Cooperative Awareness Research Articles

Exploring fisherfolk community readiness for cooperative engagement in the Global South

Cooperatives have become significant contributors to the realization of the Sustainable Development Goals No. 1: No Poverty. Transitioning associations to cooperatives is crucial for promoting sustainable economic development, empowering communities, and enhancing collective well-being. This research assessed the readiness of Small-Scale Fisheries (SSF) communities in the Global South to form a cooperative. This research employed an exploratory research approach in six coastal Barangays of Batad, situated in the 5th District of Iloilo Province. The findings indicated that respondents have a slight level of awareness with regard to the advantages and economic advantages associated with becoming part of a cooperative. On the other hand, there was a clear difference in members’ perceptions of the benefits and financial returns that comes with belonging to a cooperative. According to the study, females are more likely to support the association’s move towards a cooperative structure, especially younger individuals. The main issue highlighted was the lack of skilled officers and inadequate resources and training for association members. A lecture on Cooperative Awareness and capability trainings on financial management, bookkeeping, and credit management should be organized in order to increase associations readiness to be a cooperative.

Exploring fisherfolk community readiness for cooperative engagement in the Global South

Cooperatives have become significant contributors to the realization of the Sustainable Development Goals No. 1: No Poverty. Transitioning associations to cooperatives is crucial for promoting sustainable economic development, empowering communities, and enhancing collective well-being. This research assessed the readiness of Small-Scale Fisheries (SSF) communities in the Global South to form a cooperative. This research employed an exploratory research approach in six coastal Barangays of Batad, situated in the 5th District of Iloilo Province. The findings indicated that respondents have a slight level of awareness with regard to the advantages and economic advantages associated with becoming part of a cooperative. On the other hand, there was a clear difference in members’ perceptions of the benefits and financial returns that comes with belonging to a cooperative. According to the study, females are more likely to support the association’s move towards a cooperative structure, especially younger individuals. The main issue highlighted was the lack of skilled officers and inadequate resources and training for association members. A lecture on Cooperative Awareness and capability trainings on financial management, bookkeeping, and credit management should be organized in order to increase associations readiness to be a cooperative.

CLlight: Enhancing representation of multi-agent reinforcement learning with contrastive learning for cooperative traffic signal control

Multi-agent reinforcement learning has shown great potential for coordinating multi-intersection traffic signals due to its powerful adaptive capabilities, treating each intersection as an agent. However, in the real world, different intersections possess differentiating characteristics such as unique vehicle distributions and traffic patterns. Most existing methods directly add neighboring intersection states to local intersections and optimize the cooperative policy network based on synthesized global features. This indirect optimization approach makes it difficult to thoroughly explore the mutual interactions among different intersection agents, preventing agents from truly learning features with cooperative awareness. To resolve these challenges, we introduce contrastive learning as representation task to the multi-intersection traffic signal control approach named CLlight for two-stage policy network updating. In the first stage, we utilize policy-based or actor-critic-based reinforcement learning methods such as A2C, SAC, and PPO to train policy networks with certain representational capabilities. In the second stage, by extracting pre- and post-masked features and reconstructing the post-masked features, the agents are encouraged to learn the similarities and differences between different intersection policies, which in turn enhances the cooperative and individual representation capabilities of the policy network. To the best of our knowledge, this is the first application of contrastive learning in the field of traffic signal control. Experimental results demonstrate, compared to other state-of-the-art traffic signal control methods, superior average travel time and average waiting time performance under various scenarios, tested on synthetic and real-world datasets.

Enhancing the Minimum Awareness Failure Distance in V2X Communications: A Deep Reinforcement Learning Approach.

