The advancement of cooperative autonomous vehicle systems depends heavily on effective coordination between multiple agents, aiming to enhance traffic efficiency, fuel economy, and road safety. Despite these potential benefits, real-world testing of such systems remains a major challenge and is essential for validating control strategies, trajectory modeling methods, and communication robustness across diverse environments. To address this need, we introduce ConvoyNext, a scalable, modular, and extensible platform tailored for the real-world evaluation of cooperative driving behaviors. We demonstrate the capabilities of ConvoyNext through a series of experiments involving convoys of autonomous vehicles navigating complex trajectories. These tests highlight the platform’s robustness across heterogeneous vehicle configurations and its effectiveness in assessing convoy behavior under varying communication conditions, including intentional packet loss. Our results validate ConvoyNext as a comprehensive, open-access testbed for advancing research in cooperative autonomous vehicle systems.

Small-scale autonomous vehicle platforms provide a cost-effective environment for developing and testing advanced driving systems. However, specific configurations within this scale are underrepresented, limiting full awareness of their potential. This paper focuses on a one-sixth-scale setup, offering a high-level overview of its design, hardware and software integration, and typical challenges encountered during development. We discuss methods for addressing mechanical and electronic issues common to this scale and propose guidelines for improving reliability and performance. By sharing these insights, we aim to expand the utility of small-scale vehicles for testing autonomous driving algorithms and to encourage further research in this domain.

Cooperative driving, enabled by communication between automated vehicle systems, promises significant benefits to fuel efficiency, road capacity, and safety over single-vehicle driver assistance systems such as adaptive cruise control (ACC). However, the responsible development and implementation of these algorithms pose substantial challenges due to the need for extensive real-world testing. We address this issue by introducing OpenConvoy, an open and extensible framework designed for the implementation and assessment of cooperative driving policies on physical connected and autonomous vehicles (CAVs). We demonstrate the capabilities of OpenConvoy through a series of experiments on a convoy of multi-scale vehicles controlled by Platooning, showcasing the stability of our system across different vehicle configurations and its ability to effectively measure convoy cohesion across driving scenarios, including varying degrees of communication loss.

In this study, we explore an innovative approach to enhance cooperative driving in vehicle platooning systems through the use of vehicle-to-everything (V2X) communication technologies. As Connected and Autonomous Vehicles (CAVs) integrate into increasingly dense traffic networks, the challenge of efficiently managing communication resources becomes crucial. Our focus is on optimizing communication strategies to support the growing network of interconnected vehicles without compromising traffic safety and efficiency. We introduce a novel control-aware communication framework designed to reduce communication overhead while maintaining essential performance standards in vehicle platoons. This method pivots from traditional periodic communication to more adaptable aperiodic or event-triggered schemes. Additionally, we integrate Model-Based Communication (MBC) to enhance vehicle perception under suboptimal communication conditions. By merging control-aware communication with MBC, our approach effectively controls vehicle platoons, striking a balance between communication resource conservation and control performance. The results show a marked decrease in communication frequency by 47%, with minimal impact on control accuracy, such as less than 1% variation in speed. Extensive simulations validate the effectiveness of our combined approach in managing communication and control in vehicle platoons, offering a promising solution for future cooperative driving systems.

Research in Cooperative Intersection Management (CIM), utilizing Vehicle-to-Everything (V2X) communication among Connected and/or Autonomous Vehicles (CAVs), is crucial for enhancing intersection safety and driving experience. CAVs can transceive basic and/or advanced safety information, thereby improving situational awareness at intersections. The focus of this study is on unsignalized intersections, particularly Stop Controlled-Intersections (SC-Is), where one of the main reasons involving crashes is the ambiguity among CAVs in SC-I crossing priority upon arriving at similar time intervals. Numerous studies have been performed on CIM for unsignalized intersections based on centralized and distributed systems in the presence and absence of Road-Side Unit (RSU), respectively. However, most of these studies are focused towards replacing SC-I where the scheduler provides spatio-temporal or sequence-based reservation to CAVs, or where it controls CAVs via kinematic commands. These methods cause CAVs to arrive at the intersection at non-conflicting times and cross without stopping. This logic is severely limited in real-world mixed traffic comprising human drivers where kinematic commands and other reservations cannot be implemented as intended. Thus, given the existence of SC-Is and mixed traffic, it is significant to develop CIM systems incorporating SC-I rules while assigning crossing priorities and resolving the related ambiguity. In this regard, we propose a distributed Automated Right-of-Way (AROW) algorithm for CIM to assign explicit SC-I crossing turns to CAVs and mitigate hazardous scenarios due to ambiguity towards crossing priority. The algorithm is validated with extensive experiments for its functionality, scalability, and robustness towards CAV non-compliance, and it outperforms the current solutions.

Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles (HVs) is challenging, as HVs continuously update their policies in response to AVs. In order to navigate safely in the presence of complex AV-HV social interactions, the AVs must learn to predict these changes. Humans are capable of navigating such challenging social interaction settings because of their intrinsic knowledge about other agents’ behaviors and use that to forecast what might happen in the future. Inspired by humans, we provide our AVs the capability of anticipating future states and leveraging prediction in a cooperative reinforcement learning (RL) decision-making framework, to improve safety and robustness. In this paper, we propose an integration of two essential and earlier-presented components of AVs: social navigation and prediction. We formulate the AV’s decision-making process as a RL problem and seek to obtain optimal policies that produce socially beneficial results utilizing a prediction-aware planning and social-aware optimization RL framework. We also propose a Hybrid Predictive Network (HPN) that anticipates future observations. The HPN is used in a multi-step prediction chain to compute a window of predicted future observations to be used by the value function network (VFN). Finally, a safe VFN is trained to optimize a social utility using a sequence of previous and predicted observations, and a safety prioritizer is used to leverage the interpretable kinematic predictions to mask the unsafe actions, constraining the RL policy. We compare our prediction-aware AV to state-of-the-art solutions and demonstrate performance improvements in terms of efficiency and safety in multiple simulated scenarios.

