A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks

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

A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

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).Comment: 10 pages, Submitted as a journal paper at T-I

Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application

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

Machine Learning-Driven Structure Prediction for Iron Hydrides

We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a versatile machine-learned interatomic potential for iron hydride via a neural network using an iterative training process to explore its energy landscape under different pressures. To evaluate the accuracy and comprehend the intricacies of the PES, we conducted comprehensive crystal structure predictions using our neural network-based potential paired with the minima hopping approach. The predictions spanned pressures ranging from ambient to 100 GPa. Our results reproduce the experimentally verified global minimum structures such as \textit{dhcp}, \textit{hcp}, and \textit{fcc}, corroborating previous findings. Furthermore, our in-depth exploration of the iron hydride PES at different pressures has revealed complex alterations and stacking faults in these phases, leading to the identification of several new low-enthalpy structures. This investigation has not only confirmed the presence of regions of established FeH configurations but has also highlighted the efficacy of using data-driven, extensive structure prediction methods to uncover the multifaceted PES of materials

STRATIGRAPHY OF THE LOWER OLIGOCENE NUMMULITIC LIMESTONES, NORTH OF SONQOR (NW IRAN)

The lower Oligocene hyaline and porcellaneous larger foraminifera of a carbonate platform setting, north of Sonqor, were studied for high-resolution biostratigraphy in the context of European standard zonation (Shallow Benthic Zones). According to the geological map of Kermanshah, these beds were previously ascribed to the Miocene. The identified larger foraminifera include Nummulites fichteli Michelotti, Nummulites vascus Joly & Leymerie, Operculina complanata (Defrance), Asterigerina rotula (Kaufmann), Planorbulina bronnimanni Bignot & Decrouez, Discogypsina discus (Goës), Gypsina mastelensis Bursch, Halkyardia maxima Cimerman, Stomatorbina concentrica (Parker & Jones), Praerhapydionina delicata Henson, Penarchaias glynnjonesi (Henson), Austrotrillina aff. paucialveolata Grimsdale, and Haddonia heissigi Hagn, associated with the coralline alga Subterraniphyllum thomasii Elliott. The foraminiferal association characterises the SBZ 21 Zone (early Rupelian)

Serological survey of human Toxoplasma gondii infection in northern and central regions of Iran

Introduction: Toxoplasma gondii is an important zoonotic protozoan parasite that can infect man and animals. The pathogen can infect the fetus by congenital transmission during pregnancy. The aim of this study was to investigate T. gondii infection in people referred to health care centers in northern and central regions of Iran. Materials and methods: Serum samples from 712 individuals in Mazandaran, Isfahan and ChaharmahalvaBakhtiari provinces, Iran, were examined for the levels of anti-T. Gondii IgG by ELISA. Prevalence of T. gondii infection in respect of gender and age was analyzed. Results: The overall anti-T. gondii IgG prevalence in the study population was 72.05%. In Mazandaran, Isfahan and ChaharmahalvaBakhtiari provinces,in male population respectively 87.6, 41.46 and 61.81% and in female population respectively 89.31, 47.61 and 64.44% were sero-positive with anti-T. gondii IgG. Sero-prevalance of anti-T.gondii IgG in the females was higher than in the males in the northern and central regions of Iran. Discussion and conclusion:The present study demonstrates high prevalence of Toxoplasma infection in northern and central regions of Iran and a higher prevalence of T. gondii infection was observed in females. Significant difference in infection rate between individuals living in northern and central areas in Iran was found (p <0.05), which indicated that T. gondii infection is dependent on living places. Deeper investigations for the potential risk factors that threat the Iranian populations, especially female are recommended