Vehicle-to-everything (V2X) communication is pivotal in enhancing cooperative awareness in vehicular networks. Typically, awareness is viewed as a vehicle's ability to perceive and share real-time kinematic information. We present a novel definition of awareness in V2X communications, conceptualizing it as a multi-faceted concept involving vehicle detection, tracking, and maintaining their safety distances. To enhance this awareness, we propose a deep reinforcement learning framework for the joint control of beacon rate and transmit power (DRL-JCBRTP). Our DRL-JCBRTP framework integrates LSTM-based actor networks and MLP-based critic networks within the Soft Actor-Critic (SAC) algorithm to effectively learn optimal policies. Leveraging local state information, the DRL-JCBRTP scheme uses an innovative reward function to increase the minimum awareness failure distance. Our SLMLab-Gym-VEINS simulations show that the DRL-JCBRTP scheme outperforms existing beaconing schemes in minimizing awareness failure probability and maximizing awareness distance, ultimately improving driving safety.

A Cascaded Multi-Agent Reinforcement Learning-Based Resource Allocation for Cellular-V2X Vehicular Platooning Networks.

The platooning of cars and trucks is a pertinent approach for autonomous driving due to the effective utilization of roadways. The decreased gas consumption levels are an added merit owing to sustainability. Conventional platooning depended on Dedicated Short-Range Communication (DSRC)-based vehicle-to-vehicle communications. The computations were executed by the platoon members with their constrained capabilities. The advent of 5G has favored Intelligent Transportation Systems (ITS) to adopt Multi-access Edge Computing (MEC) in platooning paradigms by offloading the computational tasks to the edge server. In this research, vital parameters in vehicular platooning systems, viz. latency-sensitive radio resource management schemes, and Age of Information (AoI) are investigated. In addition, the delivery rates of Cooperative Awareness Messages (CAM) that ensure expeditious reception of safety-critical messages at the roadside units (RSU) are also examined. However, for latency-sensitive applications like vehicular networks, it is essential to address multiple and correlated objectives. To solve such objectives effectively and simultaneously, the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework necessitates a better and more sophisticated model to enhance its ability. In this paper, a novel Cascaded MADDPG framework, CMADDPG, is proposed to train cascaded target critics, which aims at achieving expected rewards through the collaborative conduct of agents. The estimation bias phenomenon, which hinders a system's overall performance, is vividly circumvented in this cascaded algorithm. Eventually, experimental analysis also demonstrates the potential of the proposed algorithm by evaluating the convergence factor, which stabilizes quickly with minimum distortions, and reliable CAM message dissemination with 99% probability. The average AoI quantity is maintained within the 5-10 ms range, guaranteeing better QoS. This technique has proven its robustness in decentralized resource allocation against channel uncertainties caused by higher mobility in the environment. Most importantly, the performance of the proposed algorithm remains unaffected by increasing platoon size and leading channel uncertainties.

Security-Minded Verification of Cooperative Awareness Messages

Security-Minded Verification of Cooperative Awareness Messages

Evaluation of Radio Access Protocols for V2X in 6G Scenario-Based Models

The expansion of mobile connectivity with the arrival of 6G paves the way for the new Internet of Verticals (6G-IoV), benefiting autonomous driving. This article highlights the importance of vehicle-to-everything (V2X) and vehicle-to-vehicle (V2V) communication in improving road safety. Current technologies such as IEEE 802.11p and LTE-V2X are being improved, while new radio access technologies promise more reliable, lower-latency communications. Moreover, 3GPP is developing NR-V2X to improve the performance of communications between vehicles, while IEEE proposes the 802.11bd protocol, aiming for the greater interoperability and detection of transmissions between vehicles. Both new protocols are being developed and improved to make autonomous driving more efficient. This study analyzes and compares the performance of the protocols mentioned, namely 802.11p, 802.11bd, LTE-V2X, and NR-V2X. The contribution of this study is to identify the most suitable protocol that meets the requirements of V2V communications in autonomous driving. The relevance of V2V communication has driven intense research in the scientific community. Among the various applications of V2V communication are Cooperative Awareness, V2V Unicast Exchange, and V2V Decentralized Environmental Notification, among others. To this end, the performance of the Link Layer of these protocols is evaluated and compared. Based on the analysis of the results, it can be concluded that NR-V2X outperforms IEEE 802.11bd in terms of transmission latency (L) and data rate (DR). In terms of the packet error rate (PER), it is shown that both LTE-V2X and NR-V2X exhibit a lower PER compared to IEEE protocols, especially as the distance between the vehicles increases. This advantage becomes even more significant in scenarios with greater congestion and network interference.