Cellular Vehicle-to-Everything (C-V2X) is a frontier in the evolution of distributed communication introduced in 3GPP release 14 towards advanced use cases. While research efforts continue to optimize the accessible bandwidth for transportation ecosystem, analysis of the network from the application layer perspective is essential prior to deployment, as it can help identify potential issues that end-users may encounter in a dynamic road environment. This emphasizes on assessing the network using application-oriented metrics to evaluate its capacity of providing advanced vehicular services with stringent latency and throughput requirements. C-V2X facilitates advanced applications like autonomous driving and on-the-go transaction services that necessitate consecutive exchange of messages. For such services, the network level metrics fails to capture the edge case service quality as they express an average measure of performance. In this paper, we present an application-oriented analysis of a transaction service built on C-V2X protocol. We analyze different design choices that affects quality of service both from network-oriented and user-centric metrics, and highlight issues regarding packet dissemination from infrastructures for vehicle-to-infrastructure (V2I) based service applications. We also present our study on the impact of batching in disseminating acknowledgement packets (ACK) and its consequences on both the service reliability and network congestion. Our results show that time-sensitive and mission-sensitive vehicular applications should aim for a balance between achieving the mission utility in shortest duration possible while maintaining minimal impact on the system-wide stability.

Cellular-V2X (C-V2X) enables communication between vehicles and other transportation entities over the 5.9GHz spectrum. C-V2X utilizes direct communication mode for safety packet broadcasts (through the usage of periodic basic safety messages) while leaving sufficient room in the resource pool for advanced service applications. While many such ITS applications are under development, it is crucial to identify and optimize the relevant network parameters. In this paper, we envision an infrastructure-assisted transaction procedure entirely carried out by C-V2X, and we optimize it in terms of the service parameters. To achieve the service utility of a transaction class, two C-V2X entities require a successive exchange of multiple messages. With this notion, our proposed application prototype can be generalized for any vehicular service to establish connections on-the-fly. We identify suitable activation zones for vehicles and assess their impact on service efficiency. The results show a variety of potential service and parameter settings that can be appropriate for different use-cases, laying the foundation for subsequent studies.

This article analyzes the scheduling protocol for basic safety messages standardized in the SAE J2945/1 and presents large-scale scalability results obtained from a high-fidelity simulation platform. The presented results demonstrate the protocol’s efficacy to address the scalability issues in vehicle-to-vehicle communication. By employing a distributed opportunistic approach, the SAE J2945/1 congestion control algorithm keeps the overall offered channel load within an optimal operating range, while meeting the minimum tracking requirements set forth by upper-layer applications. The scheduling protocol allows transmission of event-triggered and vehicle-dynamics driven messages that further the situational awareness in cooperative vehicle-to-vehicle communications. The validation results are evaluated using position tracking error as the main performance measure, with age of communicated information as the supporting evaluation measure of the congestion control algorithm. In addition, we examine the optimality of the default settings of the SAE J2915/1 congestion control algorithm parameters. Comprehensive analysis and trade-off study of the control parameters reveal some areas of improvement to further the algorithm’s efficacy.

Connected vehicle safety and efficiency applications rely on vehicular wireless networks for information sharing. To study the performance of these networks, and numerous protocols designed for them, it is essential to be able to model the channel behavior. However, due to the complexity of the vehicular environment, such modeling has to rely on data collected from large scale field tests. A majority of data collection campaigns with a large number of vehicles utilize vehicular on-board units (transceivers) to collect data in the form of received signal strength (RSS) values. Such datasets are limited in information since only the RSS values of correctly received packets are recorded, leading to datasets that can be considered as censored; especially where packet losses are high. Therefore, deriving channel models that accurately represent the channel behavior at distances with considerable loss of packets is a challenge. In this paper, we propose Bernoulli Mapping Estimation, a novel approach for the estimation of distributions from censored samples. Additionally, we provide a general method and formulation for modeling the communication channel in vehicular environments, considering receiver characteristics, imperfections, and data sparsity. The proposed methods were applied to datasets collected from highway and intersection environments; and the derived models are shown to be accurate representations of the datasets. It is also shown that even with only a small fraction of the RSS values, the framework is able to produce fading models that are similar in characteristics to field data.

Despite the advances in the autonomous driving domain, autonomous vehicles (AVs) are still inefficient and limited in terms of cooperating with each other or coordinating with vehicles operated by humans. A group of autonomous and human-driven vehicles (HVs) which work together to optimize an altruistic social utility can co-exist seamlessly and assure safety and efficiency on the road. Achieving this mission without explicit coordination among agents is challenging, mainly due to the difficulty of predicting the behavior of humans with heterogeneous preferences in mixed-autonomy environments. Formally, we model an AV’s maneuver planning in mixed-autonomy traffic as a partially-observable stochastic game and attempt to derive optimal policies that lead to socially-desirable outcomes using a multi-agent reinforcement learning framework (MARL), and propose a semi-sequential multi-agent training and policy dissemination algorithm for our MARL problem. We introduce a quantitative representation of the AVs’ social preferences and design a distributed reward structure that induces altruism into their decision-making process. Altruistic AVs are able to form alliances, guide the traffic, and affect the behavior of the HVs to handle competitive driving scenarios. We compare egoistic AVs to our altruistic autonomous agents in a highway merging setting and demonstrate the emerging behaviors that lead to improvement in the number of successful merges and the overall traffic flow and safety.

