiple trains using adjacent information. For the multiple-train coordinated tracking control in the presence of model uncertainties, unknown parameters, and external disturbances, a distributed cooperative control scheme for multiple trains is designed using the displacement, velocity, and acceleration information of adjacent trains, combined with radial basis function neural networks and adaptive methods. A fast high-order sliding mode observer is used to estimate the train velocity and acceleration information. Stability and convergence are proved for single and multiple trains utilizing Lyapunov stability analysis. Simulation examples demonstrate the effectiveness of the proposed controller.

Fixed-Time Prescribed Performance Adaptive Cooperative Fault-Tolerant Tracking Control for Multiagent Systems Under Integrated Constraints

Fixed-Time Prescribed Performance Adaptive Cooperative Fault-Tolerant Tracking Control for Multiagent Systems Under Integrated Constraints

Adapting Time Headway in Cooperative Adaptive Cruise Control to Network Reliability

Wireless network impairment results in adverse effect on string stability of a Cooperative Adaptive Cruise Control (CACC) platoon that relies on the wireless network, causing disturbances in the platoon that may lead to a collision. Previous work in the literature proposed fallback strategies that utilizes on-board sensors to recover in case of wireless network impairment, those methods assumes a fixed time headway value. In this paper, we study the string stability of a one-vehicle look-ahead CACC platoon under different network loss scenarios, and propose to adapt the time headway parameter of the model according to a network reliability metric that we defined based on packet burst loss length to maximize traffic flow efficiency while maintaining a string-stable platoon. Our findings show that careful adjustment of headway value according to the wireless network reliability allows the platoon to maintain string-stable operation while maximizing traffic flow.