Optimizing Hybrid V2X Communication: An Intelligent Technology Selection Algorithm Using 5G, C-V2X PC5 and DSRC

Cooperative communications advancements in Vehicular-to-Everything (V2X) are bolstering the autonomous driving paradigm. V2X nodes are connected through communication technology, such as a short-range communication mode (Dedicated Short Range Communication (DSRC) and Cellular-V2X) or a long-range communication mode (Uu). Conventional vehicular networks employ static wireless vehicular communication technology without considering the traffic load on any individual V2X communication technology and the traffic dynamics in the vicinity of the V2X node, and are hence inefficient. In this study, we investigate hybrid V2X communication and propose an autonomous and intelligent technology selection algorithm using a decision tree. The algorithm uses the information from the received Cooperative Intelligent Transport Systems (C-ITS) Cooperative Awareness Messages (CAMs) to collect statistics such as inter vehicular distance, one-way end-to-end latency and CAM density. These statistics are then used as input for the decision tree for selecting the appropriate technology (DSRC, C-V2X PC5 or 5G) for the subsequent scheduled C-ITS message transmission. The assessment of the intelligent hybrid V2X algorithm’s performance in our V2X test setup demonstrates enhancements in one-way end-to-end latency, reliability, and packet delivery rate when contrasted with the conventional utilization of static technology.

Data accuracy in Vehicle-to-X cooperative awareness messages: An experimental study for the first commercial deployment of C-ITS in Europe

Cooperative Intelligent Transportation Systems have achieved a mature technology stage and are in an early phase of mass deployment in Europe. Relying on Vehicle-to-X communication, these systems were primarily developed to improve traffic safety, efficiency, and driving comfort. However, they also offer great opportunities for other use cases. One of them is forensic accident analysis, where the received data provide details about the status of other traffic participants, give insights into the accident scenario, and therefore help in understanding accident causes. A high accuracy of the sent information is essential: For safety use cases, such as traffic jam warning, a poor accuracy of the data may result in wrong driver information, undermine the usability of the system and even create new safety risks. For accident analysis, a low accuracy may prevent the correct reconstruction of an accident. This paper presents an experimental study of the first generation of Cooperative Intelligent Transportation Systems in Europe. The results indicate a high accuracy for most of the data fields in the Vehicle-to-X messages, namely speed, acceleration, heading and yaw rate information, which meet the accuracy requirements for safety use cases and accident analysis. In contrast, the position data, which are also carried in the messages, have larger errors. Specifically, we observed that the lateral position still has an acceptable accuracy. The error of the longitudinal position is larger and may compromise safety use cases with high accuracy requirements. Even with limited accuracy, the data provide a high value for the accident analysis. Since we also found that the accuracy of the data increases for newer vehicle models, we presume that Vehicle-to-X data have the potential for exact accident reconstruction.

LAMP: A latency-aware MAC protocol for joint scheduling of CAM and DENM traffic over 5G-NR sidelink

Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) are two types of messages used in Intelligent Transport Systems (ITS) for Vehicle-to-Everything (V2X) communication. CAMs are used to periodically exchange the current state of a vehicle with its nearby vehicles and the DENMs are used to aperiodically provide critical and time-sensitive information about current environmental conditions to other vehicles and infrastructure entities. While considerable research has been conducted on the efficient scheduling of CAM and DENM separately or at the network level coexistence, no work has been done to integrate the scheduling of both message types at the granularity of the per-vehicle level. The scheduling of CAM is commonly done through Semi-Persistent Scheduling (SPS). However, no equivalent scheduling algorithm has been developed for the aperiodic and variable-sized DENMs. As a result, there is a need for novel mechanisms that can efficiently prioritize and schedule mixed traffic of CAM and DENM messages. This paper introduces a Quality of Service (QoS) scheduler called LAMP for segregating traffic in the Radio Link Control (RLC) layer for joint sidelink scheduling of CAM and DENM at the vehicular level. The LAMP scheduler is aided with a special resource selection and reservation scheme for mixed traffic scenarios. The groundwork involves an experimental analysis in Network Simulator-3 (NS-3) that uses the New Radio (NR) Vehicle-to-Everything (V2X) module in Mode-2. The simulation findings demonstrate that LAMP could significantly reduce the end-to-end latency of DENM by 89.36% and CAM by 40.2% while also increasing the packet reception rate by 12.1% and 9.74% for CAM and DENM with repetitions, respectively.