Widespread adoption of autonomous vehicles will not become a reality until solutions are developed that enable these intelligent agents to co-exist with humans. This includes safely and efficiently interacting with human-driven vehicles, especially in both conflictive and competitive scenarios. We build up on the prior work on socially-aware navigation and borrow the concept of social value orientation from psychology —that formalizes how much importance a person allocates to the welfare of others— in order to induce altruistic behavior in autonomous driving. In contrast with existing works that explicitly model the behavior of human drivers and rely on their expected response to create opportunities for cooperation, our Sympathetic Cooperative Driving (SymCoDrive) paradigm trains altruistic agents that realize safe and smooth traffic flow in competitive driving scenarios only from experiential learning and without any explicit coordination. We demonstrate a significant improvement in both safety and traffic-level metrics as a result of this altruistic behavior and importantly conclude that the level of altruism in agents requires proper tuning as agents that are too altruistic also lead to sub-optimal traffic flow. The code and supplementary material are available at: https://symcodrive.toghi.net/

Cooperative driving, enabled by communication between automated vehicle systems, is expected to significantly contribute to transportation safety and efficiency. Cooperative Adaptive Cruise Control (CACC) and platooning are two of the main cooperative driving applications that are currently under study. These applications offer significant improvements over current advanced driver assistant systems such as adaptive cruise control (ACC). The primary motivation of CACC and Platooning is to reduce traffic congestion and improve traffic flow, traffic throughput, and highway capacity. These applications need an efficient controller to consider the computational cost and ensure driving comfort and high responsiveness. The advantage of Model Predictive Control is that we can realize high control performance since all constrains for these applications can be explicitly dealt with through solving an optimization problem. These applications highly depend on information updates and Communication reliability for their safety and stability purposes. In this paper, we propose a Model Predictive Control (MPC) based approach for CACC and platooning, and examine the impact of communication loss on the performance and robustness of the control scheme. The results show an improvement in response time and string stability, demonstrating the potential of cooperation to attenuate disturbances and improve traffic flow.

The recent advancements in communication and computational systems have led to significant improvement of situational awareness in connected and autonomous vehicles. Computationally efficient neural networks and high speed wireless vehicular networks have been some of the main contributors to this improvement. However, scalability and reliability issues caused by inherent limitations of sensory and communication systems are still challenging problems. In this paper, we aim to mitigate the effects of these limitations by introducing the concept of feature sharing for cooperative object detection (FSCOD). In our proposed approach, a better understanding of the environment is achieved by sharing partially processed data between cooperative vehicles while maintaining a balance between computation and communication load. This approach is different from current methods of map sharing, or sharing of raw data which are not scalable. The performance of the proposed approach is verified through experiments on Volony dataset. It is shown that the proposed approach has significant performance superiority over the conventional single-vehicle object detection approaches.

As Internet of Things (IoT) and Cyber-Physical systems become more ubiquitous in our daily lives, it necessitates the capability to measure the trustworthiness of the aggregate data from such systems to make fair decisions. However, the interpretation of trustworthiness is contextual and varies according to the risk tolerance attitude of the concerned application. In addition, there exist varying levels of uncertainty associated with an evidence upon which a trust model is built. Hence, the data integrity scoring mechanisms require some provisions to adapt to different risk attitudes and uncertainties. In this article, we propose a prospect theoretic framework for data integrity scoring that quantifies the trustworthiness of the collected data from IoT devices in the presence of adversaries who try to manipulate the data. In our proposed method, we consider an imperfect anomaly monitoring mechanism that tracks the transmitted data from each device and classifies the outcome (trustworthiness of data) as not compromised, compromised, or undecided . These outcomes are conceptualized as a multinomial hypothesis of a Bayesian inference model with three parameters. These parameters are then used for calculating a utility value via prospect theory to evaluate the reliability of the aggregate data at an IoT hub. In addition, to take into account different risk attitudes, we propose two types of fusion rule at IoT hub—optimistic and conservative. Furthermore, we put forward asymmetric weighted moving average scheme to measure the trustworthiness of aggregate data in presence of On-Off attacks. The proposed framework is validated using extensive simulation experiments for both uniform and On-Off attacks. We show how trust scores vary under a variety of system factors like attack magnitude and inaccurate detection. In addition, we measure the trustworthiness of the aggregate data using the well-known expected utility theory and compare the results with that obtained by prospect theory. The simulation results reveal that prospect theory quantifies trustworthiness of the aggregate data better than expected utility theory.

A fundamental challenge in autonomous vehicles is adjusting the steering angle at different road conditions. Recent state-of-the-art solutions addressing this challenge include deep learning techniques as they provide end-to-end solution to predict steering angles directly from the raw input images with higher accuracy. Most of these works ignore the temporal dependencies between the image frames. In this paper, we tackle the problem of utilizing multiple sets of images shared between two autonomous vehicles to improve the accuracy of controlling the steering angle by considering the temporal dependencies between the image frames. This problem has not been studied in the literature widely. We present and study a new deep architecture to predict the steering angle automatically by using Long-Short-Term-Memory (LSTM) in our deep architecture. Our deep architecture is an end-to-end network that utilizes CNN, LSTM and fully connected (FC) layers and it uses both present and futures images (shared by a vehicle ahead via Vehicle-to-Vehicle (V2V) communication) as input to control the steering angle. Our model demonstrates the lowest error when compared to the other existing approaches in the literature.

