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We derive optimal control policies for a Connected Automated Vehicle (CAV) cooperating with neighboring CAVs in order to implement a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. For the first phase, we optimize the maneuver time subject to safety constraints and subsequently minimize the associated surrogate energy consumption of all cooperating vehicles in this maneuver. For the second phase, we jointly optimize time and energy approximation and provide three different solution methods including a real-time approach based on Control Barrier Functions (CBFs). We prove structural properties of the optimal policies which simplify the solution derivations and, in the case of the longitudinal maneuver, lead to analytical optimal control expressions. The solutions, when they exist, are guaranteed to satisfy safety constraints for all vehicles involved in the maneuver. Simulation results where the controllers are implemented show their effectiveness in terms of significant performance improvements compared to maneuvers performed by human-driven vehicles.

From the perspective of an individual vehicle, the prediction of a “slow-down” or shockwave event on a freeway can help the driver reduce potential collision risks, enhance the driving experience, and reduce the cost of energy consumption and vehicle maintenance. From the perspective of traffic management, shockwave prediction may help regulate traffic flow effectively and allow for the response to (non-recurrent) incidents in a timely manner. In this paper, two real-time prediction algorithms are proposed and investigated, which are based on the high-resolution information provided from a set of connected vehicles within the communication range of the host vehicle. Both methods are able to predict the “slow-down” event under high traffic density at 3.51 seconds (on average) earlier than its occurrence. Both algorithm performances degrade with the decrease of the traffic density and penetration rate of the connected vehicles.

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.

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.

The performance of cooperative vehicular applications is tightly dependent on the reliability of the underneath Vehicle-to-Everything (V2X) communication technology. V2X standards, such as Dedicated Short-Range Communications (DSRC) and Cellular-V2X (C-V2X), which are passing their research phase before being mandated in the US, are supposed to serve as reliable circulatory systems for the time-critical information in vehicular networks; however, they are still heavily suffering from scalability issues in real traffic scenarios. The technology-agnostic notion of Model-Based Communications (MBC) has been proposed in our previous works as a promising paradigm to address the scalability issue and its performance, while acquiring different modeling strategies, has been vastly studied. In this work, the modeling capabilities of a powerful non-parametric Bayesian inference scheme, i.e., Gaussian Processes (GPs), is investigated within the MBC context with more details. Our observations reveal an important potential strength of GP-based MBC scheme, i.e., its capability of accurately modeling different driving behavioral patterns by utilizing only a limited size GP kernel bank. This interesting aspect of integrating GP inference with MBC framework, which has been verified in this work using realistic driving data sets, introduces this architecture as a strong and appealing candidate to address the scalability challenge. The results confirm that our proposed approach over-performs the state of the art research in terms of the required communication rate and GP kernel bank size., Accepted in IEEE VNC 2019

In this work, we introduce a stochastic prediction method which can be utilized in applications such as cooperative adaptive cruise control (CACC) to predict interfering vehicles’ movements. One of the main criteria in the design of automated vehicle systems is their robustness against the disturbances resulted from the non-homogeneity of the vehicular environment. The non-homogeneity is mainly due to the human-driven and automated/autonomous vehicles co-existence. It is therefore imperative for the automated applications to be designed with the capability of handling the uncertain behaviors of human-driven vehicles in a robust manner. This paper presents a method for vehicle movements time-series forecasting using a powerful non-parametric Bayesian inference method, namely Gaussian Processes. The proposed methodology is evaluated using realistic vehicle trajectory data from NGSIM dataset and is shown to provide more accurate results compared to baseline methods that use constant velocity coasting.

Scalable communication is of utmost importance for reliable dissemination of time-sensitive information in cooperative vehicular ad-hoc networks (VANETs), which is, in turn, an essential prerequisite for the proper operation of the critical cooperative safety applications. The model-based communication (MBC) is a recently-explored scalability solution proposed in the literature, which has shown a promising potential to reduce the channel congestion to a great extent. In this work, based on the MBC notion, a technology-agnostic hybrid model selection policy for Vehicle-to-Everything (V2X) communication is proposed which benefits from the characteristics of the non-parametric Bayesian inference techniques, specifically Gaussian Processes. The results show the effectiveness of the proposed communication architecture on both reducing the required message exchange rate and increasing the remote agent tracking precision., Accepted for Oral Presentation at the 13th IEEE Systems Conference (SysCon 2019)

While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this perspective. Leveraging the existing V2V platforms, we propose a new framework using a DSRC-enabled smartphone to extend safety benefits to VRUs. The interoperability of applications between vehicles and portable DSRC enabled devices is achieved through the SAE J2735 Personal Safety Message (PSM). However, considering the fact that VRU movement dynamics, response times, and crash scenarios are fundamentally different from vehicles, a specific framework should be designed for VRU safety applications to study their performance. In this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios. The details of our VRU safety module, including target classification and collision detection algorithms, are explained next. Furthermore, we propose and evaluate a mitigating solution for congestion and power consumption issues in such systems. Finally, the whole system is implemented and analyzed for realistic crash scenarios.

Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory prediction problem needs to be deeply investigated in order to come up with an end to end framework with enough precision required by the safety applications' controllers. This problem has been tackled in the literature using different methods. However, machine learning, which is a promising and emerging field with remarkable potential for time series prediction, has not been explored enough for this purpose. In this paper, a two-layer neural network-based system is developed which predicts the future values of vehicle parameters, such as velocity, acceleration, and yaw rate, in the first layer and then predicts the two-dimensional, i.e. longitudinal and lateral, trajectory points based on the first layer's outputs. The performance of the proposed framework has been evaluated in realistic cut-in scenarios from Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable improvement in the prediction accuracy in comparison with the kinematics model which is the dominant employed model by the automotive industry. Both ideal and nonideal communication circumstances have been investigated for our system evaluation. For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency.

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 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., This work has been accepted in 2018 IEEE Connected and Automated Vehicles Symposium (CAVS 2018)

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD)., 10 pages, Submitted as a journal paper at T-IV

Vehicle-to-everything (V2X) communication enables vehicles, roadside vulnerable users, and infrastructure facilities to communicate in an ad-hoc fashion. Cellular V2X (C-V2X), which was introduced in the 3rd generation partnership project (3GPP) release 14 standard, has recently received significant attention due to its perceived ability to address the scalability and reliability requirements of vehicular safety applications. In this paper, we provide a comprehensive study of the resource allocation of the C-V2X multiple access mechanism for high-density vehicular networks, as it can strongly impact the key performance indicators such as latency and packet delivery rate. Phenomena that can affect the communication performance are investigated and a detailed analysis of the cases that can cause possible performance degradation or system limitations, is provided. The results indicate that a unified system configuration may be necessary for all vehicles, as it is mandated for IEEE 802.11p, in order to obtain the optimum performance. In the end, we show the inter-dependence of different parameters on the resource allocation procedure with the aid of our high fidelity simulator., Comment: Accepted to the IEEE Vehicular Networking Conference (VNC 2018), Taipei, Taiwan

Backbone wireless mesh networking concept has been developed in order to provide higher bandwidth, better QoS (less delay and jitter) and larger coverage range compared with previous network types. These factors should be balanced in an appropriate way in order to satisfy mentioned goals. In this paper an optimization framework has been proposed in order to maximize throughput according to upper bound allowable delay. Problem has been studied in two cases with fixed and variable duration transmission intervals and results have been compared. Also secondary interference avoidance constraints based on IEEE 802.11 MAC protocol model have been discussed.