Intelligent Extra Resource Allocation for Cooperative Awareness Message Broadcasting in Cellular-V2X Networks

Intelligent Extra Resource Allocation for Cooperative Awareness Message Broadcasting in Cellular-V2X Networks

Performance comparison based on priority between 802.11p and C-V2X

With the increasing number of vehicles, the issue of traffic safety has become more and more prominent. There is a growing demand for real-time vehicle monitoring and vehicle-to-vehicle communication, which has led to the rapid development of connected car technology. Currently, the two mainstream wireless access technologies that support vehicle-to-everything (V2X) communication are Dedicated Short-Range Communications (DSRC) and Cellular-V2X (C-V2X) or LTE-V2X. This paper aims to compare the performance of these two technologies by studying two types of information, Cooperative Awareness Messages (CAM) and Decentralized Environmental Notification Messages (DENM), as well as the prioritization defined for C-V2X. Additionally, this paper compares the prioritization of 802.11p with different priority levels of C-V2X. Through simulation experiments, it is ultimately proven that 802.11p outperforms C-V2X in terms of latency but is inferior to C-V2X in terms of collision probability.

Predicting CAM generation times through machine learning for cellular V2X communication

In the narrow Intelligent Transportation System (ITS) band, avoiding wireless channel congestion is essential. Reporting vehicle kinematics in the Cooperative Awareness Message (CAM) only when there are notable changes in vehicle dynamics is a standardized approach to reducing bandwidth usage of periodic CAM messages that are exchanged between vehicles, and is called the CAM generation rule. However, in cellular vehicle-to-everything (V2X) communication, aperiodicity due to frequent omissions of periodic CAM raises problems of resource waste and instability in resource scheduling. The problem can be solved by reserving a resource only for actual CAM transmission times in the future. This article demonstrates that a neural network-based scheme can predict the next CAM generation times at an average accuracy of over 94%, which can be utilized for resource reservation under the CAM generation rule.

Decentralized piggybacking-based dissemination of Cooperative Awareness Messages in vehicular ad-hoc networks

Vehicle-to-Vehicle (V2V) communication has shown great promise to improve road safety and traffic efficiency via the periodic broadcasting of Cooperative Awareness Messages (CAMs), in which traffic information could be exchanged among adjacent vehicles. Since each CAM contains local-observed traffic information such as geolocation, speed, and safety alerts, frequent CAM losses will decrease the reliability on avoiding crashes and injuries on the road. Existing solutions tend to recover the lost CAMs through either source-based re-transmission or neighbor-based forwarding, which may suffer from significant performance degradation due to the fast movement of vehicles and high dynamic network topology. In this paper, we develop a decentralized cooperative broadcasting protocol for CAM dissemination, where each vehicle can piggyback some received CAMs in its routine broadcasting to help others recover their lost CAMs. To promote cooperation of CAM piggybacking, a new data structure called bitmap is introduced to record the packet receiving status at each vehicle, based on which time slots could be allocated to vehicles with low data collisions, lost CAMs could be detected only using each vehicle’s local information, and the qualities of wireless links could be estimated dynamically. Then, we propose two piggybacking schemes, Benefit-based Piggybacking (BP) and Suggestion-based Piggybacking (SP), aiming to maximize the average CAM delivery ratio. We evaluate the proposed schemes in simulations with realistic vehicle mobility traces and channel models. Simulation results show that our bitmap-based lost CAM detection has superior performance than traditional request-based solutions. In comparison with forwarding-based schemes, our schemes can achieve much higher CAM broadcast reliability with short time delay and small communication overhead.