Cellular Vehicle-to-everything (C-V2X) communication has been proposed in the 3rd Generation Partnership Project release 14 standard to address the latency and reliability requirements of cooperative safety applications. Such applications can involve highly congested vehicular scenarios where the network experiences high data loads. Thus, a sophisticated congestion control solution is vital in order to maintain the network performance required for safety-related applications. With the aid of our high-fidelity link-level network simulator, we investigate the feasibility of implementing the distributed congestion control algorithm specified in SAE J2945/1 standard on top of the C-V2X stack. We describe our implementation and evaluate the performance of transmission rate and range control mechanisms using relevant metrics. Additionally, we identify areas for potential design enhancements and further investigation.

By providing information about the objects that are non-line of sight and/or beyond the detection range of the local sensors, inter-vehicle communication compensates for the limitations of vehicle tracking subsystem in automated driving systems that relies on on-board sensing devices. Tracking capability in such systems can further be improved by making optimal use of the communication channel through sharing of locally created map data instead of transmitting only beacon messages. Message length adaptation, together with transmit rate control can address the scalability issue inherent in the vehicular network. The content of the exchanged information is another important aspect that has significant impact on the map accuracy in cooperative driving systems. In this paper, we study different congestion and content control schemes for a communication architecture aimed at map sharing, and evaluate their performance in terms of a situational awareness metric, namely position tracking error. This paper determines that message content should be concentrated on mapped objects that are located farther away from the sender, but near the edge of local sensor range. This paper also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system.

Channel modeling is essential for design and performance evaluation of numerous protocols in vehicular networks. In this paper, we study and provide results for large- and small-scale modeling of communication channel in dense vehicular networks. We first propose an approach to remove the effect of fading on deterministic part of the large-scale model and verify its accuracy using a single transmitter-receiver scenario. Two-ray model is then utilized for path-loss characterization and its parameters are derived from the empirical data based on a newly proposed method. Afterward, we use α -μ distribution to model the fading behavior of vehicular networks for the first time, and validate its precision by Kolmogorov-Smirnov(K-S) goodness-of-fit test. To this end, the significantly better performance of utilizing α - μ distribution over the most adopted fading distribution in the vehicular channels literature, i.e., Nakagami-m, in terms of passing K-S test has been investigated and statistically verified in this paper. A large received signal strength indicator (RSSI) data set from a measurement campaign is used to evaluate our claims. Moreover, the whole model is implemented in a reliable discrete event network simulator which is widely used in the academic and industrial research for network analysis, i.e., network simulator-3 (ns-3), to show the outcome of the proposed model in the presence of upper layer network protocols.

Channel modeling is critical for evaluating various protocols designed for different network layers, as credibility of the corresponding evaluation largely depends on the accuracy of the propagation models. Dynamic vehicular network topology and propagation environment define the communication channel behavior. In this paper, we study the received signal strength (RSS) based channel characterization dataset collected from an urban intersection scenario for Dedicated Short Range Communication (DSRC) in the 5.9 GHz band. We conducted a large-scale RSS measurement campaign in one of the densest traffic environments in the US. The channel characterization data was collected using ten DSRC-enabled vehicles in an Orange County, CA intersection environment. Using the dataset, we study and identify the key aspects of the network topology and physical environment that collectively describe the channel behavior and determine realistic wireless propagation channel models for urban intersections. In particular, we show that the path losses are harsh between vehicles on perpendicular roads. Our results may prove to be instrumental in evaluating vehicleto- vehicle (V2V) safety applications.

Scalability is one of the major issues for real- world Vehicle-to-Vehicle network realization. To tackle this challenge, a stochastic hybrid modeling framework based on a non-parametric Bayesian inference method, i.e., hierarchical Dirichlet process (HDP), is investigated in this paper. This framework is able to jointly model driver/vehicle behavior through forecasting the vehicle dynamical time-series. This modeling framework could be merged with the notion of model-based information networking, which is recently proposed in the vehicular literature, to overcome the scalability challenges in dense vehicular networks via broadcasting the behavioral models instead of raw information dissemination. This modeling approach has been applied on several scenarios from the realistic Safety Pilot Model Deployment (SPMD) driving data set and the results show a higher performance of this model in comparison with the zero-hold method as the baseline.

Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semi-automated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical stochastic hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a stochastic object. Non-parametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from the NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.

Forward Collision Warning (FCW) is a promising Advanced Driver Assistance System (ADAS) to mitigate rear-end collisions. The deterministic FCW approaches may occasionally lead to the issuance of annoying false warnings, as they cannot be individualized for different drivers. This application oversight, which may cause the driver to deactivate the system, has been tackled with some adaptive methods. However, driver distraction, which is one of the most influential driver-specific factors on FCW warnings acceptability, has not been considered yet and is analyzed in this paper for the first time. Specifically, the adaptive FCW method proposed in this paper generates the warnings by continuously comparing Time Headway with a flexible threshold. The core of the proposed threshold updating mechanism is a real-time monitoring of the driver reactions against the previously generated warnings using the available indicators such as braking history. This method considers the driver distraction in parallel to fine-tune the calculated threshold in accordance with driver cognitive state. In order to incorporate the driver distraction in the system framework, a learning-based approach is designed which continuously estimates driver distraction by the virtue of different available Controller Area Network (CAN) bus time series, such as throttle pedal position, velocity, acceleration, and yaw rate. Neural network, as a widely adopted classification method, is nominated to detect driver distraction. The framework performance is evaluated over two realistic driving datasets. An approximately 80% false warning reduction is observed in analyzed safe scenarios, while no critical warning is missed in the dangerous ones.

