Performance of CAM based Safety Applications using ITS-G5A MAC in High Dense Scenarios

ETSI ITS-G5 is the current vehicle-to-vehicle communication technology in Europe, which will be standardized by ETSI TC ITS. It is based on IEEE 802.11p and therefore uses a CSMA/CA scheme for Media Access Control (MAC). In this paper we analyze the performance of CAM based safety applications using the ETSI ITS-G5 MAC technology in a challenging scenario with respect to MAC issues: A suitable freeway segment with 6 lanes in each direction. The freeway scenario is thoroughly modeled and implemented in the well known ns-3 simulation environment. Based on this model, the paper shows the performance of CAM based safety applications under MAC challenging conditions. Therefore we provide a set of simulation results resting upon a particular performance metric which incorporates the key requirements of safety applications. Finally we analyze two concrete example scenarios to make a point how reliable CAM based safety applications are in high dense traffic scenarios

Patient Safety Applications for Improving Health Care Quality

Patient safety is the fundamental thing that needs to be taken care of by medical staff when giving healthcare to patient especially in the Emergency Installation. PKU Muhammadiyah Public Hospital at Bantul has implemented Patient safety well in providing services for patients. Expected to be a reference for other hospitals to implement Patient safety as well as efforts to improve the quality of health services, but do not close the possibility there were still some deficiencies that could be made a suggestion for the PKU Muhammadiyah Bantul Hospital. There for researchers interested to review more detail how application of Patient safetyin Emergency Installation PKU Muhammadiyah Bantul Public Hospital in 2014. This research was qualitative observational study, using research subjects: The Head of the medical service, 1 Officer of Emergency Installation, the head of disaster management, 1 medical doctor, 1 nurse, and 4 patients that were taken with inclusion criteria: getting inpatient class III Hospitals in Yogyakarta, have inpatient more than 1 day. All the input items on the application of patient safety which include facilities, equipment, drugs, procedures, and activities of officers at Emergency Installation at PKU Muhammadiyah Bantul Hospital, as well as the application process is in compliance with the Guidelines of Observation Guidelines Patient Safety 2008 Survey and Guidelines for Accreditation of Hospital Emergency Services Specific Guidelines (revised edition 2007), ACT No. 44 Hospital in 2009, and patient safety procedures 2008. Patient perceived output includes five dimensions of quality (Tangible, Reliable, Responsiveness, Assurance and Empathy) all have the same perception of good and satisfying. Application on standard input, process and output is suit with Observation Guidelines for Patient Safety 2008 Survey and Guidelines for Accreditation of Hospital Emergency Services Specific Guidelines (revised edition 2007), ACT No. 44 Hospital in 2009, and patient safety procedures 2008

Adaptive message rate control of infrastructured DSRC vehicle networks for coexisting road safety and non-safety applications

Intelligent transport system (ITS) has large potentials on road safety applications as well as nonsafety applications. One of the big challenges for ITS is on the reliable and cost-effective vehicle communications due to the large quantity of vehicles, high mobility, and bursty traffic from the safety and non-safety applications. In this paper, we investigate the use of dedicated short-range communications (DSRC) for coexisting safety and non-safety applications over infrastructured vehicle networks. The main objective of this work is to improve the scalability of communications for vehicles networks, ensure QoS for safety applications, and leave as much as possible bandwidth for non-safety applications. A two-level adaptive control scheme is proposed to find appropriate message rate and control channel interval for safety applications. Simulation results demonstrated that this adaptive method outperforms the fixed control method under varying number of vehicles

Micromobility safety applications using AI

In recent years, population overcrowding in large cities has generated serious problems for urban transport, encouraging the use of new means of transportation such as micro-mobility vehicles. This thesis explores the possibilities of using computer vision techniques for the detection of damages in bike lanes, thus reinforcing the safety of this type of vehicle. A damage detector is developed using a convolutional neural network. The project also provides a database of damages present in the bike lanes of the city of Barcelona.En los últimos años, la masificación de la población en las grandes ciudades ha generado graves problemas para el transporte urbano, fomentando el uso de nuevos medios de transporte como es el caso de los vehículos de micromobilidad. Esta tesis explora las posibilidades del uso de técnicas de visión por ordenador para la detección de daños en los carriles bici, reforzando de esta forma la seguridad de este tipo de vehículos. Se desarrolla un detector de daños usando una red neuronal convolucional. El proyecto también aporta una base de datos de daños presentes en los carriles bici de la ciudad de Barcelona.En els darrers anys, la massificació de la població a les grans ciutats ha generat greus problemes per al transport urbà, encoratjant l'ús de nous mitjans de transport com és el cas dels vehicles de micromobilitat. Aquesta tesi explora les possibilitats de les tècniques de visió per ordinador per la detecció de danys als carrils bici, reforçant d'aquesta manera la seguretat per aquest tipus de vehicles. Es desenvolupa un detector de danys utilitzant una xarxa neuronal convolucional. El projecte també aporta una base de dades de danys presents en els carrils bici de la ciutat de Barcelona

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

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