Antenna Combiner for Periodic Broadcast V2V Communication Under Relaxed Worst-Case Propagation

The performance of a previously developed analog combining network (ACN) of phase shifters for periodic broadcast vehicle-to-vehicle (V2V) communication is investigated. The original ACN was designed to maximize the sum of signal-to-noise ratios (SNRs) for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$</tex-math> </inline-formula> consecutive cooperative awareness messages (CAMs). The design was based on the assumption of a dominant propagation path with an angle of arrival (AOA) that is constant for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$</tex-math> </inline-formula> messages. In this work, we relax this assumption by allowing the AOA and path-loss (PL) of the dominant path to be time-variant. Assuming a highway scenario with a line of sight (LOS) propagation between vehicles, we use affine approximations to model the time variation of different path quantities, including the PL, the relative distance-dependent phase shift between antennas, and the AOA-dependent far-field function of the antennas. By leveraging these approximations, we analytically derive the ACN sum-SNR as each one of these quantities varies over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$</tex-math> </inline-formula> CAMs. Moreover, we suggest a design rule for a phase slope that is robust against time variation of the dominant path and optimal under time-invariant conditions. Finally, we validate this design rule using numerical computations and an example of vehicular communication antenna elements.

AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning

This paper investigates the problem of age of information (AoI) aware radio resource management for a platooning system. Multiple autonomous platoons exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the cooperative awareness messages (CAMs) to their followers while ensuring timely delivery of safety-critical messages to the Road-Side Unit (RSU). To lower the computational load at the RSU and cope with the challenges of dynamic channel conditions, we exploit a distributed resource allocation framework based on multi-agent reinforcement learning (MARL), where each platoon leader (PL) acts as an agent and interacts with the environment to learn its optimal policy. Motivated by the existing literature in RL, we propose two novel MARL frameworks based on the multi-agent deep deterministic policy gradient (MADDPG), named Modified MADDPG, and Modified MADDPG with task decomposition. Both algorithms train two critics with the following goals: A global critic which estimates the global expected reward and motivates the agents toward a cooperating behavior and an exclusive local critic for each agent that estimates the local individual reward. Furthermore, based on the tasks each agent has to accomplish, in the second algorithm, the holistic individual reward of each agent is decomposed into multiple sub-reward functions where task-wise value functions are learned separately. Numerical results indicate our proposed algorithms' effectiveness compared with other contemporary RL frameworks, e.g., federated reinforcement learning (FRL) in terms of AoI performance and CAM message transmission probability.

Traffic-sensitive speed advisory system based on Lagrangian traffic indicators

Can we elaborate a traffic-sensitive eco-driving or GLOSA (Green Light Optimal Speed Advice) strategy with a frugal amount of data when approaching an intersection? Here is the purpose of this work, which aims to adapt a traffic-theory-based estimation of the expected queue-length within mixed traffic (Connected and non-Connected Vehicles) in the vicinity of a signalized intersection. While the expected queue-length methodology was developed recently and fits natively with Eulerian traffic indicators resulting from loop sensors or cameras, this paper adapts such a methodology to Lagrangian indicators as the traces produced by any Connected Vehicle, including Floating Car or Probe Data. The main interest of the methodology lies in the frugal amount of data and expenses required to perform the traffic-sensitive speed-advisory at any connected road intersection. The full methodology is developed to extend the SPAT messages broadcast to end-users and take advantage of the Cooperative Awareness Messages (CAM) acting as GPS traces for Connected Vehicles. Contrary to Eulerian-based indicators, no supplementary and costly investment is required to collect the input data and compute the queue-length estimation. However, applying strategies based on Lagrangian indicators will affect the direct traffic observation through these indicators. Therefore, it requires to develop an assessment and predictive framework to estimate the traffic conditions. The performance of the introduced methodology is compared to alternative methods, among other Eulerian-based methods. It results from the analysis that the introduced approach performs almost as well as the ones based on exhaustive, but costly data collections.