By providing information about the objects that are non-line of sight and/or beyond the detection range of the local sensors, inter-vehicle communication compensates for the limitations of vehicle tracking subsystem in automated driving systems that relies on on-board sensing devices. Tracking capability in such systems can further be improved by making optimal use of the communication channel through sharing of locally created map data instead of transmitting only beacon messages. While map data are collected from both sensor and communicated information, exchanging a subset of the local map requires some sort of content control scheme in place to ensure that useful information is being exchanged among vehicles that would enhance tracking accuracy. This paper investigates how information sharing based on proximity of mapped objects impact the position tracking capability. It also compares deterministic and probabilistic variants of the distance based content control approaches. Preliminary results show that exchanging information about objects located near the edge of sensor range increases the tracking performance. It is also shown that the probabilistic approaches have higher mapping accuracy than their deterministic counterparts.

Understanding the surrounding environment including both still and moving objects is crucial to the design and optimization of intelligent vehicles. Knowledge about the vehicle environment could facilitate reliable detection of moving objects, especially irregular events (e.g., pedestrians crossing the road, vehicles making sudden lane changes,) for the purpose of avoiding collisions. Inspired by the analogy between point cloud and video data, we propose to formulate a problem of reconstructing the vehicle environment (e.g., terrains and buildings) from a sequence of point cloud sets. Built upon existing point cloud registration tool such as iterated closest point (ICP), we have developed an expectation-maximization (EM)-ICP technique that can automatically mosaic multiple point cloud sets into a larger one characterizing the still environment surrounding the vehicle. Moreover, we propose to address the issue of irregularity detection from the extracted moving objects. Our experimental results have shown successful reconstruction of a variety of challenging vehicle environments (including rural and urban, road and intersection, etc.) and simultaneous tracking/segmentation of multiple moving objects.

Vehicle-to-vehicle communications (V2V) has attracted the interest of research community in recent years because of its potential to increase safety and efficiency of roads and make connected-vehicles a reality. DSRC based vehicular networks are already standardized by IEEE 802.11 which supports communication among vehicles and road-side infrastructures. Different safety applications need to be tested and validated for numerous road scenarios to show the benefit of DSRC based networks. Simulation tools come in handy where a sizable number of tests are required to substantiate the performance of safety applications. ns-3 is a network simulator that offers a rich set of libraries for modeling mobility, communication channel, and many other network components. Although ns-3 is designed for simulating Wi-Fi networks, some enhancements are still required to make the simulations more realistic for vehicular networks. Large-scale field tests reveal that some of the substantial details are missing in existing ns-3 implementation of physical (PHY) and medium access control (MAC) layers that play a major role in defining the network behavior. This paper is a step toward an exhaustive and higher fidelity simulation of IEEE 802.11. We validate our proposed receiver model by comparing its simulation results with large-scale field data.

Advanced communication technology is the enabling factor for distributed sensing and control in smart grid. The performance of communication has a significant effect on the performance of the controllers that manage a power system. This effect is more profound when transient level behavior and critical applications are concerned. In these cases, an important issue is to design control-aware communication strategies for utilizing available communication technologies. Such strategies should describe what needs to be communicated when and between which nodes. In this paper, an “on-demand” strategy is presented that describes how communication subsystems should be configured, almost agnostically to the underlying technologies, to achieve significant performance improvement for the application. The on-demand method relies on the concept of error-dependent communication for tracking dynamical systems over communication networks. The paper also introduces the design of an embedded communication simulator integrated with PSCAD for cosimulation of communication strategies/protocols and power system components.

Cooperative vehicle safety (CVS) systems operate based on broadcast of vehicle position and safety information to neighboring cars. The communication medium of CVS is a vehicular ad-hoc network. One of the main challenges in large scale deployment of CVS systems is the issue of scalability. To address the scalability problem, several congestion control methods have been proposed and are currently under field study. These algorithms adapt transmission rate and power based on network measures such as channel busy ratio. We examine two such algorithms and study their dynamic behavior in time and space to evaluate stability (in time) and fairness (in space) properties of these algorithms. We present stability conditions and evaluate stability and fairness of the algorithms through simulation experiments. Results show that there is a trade-off between fast convergence, temporal stability and spatial fairness. The proper ranges of parameters for achieving stability are presented for the discussed algorithms. Stability is verified for all typical highway density cases for static traffic as well as real scenarios. Fairness is shown to be naturally achieved for some algorithms and its analysis is under study in another work of us. Under the same conditions other algorithms may have problem to maintain fairness in space. We have shown that this can be resolved by a distributed measurement of CBR and is verified.

