The field of automated vehicle technology is developing rapidly developing. While it is likely to be many years before self-driving cars are commercially viable and used in a wide range of conditions by the general public, technological advances are speeding along the automated technology continuum towards this destination. Automated vehicle technologies troth with significant social benefits such as reduced injuries and deaths, increased road efficiency, mobility. Automated vehicles can improve traffic safety, balance traffic flows, maximize road usage by offering driver warnings and/or assuming vehicle control in dangerous situations, as well as provide motorists with the best end-to-end transportation experience and reduce emissions, which are the most important goals of modern smart traffic control infrastructures. Exchanging data and integration of such systems with Vehicle-to-Vehicle (V2V) may be a keystone to successful readying of vehicular ad-hoc networks (VANETs) and will simply be the following step of this evolution, with dynamic period of time data exchange between all the players of the traffic dominant system and fostering cooperative urban quality. One of the applications of this concept is to provide vehicles and roads with the ability to make road time more enjoyable and also to make roads safer. These applications are typical examples of what an Intelligent Transportation System (ITS) is called, whose objective is to improve security by using new information and communication technologies (NTIC). In this paper, we will focus on the study of the main component in ITS systems and present a review of the major V2V benefits related to driver safety by focusing primarily on the recent developments of these systems.

A brain tumor is a very common and devastating malignant tumor that leads to a shorter lifespan if not detected early enough. Brain tumor classification is a critical step after the tumor has been identified to create an effective treatment plan. This study aims to investigate the three deep learning tools, VGG-16 ResNet50 and AlexNet in order to detect brain tumor using MRI images. The results performance are then evaluated and compared using accuracy, precision and recall criteria. The dataset used contained 155 MRI images which are images with tumors, and 98 of them are non-tumors. The AlexNet model perform extremely well on the dataset with 96.10% accuracy, while VGG-16 achieved 94.16% and ResNet-50 achieved 91.56%. These accuracies positively impact the early detection of tumors before the tumor causes physical side effects such as paralysis and other disabilities.

Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios.

This project is aimed to develop an indoor locating system (ILS) using Bluetooth low energy (BLE) that able to track personal asset in indoor venues, in order to aid a user to trace for lost or misplaced items where the GPS signal strength is uncovered. The system design provides the user with the Internet of Things (IoT) proof-of-concept (PoC) for indoor locating based systems using Bluetooth connectivity topology. Furthermore, the performance of the system is evaluated in term of signal speed, energy consumption, and sufficient distance of deploying both transmitter and receiver. For the system development, ESP32 WiFi and BLE supported modules are used as the front-end support devices to receive nearby reporting Bluetooth Beacons signal, while a local computer runs as backend computing server to allow trilateration processing based on the reported Bluetooth RSSI values. MQTT that support IoT technology is installed on the local computer with web socket enable-feature allows the plugin to run on the internet browser. Besides that, React Application is used to create user interface on the internet browser which converts the received raw RSSI data into an animated reactive web interface. Using this IoT application, users can monitor or track the current location of the reported Bluetooth signal devices. In summary, the ILS is capable of showing the tracking device using a 2D map on the browser. Hence, it can save consumers time and effectively useful.