CAMELAMA: Cooperative awareness and spaceborne monitoring enabled by location-assisted medium access

In beaconing systems such as AIS or ADS-B, used by ships and aircraft, each node periodically broadcasts its navigational state to nearby nodes to increase traffic safety. Nowadays, these beacons are also used as a source for satellite-based global traffic monitoring. This dual use imposes competing needs on the medium access control protocol as the size of the collision domains varies by a large factor between the use cases. Even the subproblem of solely avoiding terrestrial nodes’ packets to collide from the perspective of a receiving satellite is not trivial to solve if the satellite’s collision domain spans multiple hops in the terrestrial network. Based on ideas of the LAMA protocol, we propose CAMELAMA, a novel contention-free medium access control protocol for position awareness beaconing. Our low-overhead approach uses neither forwarding of node state nor handshakes but only the navigational data that is shared between the terrestrial nodes anyway. CAMELAMA provides local cooperative awareness while at the same time desynchronizing transmissions within a satellite’s collision domain even in terrestrially disconnected topologies. In a simulation-based performance evaluation we find that CAMELAMA outperforms SO-TDMA (the MAC protocol of AIS) and also scales better with respect to high node densities.

Deep RNN Based Prediction of Driver’s Intended Movements at Intersection Using Cooperative Awareness Messages

This paper presents an early prediction framework to classify drivers’ intended intersection movements in a connected vehicle environment. Intersections are considered accident blackspots with major traffic violations that cause property damage, injuries and fatalities. An accurate perception of drivers’ intended movements at intersections is required for advanced red-light (ARLW) or turning warnings for vulnerable road users (TWVR). Early prediction of intersection movement and adequate warning assistance will ensure road users’ safety at the intersection. In this study, we adopted recurrent neural networks (RNN): long short-term memory (LSTM) and gated recurrent units (GRU) networks to predict driver intended movements at intersections using the vehicle kinematics extracted from the Cooperative Awareness Messages (CAMs). We used naturalistic driving data of the Ipswich Connected Vehicle Pilot (ICVP) project, Queensland, which was collected from 351 participants who drove their connected vehicles during the pilot period. The pilot study installed roadside equipment at 29 signalised intersections to enable the Cooperative Intelligent Transportation System (C-ITS) use cases. Vehicle speed, speed limit, longitudinal acceleration, lateral acceleration, and yaw rate are used as predictors and monitored in 100-millisecond intervals for 1s to 4s at different warning distances from the stop line. Separate prediction models are trained based on different monitoring windows. Furthermore, drivers’ intended intersection movements are predicted at two individual intersections to evaluate intersection-specific prediction performance and are found with improved prediction accuracy than overall prediction models trained with all 29 intersections data. Overall prediction models are useful for some intersections which lack available data for individual intersection-based prediction.

Streaming-Based Anomaly Detection in ITS Messages

Intelligent transportation systems (ITS) enhance safety, comfort, transport efficiency, and environmental conservation by allowing vehicles to communicate wirelessly with other vehicles and road infrastructure. Cooperative awareness messages (CAMs) contain information about vehicles status, which can reveal road anomalies. Knowing the location, time, and frequency of these anomalies is valuable to road users and road authorities, and timely detection is critical for emergency response teams, resulting in improved efficiency in rescue operations. An enhanced locally selective combination in parallel outlier ensembles (ELSCP) technique is proposed for data stream anomaly detection. A data-driven approach is considered with the objective of detecting anomalies on the fly from CAMs using unsupervised detection approaches. Based on the experiments carried out, we note that ELSCP outperforms other techniques, with 3.64 % and 9.83 % better performance than the second-best technique, LSCP, on AUC-ROC and AUCPR, respectively. Based on our findings, ELSCP can effectively detect anomalies in CAMs.