In this paper, we describe VCAST, an algorithm for obtaining individual vehicle location as well as aggregate traffic information over an infrastructure-less multi-hop wireless vehicular network. VCAST can be used to improve safety against collisions, inform about approaching emergency vehicles and lane merging vehicles (that may even be beyond a single hop communication range) and to enable dynamic routing and navigation techniques by providing aggregate traffic information in an extended neighborhood. To ensure scalability in forwarding information over multiple hops, we exploit a notion of distance sensitivity in information propagation, by which traffic information is propagated at a rate that decreases linearly with distance from the source. By doing so, traffic information can be obtained with a staleness, which is a measure of error in the traffic information, that is bounded by O(dh2) where dhis the communication hop distance from the source of the information. At the same time, the required communication rate per unit time at each node only depends on the radius of the region in terms of communication hops, and not on the vehicular density or the number of vehicles in the region. VCAST does not require any special hardware or modification to vehicular transmission standards; instead it can simply piggyback on basic Here I am communication for vehicular networks.

In this paper, we describe a framework and system architecture for sharing of information between vehicles and infrastructure with the aim of increasing intersection safety. Emerging intersection safety applications are based on location sensing and wireless communication. We show that these applications can only be realized if vehicles and intersection can both participate and share their information and intelligence. The framework to share information is based on wireless communications, usually in DSRC mode. We first present a system architecture that uses a networking gateway in order to allow incorporation of wireless communication modes other than DSRC. Then the overhead of this extra component is examined via experiments and shown not to have a significant effect on the performance of the system. Experimental results from the implementation of the proposed architecture is reported and analyzed. For system scalability we also conducted simulations to examine different design choices for the rate of information exchange and operation of the components of the architecture. The results show that even modest cooperation of vehicle and infrastructure can be adequate to enable safety critical applications.

Scalability is one of the main challenges of cooperative vehicle safety (CVS) systems. In this paper, we describe a demonstration of the scalability solution that we have developed for CVS. The solution is an adaptive communication scheme for safety messages. The demo comprises of a demonstration of the rate control algorithm using recorded vehicle trajectories, and a live demonstration of the power control scheme using emulated interference. We show that the proposed algorithms are able to deliver considerably better performance than the baseline solution for CVS. The demonstration will use several DSRC units as well as visualization and simulation software for visualizing the network dynamics.

We propose an intervehicle communication framework for the cooperative active safety system (CASS) whose operation is based on the dissemination of each vehicle's state information through a wireless network. Such a CASS requires each subject vehicle to be aware of its surroundings, particularly of the motion and position of other vehicles in its proximity. In this paper, we assume that all vehicles are equipped with onboard communication devices. In such situations, the wireless channel is simultaneously shared by a large number of vehicles, and one of the most difficult challenges in designing CASS is to maintain real-time tracking accuracy of neighboring vehicles while avoiding network congestion and failure. To address this issue, we analyze the problem that multiple scalar linear time-invariant dynamical systems track each other over a multiaccess channel, and then, we propose a rate adaptation algorithm to distributively control the self-information broadcast behavior of each vehicle. The proposed algorithm uses a closed-loop control concept and accounts for the lossy channel. Simulation results show that, if the message generation rate is dynamically adjusted in an on-demand fashion, more accurate and robust tracking performance can be achieved under various traffic conditions.

Cooperative vehicle safety (CVS) systems rely on vehicular ad-hoc networks operating in broadcast mode to deliver vehicle tracking and safety information to neighboring cars. This information is used to enable collision avoidance and warning systems. One of the main challenges of the eventual large scale deployment of such systems is network congestion, which could critically degrade the quality of a CVS system. In this paper, we present a method for congestion monitoring and control based on limited feedback from the network. We study the relationship between channel occupancy, as a readily available feedback measure, controllable network parameters, and network performance. We describe a performance measure relevant to CVS systems and present a congestion control method, based on channel occupancy measurements, that robustly maintains the system performance near optimal operation points. We examine the convergence properties of the congestion control algorithm and provide guidelines for the design of such systems based on network and traffic density conditions. Through simulation experiments we show significant gains in performance when closed loop congestion control methods are applied.

Successful deployment of Broadband Wireless Access Networks such as WiMAX (IEEE 802.16) will be contingent on provisions for supporting multimedia traffic. In this paper, we review the quality of service features of access networks such as the 802.16 standard, and identify algorithms and schemes that are needed for supporting multimedia traffic in such networks. The 802.16 standard only specifies the features that should be implemented and leaves the design of a quality of service solution to developers. This includes the design of a mandatory scheduling framework. We present a comprehensive multimedia support framework based on the standard features. The framework specifies the architectures for the base station and the subscriber station, and contributes a number of algorithms for different service provisioning objectives. We use the concept of virtual packets to provide fair packet based centralized scheduling of uplink and downlink packets. The presented solution also provides algorithms for temporal and throughput fair scheduling in multirate physical layer of the 802.16 networks. An important part of the presented design is a multi-class fair scheduling scheme which is proposed for providing better delay performance for real time applications, while maintaining slightly longer term fairness.

In this paper a new method is presented for finding data clusters centroids. This method, called Force, is based on the concepts of electrostatic fields in which the centroids are positioned at locations where an electrostatic equilibrium or balance could be achieved. After determining the centroids locations, criteria such as minimum distance to centroid can be used for clustering data points. The performance of the proposed method is compared against the k-means algorithm through simulation experiments. Experimental results show that the Force algorithm does not suffer from problems associated with k-means, such as sensitivity to noise and initial selection of centroids, and tendency to converge to poor local optimum. In fact, we show that this algorithm always converges to global equilibrium points, regardless of the initial guesses, and even in presence of high levels of noise.

