Cooperative Awareness Messages Research Articles

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

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.

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.

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.

Numerical Approximation of 5G-V2V Physical Sidelink Shared Channel (PSSCH) Capacity Limit using M/G/1 Queuing and Newton–Raphson Method

3GPP 5G V2X technology promised some advanced vehicle safety driving use cases in the future. Vehicle to vehicle communications (V2V) is the pivotal enabler in 5G-V2X by allowing the exchange of Cooperative Awareness Message (CAM) over the Physical Sidelink Shared Channel (PSSCH). In this paper, we modeled the performance of the 5G V2V PSSCH capacity limits based on different CAM payload sizes and vehicle densities using M/G/1 queuing theory and Newton Raphson (N-R) method. Results proved that 5G V2V cell capacity demands is in the order of multiple Gbps. PSSCH message should be kept below 1500 bytes to ensure moderate capacity demands below 1 Gbps limit. For any given practical 5G V2V system vehicle densities, size of CAM data payload and generation times will influence capacity limits. System capacity increase with increasing PSSCH message sizes and number of vehicles arriving into the system, with PSSCH message sizes being the more dominant contributor. Average capacity growth is larger in the 1000 to 1500 bytes region of about 130% as compared to higher PSSCH message size regions. The results ultimately presents important understanding on the relation between different CAM payload sizes and the resultant PSSCH message sizes, and their impact on total system capacities.

User-Driven Relay Beamforming for mmWave Massive Analog-Relay MIMO

Sixth-generation mobile communication (6G) aims to further improve capacity and reliability by controlling the radio propagation environment. Millimeter wave (mmWave) high-frequency band communication offers large bandwidth at the cost of high attenuation, even for smaller distances. Due to this, fewer multiple input multiple outputs (MIMO) multiplexing is possible at the base station (BS). Distributed analog relay nodes with beamforming capability improve the received power and MIMO multiplexing of mmWave communication. Due to limited signal processing, the analog relay node cannot perform beam search and tracking using these mmWave reference signals. The beam search and tracking are possible at BS or user equipment at the cost of increased control overhead. To reduce this overhead and provide relay-based 6G communication, we propose user-driven relay beamforming methods which can obtain the benefits of a massive analog relay MIMO. Assuming vehicular-to-everything (V2X) as a 6G application, we considered a relay-beam control method that uses the user information (location, velocity, acceleration, and direction of the terminal) contained in intelligent transport systems (ITS) messages called Cooperative Awareness Message (CAM). Simulation results show that the proposed method significantly reduces the overhead and the obtains benefits of the massive analog-relay MIMO. Furthermore, the accuracy of CAM's location information, the control period, and the effects of UE mobility are evaluated and presented. The results also show that the proposed method can work effectively in future V2X applications.

Cooperative Awareness Messages’ Generation Frequencies, Trigger Distributions, and Pseudonym Changes of First Commercially Deployed Vehicles in Real Operating Scenarios

Cooperative Awareness Messages’ Generation Frequencies, Trigger Distributions, and Pseudonym Changes of First Commercially Deployed Vehicles in Real Operating Scenarios

Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs

Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques.

Mobility Sensing and V2X Communication for Emergency Services

The warning of traveling emergency response vehicles (ERVs) is performed through the help of both sound and lights when the ERV is approaching. This is not an ideal situation for, at least, two reasons: the information is received only when the ERV is very close to other vehicles; and the other vehicles cannot understand where exactly the ERV is, and which action is the best according to its location and direction. With the help of vehicular communications, this paper proposes a different approach for an ERV to be autonomously and preemptively detected. Based on the collected data through vehicle communication and sensors, primarily through Cooperative Awareness Messages (CAMs) and Radio Detection And Ranging (RADAR) data, a prediction of the ERV future location is performed in real-time, and warning messages are disseminated to vehicles just before the ERV arrival. Real-world evaluation tests, considering different warning dissemination scenarios, show that the warning messages lost during the process is significantly reduced from almost 80% down to 22%, especially when infrastructure-to-vehicle and vehicle-to-vehicle communications are used together. Moreover, 80% of the total warning messages are delivered in less than 100 ms.

Efficient and Privacy-Preserving Certificate Activation for V2X Pseudonym Certificate Revocation

Vehicle to everything (V2X) technology allows the broader development of driving safety, efficiency, and comfort. Because the vehicles can quickly send and receive frequent messages from other vehicles and nearby devices, e.g., cooperative awareness message applications on the intelligent transport system (ITS), V2X requires a good security and privacy protection system to make the messages reliable for the ITS requirements. The existing standards developed in the US and Europe use many short valid period pseudonym certificates to meet the security and privacy requirements. However, this method has difficulty ensuring that revoked pseudonym certificates are treated as revoked by any vehicles because distributing revocation information on a wireless vehicular network with intermittent and rapidly changing topology is demanding. A promising approach to solving this problem is the periodic activation of released pseudonym certificates. Initially, it releases all required pseudonym certificates for a certain period to the vehicle, and pseudonym certificates can be used only after receiving an activation code. Such activation-code-based schemes have a common problem in the inefficient use of network resources between the road-side unit (RSU) and vehicles. This paper proposes an efficient and privacy-preserving activation code distribution strategy solving the problem. By adopting the unicast distribution model of modified activation code for pseudonym certificate (ACPC), our scheme can obtain benefits of efficient activation code distribution. The proposed scheme provides small communication resource usage in the V2X network with various channel options for delivering activation codes in a privacy preserved manner.

Dynamic resource scheduling for real-time group broadcasting in 6G cellular vehicular networks

Vehicle-to-Vehicle (V2V) messaging is an indispensable component of connected autonomous vehicle systems. The 3rd Generation Partnership Project (3GPP) is designed to support V2V communication without any infrastructure using sensing-based Semi-Persistent Scheduling (SPS) in order to avoid resource collisions for Cooperative Awareness Messages (CAMs) transmission. However, the legacy system suffers from significant collisions in a traffic congestion environment and resource waste in light traffic conditions. To address these problems, in this paper we propose a novel dynamic resource scheduling algorithm for real-time group broadcasting, inspired by the Lyapunov optimization framework, where the optimization objective is to minimize time-average failure probability subject to queue stability. Through extensive simulations, we show that the proposed scheduling algorithm outperforms the standard in both traffic jams and leisurely road environments.