Vehicular ad-hoc networks (VANETs) play a critical role in enabling highway active safety applications such as collision warning and vehicle tracking. The most pressing challenge in enabling such applications is to maximize the amount of disseminated vehicle state information while avoiding network congestion. In this paper, we explore the structure of VANET tracking problem and propose an adaptive rate control algorithm based on network condition and tracking error. Proposed algorithm uses a closed-loop control concept and accounts for the lossy channel. This algorithm is shown to achieve better tracking performance than existing solutions. We first analyze the algorithm behavior in small scale Matlab simulations with bounded dynamical systems and then evaluate its performance using OPNET simulations with realistic vehicle trajectories from a microscopic traffic simulator (SHIFT).

We propose an efficient error protection technique for data-partitioned H.264 video in a capacity constrained network. Our scheme maximizes video quality by choosing the optimal point in the application layer and medium access control (MAC) layer redundancy. We have shown that, in a capacity constrained network and highly lossy environment, neither forward error correction (FEC) nor retransmissions alone can result in optimum performance. Instead, it is the combination of these two techniques that effectively reduces the overall loss.

In vehicular ad-hoc networks, status update messages are used to disseminate vehicle state information so that each vehicle can track movements of its neighbors. In this paper, we focus on the aggregated power level from this kind of active safety messages and its impact on DSRC channel quality. With macroscopic model and LWR (Lighthill-Whitham-Richards) PDE, we show how the probability distribution of aggregated power level propagates along time and space. Based on our analysis, several rate-power control ideas for status update messages are discussed. In addition, microscopic traffic/network simulation results show that our proposed rate-power control algorithm can reduce channel interference and thus enhance tracking accuracy.

The success of VANET safety mechanisms, e.g. pre-crash warning and collaborative collision avoidance, relies on good proximity-awareness of each subject vehicle. Prior analysis shows that a self-information dissemination control on each car, which adapts communication rate based on estimation error and channel congestion, can achieve higher tracking accuracy than others in a multi-access channel. In this paper, we extensively evaluate the robustness of three communication policies under different highway traffics. The worst case scenario, in terms of tracking accuracy, is identified to be a bidirectional highway with one direction congested and another direction being free flowing. Our proposed algorithms are shown to be robust and achieve much better tracking performance than 100-ms beaconing suggested by.

In this paper, we address the problem of efficiently allocating network resources to support multiple scalable video streams over a constrained wireless channel. We present a resource allocation framework that jointly optimizes the operation of the link adaptation scheme in the physical layer (PHY), and that of a traffic control module in the network or medium access control (MAC) layer in multirate wireless networks, while satisfying bandwidth/capacity constraints. Multirate networks, such as IEEE 802.16 or IEEE 802.11, adjust the PHY coding and modulation schemes to maintain the reliability of transmission under varying channel conditions. Higher reliability is achieved at the cost of reduced PHY bit-rate which in turn necessitates a reduction in video stream bit-rates. The rate reduction for scalable video is implemented using a traffic control module. Conventional solutions operate unaware of the importance and loss tolerance of data and drop the higher layers of scalable video altogether. In this paper, we consider medium grain scalable (MGS) extension of H.264/AVC video and develop new rate and distortion models that characterize the coded bitstream. Performance evaluations show that our proposed framework results in significant gains over existing schemes in terms of average video PSNR that can reach 3 dB in some cases for different channel SNRs and different bandwidth budgets.

Transmission of video over a wireless network is a challenging task due to the error prone characteristics of the wireless link. This application requires the video encoding process to offer error resilience features in order to deal with the high packet loss probability of wireless networks. H.264, the newest and most efficient video compression standard, offers several error resiliency features. H.264 also offers network support by utilizing a network abstraction layer (NAL) and encapsulating individually decodable video slices in NAL units. The size of the video slices, or packets, has a significant effect on the overall performance of a video application in wireless networks. In this paper we study the possibility of determining the optimal packet size of encoded video that minimizes the packet loss rate and maximizes the video quality during transmission over 3G networks. Through simulation experiments, we show that smaller slices are more favorable, but encoding inefficiency and increase in overhead set a limit on the minimum acceptable packet size.

Existing medium access control (MAC) schemes for wireless local area networks (WLAN) have been shown to lack scalability in crowded networks, and efficiency in supporting heterogeneous traffic types. These issues are mostly due to the use of random multiple access techniques in the MAC layer. The design of these techniques is highly linked to the choice of the underlying physical (PHY) layer technology. The advent of new PHY schemes that are based on orthogonal frequency division multiple access (OFDMA) provides new opportunities for devising more efficient MAC protocols. We propose a new adaptive MAC design based on OFDMA technology. The design uses OFDMA to reduce collision during transmission request phases, and makes channel access more predictable. To improve efficiency, we combine the OFDMA access with a carrier sense multiple access (CSMA) scheme. Data transmission opportunities are assigned through an access point that can schedule traffic streams in both time and frequency (subchannels) domains. We demonstrate the effectiveness of the proposed MAC and compare it to existing mechanisms through simulation experiments and by deriving an analytical model for the operation of the MAC in saturation mode.

In this paper, we propose a cross-layer optimization scheme for delivery of scalable video over variable bit-rate wireless networks, in particular 802.11 based wireless local area networks (WLAN). For scalable video streaming applications, the conventional solution to reduced throughput due to channel distortions is to reduce the video bitrate by dropping the higher enhancement layers of the scalable video. We show that video quality can be improved, without adding to traffic load, when the WLAN link adaptation scheme uses a temporal fairness criterion along with scalable video distortion estimates to adjust its physical (PHY) layer modulation and coding parameters used for delivering each video layer. We formulate the problem as an optimization problem for assigning different PHY modes to different layers of scalable video under temporal fairness constrains; the solution to this problem provides a set of PHY configuration parameters that achieve the highest possible video quality while meeting the admission control constraints. Performance evaluations demonstrate the effectiveness of our method and the accuracy of the models.

In this paper, we propose a cross-layer optimization scheme for delivery of scalable video over multirate wireless networks, in particular the popular 802.11 based wireless local area network (WLAN). The 802.11 based networks use a link adaptation mechanism in the physical layer (PHY) to maintain the reliability of transmission under varying channel conditions. When channel condition worsens, the reliability is maintained by employing more robust modulation and coding schemes, at the cost of reduced PHY bit rate. The reduced bit rate will result in lower available throughput for applications. For scalable video streaming applications, the conventional solution to this problem is to reduce the video bit rate by dropping the higher enhancement layers of the scalable video. We show in this article that the video quality can be improved, if the link adaptation scheme uses more intelligent reliability criteria and adjusts the PHY parameters used for delivering each video layer, according to the relative importance of that layer. Our scheme achieves better video quality without increasing the traffic load of the WLAN. For this purpose we present temporal fairness constraints and formulate an optimization problem for assigning different PHY modes to different layers of scalable video; the solution to this problem provides a set of PHY configuration parameters that achieve the highest possible video quality while meeting the admission control constraints in the network. Performance evaluations demonstrate that our method outperforms the existing mechanisms.

Supporting video applications over 802.11 wireless local area networks is a challenging task due to the constant fluctuations in channel error rates and the inefficiency of the MAC layer. New video compression technologies, such as H.264, provide a network adaptation layer for adapting the output of the video encoder to the characteristics of the underlying transport network. In this article we demonstrate that it is possible to improve the performance of H.264 video applications over 802.11 WLANs through a cross-layer design that optimizes the encoded H.264 packet sizes. We propose the use of aggregation and fragmentation mechanisms to create the optimal frame lengths. We also investigate several application layer error

Supporting Multimedia applications, such as video, over 802.11 wireless local area networks (WLAN) is a challenging task due to the inefficiency of the 802.11 MAC layer and the constant fluctuations in channel error rates. Therefore, specific measures must be taken in both application and delivery layers in order to achieve efficient and satisfactory quality for multimedia applications. Advanced video compression technologies, such as H.264, provide new data partitioning error resiliency feature that allows delivery of the video data in different streams with different levels of importance. There are several possible schemes for mapping these streams to the services of the IEEE 802.1 le MAC. In this article we examine the existing schemes and propose new mechanisms that improve the performance of the video communications system. We propose to use scheduled access schemes in MAC, along with aggregation of packets in the application layer to achieve the best results. We evaluate our proposed mechanisms using simulation experiments.

Several enhancements to the Medium Access Control (MAC) layer of the IEEE 802.11 standard have been recommended in the new 802.11e standard. One main enhancement is the possibility to send bursts of frames during a limited duration called Transmission Opportunity (TXOP). Similar MAC efficiency enhancements such as Frame Aggregation have also been proposed for the upcoming 802.11n standard. We analyze the application of this feature to the existing 802.11 MAC and evaluate its performance under different network conditions. The frame bursting feature can increase the total capacity of an 802.11 network by reducing the contention and collision for bursty traffic sources. We extend the established 802.11 analytical models to include the frame bursting feature. Through simulation experiments we analyze the delay performance of the network under different frame bursting options and show that while in general frame bursting is useful and can increase the system capacity, it might cause excessive unfairness in certain cases. Using the findings of this article we present guidelines for implementing fair adaptive algorithms that use TXOP.

The widespread deployment of IEEE 802.11 based wireless local area networks (WLAN) has made broadband access a reality for many consumers. As a result, supporting a wide range of applications, in particular networked multimedia applications, has become of increasing importance. Since specific delay and bandwidth requirements of multimedia applications cannot be fulfilled by the current IEEE 802.11-based WLANs, new enhancements are being introduced to the medium access control (MAC) layer of the 802.11 standard under the framework of the IEEE 802.11e. Nevertheless, the 802.11e only provides the means of supporting quality of service (QoS) in the MAC layer and does not mandate a final solution for QoS issues. We present a QoS solution that employs the controlled access features of the 802.11e to provide per-session guaranteed QoS. Our design comprises of a scheduler that assigns guaranteed service times to individual sessions using a fair scheduling algorithm. Through analysis and experiments we prove the fairness of the algorithm and show that the proposed solution outperforms other methods that are contention or priority based

Supporting real-time multimedia applications in any network requires scheduling techniques that can provide guaranteed delay and/or bandwidth to media streams. Coordinating access to the wireless medium in an IEEE 802.11 wireless LAN is different from network layer scheduling since it requires scheduling traffic flows in a distributed manner. We introduce a new scheduling framework called multiple access hybrid scheduling (MAHS) that utilizes the concept of virtual packet and emulates stations packets in the access point in order to centralize the scheduling process. This way the main scheduling function is performed solely in the access point thus enabling the use of conventional schedulers to for scheduling both uplink and downlink packets (hybrid scheduling). We deploy a modified version of weighted fair queuing (WFQ) as this internal scheduler. Performance evaluation and analysis of the proposed framework is described in this article.