Vehicle Blind Spot Monitoring Phenomenon Using Ultrasonic Sensor

This paper evaluates a conceptualization of Vehicle Blind Spot Monitoring System (VBMS), which performs a more effective approach in eliminating blind spot of the driver. The newly developed smart blind spot monitoring system simply focusing on an advancement of the preceding work, along with compromising user compatibility and cost-effectiveness. Compact design, reliable and low-cost that contributes to a highly affordable safety feature are the flagship of this new system. Components selection is the main role in constructing an inexpensive blind spot detection system in the present work. Thus, Arduino UNO R3 model and HC-SR04 ultrasonic sensors were employed for the VBMS system due to reasonable market price. Plus, the ultrasonic sensor has demonstrated a remarkable performance in the past blind spot detection system application. Concerning easy installation as well as maintenance on any vehicle, the VBMS is designed as a compact device which assembles the main control unit and sensory partsin a single body to be located at the bottom of the side mirror. Meanwhile, the hazard-warning signal is separately located at the passenger compartment for easily visible by the driver. The angle and sensing range of sensors are both adjustable but vital as their projections define the blind spot limit accurately by characterizing low to a high potential hazard. At the end of this work, a complete VBMS functional prototype of has been establish which effective for real traffic on-road experimentation, with various conditions specified (static, various speed, and overtaken). From the data collected, all targets of the present work have been attained regarding monitoring phenomenon shown by the new-built system. Both pros and cons of VBMS are discussed for further improvement ideas on product developmen

Safe Pass

The purpose of this project is to design a sensor to be mounted on Class IV and higher vehicles to detect on-coming traffic. If traffic has been detected, the system is to warn drivers behind the stopped vehicle that passing is unsafe. The vehicle detection is to be implemented using a LiDAR detection method along with signal processing. A wireless transceiver is to transmit from the front radar module to the rear warning indicator module when the conditions are unsafe for passing. The project goals are to increase road safety and maintain traffic flow. The report details the challenges due to the wireless link and the original radar approach

Estudio de Sistemas Avanzados de Asistencia al Conductor (ADAS) en vehículos y propuesta de aplicación de técnicas de seguimiento de la mirada para su mejora

Durante las últimas décadas, el campo de la automoción ha experimentado un notable desarrollo tecnológico. En gran medida, esto se debe al ambicioso objetivo de la creación de un vehículo autónomo, en el cual el conductor es relevado de sus tareas. No obstante, dado el nivel de desarrollo actual, existen ciertas tareas en las cuales el conductor sigue siendo imprescindible.Muchas de ellas tienen una repercusión directa sobre la seguridad vial, que juega un papel fundamental en la seguridad de los usuarios durante su desplazamiento por las vías. Para garantizarla, es crucial que el conductor esté alerta y pueda responder ante situaciones imprevistas que puedan entrañar un posible riesgo. En este contexto, surgen los Sistemas Avanzados de Asistencia al Conductor (en inglés, Advanced Driver Asistance Systems, ADAS), que apoyan la tarea de conducción, asistiendo al conductor en la toma de decisiones, o incluso asumiendo el control de la conducción parcialmente si la situación lo requiere, disminuyendo así el número de accidentes que se producen.En este contexto, uno de los objetivos principales de este proyecto es el estudio del estado del arte de los ADAS empleados en la actualidad, a través de su clasificación mediante una taxonomía propuesta en la literatura, así como un análisis de su funcionamiento. Dentro de este amplio ámbito, se hace especial énfasis en ADAS basados en visión por computador, que tienen la capacidad de monitorizar tanto el exterior del vehículo como al propio conductor. Además, se estudian los ADAS que incluyen seguimiento de la mirada, puesto que proporcionan información del estado de atención del conductor, que es crítico en la garantía de la seguridad vial.En el marco de los ADAS basados en visión y seguimiento de mirada, y en base a las limitaciones de los sistemas actuales, se propone también un sistema basado en seguimiento de mirada que permite inferir información sobre el estado de atención del conductor a partir de información visual del exterior del vehículo. Para ello, se estudian modelos de aprendizaje profundo capaces de inferir las regiones del entorno visual del conductor a las que es más probable que este mire. Adicionalmente, se estudian las limitaciones de tal modelo, y se proponen diversas mejoras, que resultan en un sistema más preciso, incluyendo la capacidad de generalización en un mayor número de situaciones, así como su funcionamiento con diferentes condiciones meteorológicas y lumínicas. El sistema desarrollado es versátil, y potencialmente implementable en un ADAS de bajo coste.<br /

Context Aware Pre-Crash System for Vehicular ad hoc Networks Using Dynamic Bayesian Model

Tragically, traffic accidents involving drivers, motorcyclists and pedestrians result in thousands of fatalities worldwide each year. For this reason, making improvements to road safety and saving people’s lives is an international priority. In recent years, this aim has been supported by Intelligent Transport Systems, offering safety systems and providing an intelligent driving environment. The development of wireless communications and mobile ad hoc networks has led to improvements in intelligent transportation systems heightening these systems’ safety. Vehicular ad hoc Networks comprise an important technology; included within intelligent transportation systems, they use dedicated short-range communications to assist vehicles to communicate with one another, or with those roadside units in range. This form of communication can reduce road accidents and provide a safer driving environment. A major challenge has been to design an ideal system to filter relevant contextual information from the surrounding environment, taking into consideration the contributory factors necessary to predict the likelihood of a crash with different levels of severity. Designing an accurate and effective pre-crash system to avoid front and back crashes or mitigate their severity is the most important goal of intelligent transportation systems, as it can save people’s lives. Furthermore, in order to improve crash prediction, context-aware systems can be used to collect and analyse contextual information regarding contributory factors. The crash likelihood in this study is considered to operate within an uncertain context, and is defined according to the dynamic interaction between the driver, the vehicle and the environment, meaning it is affected by contributory factors and develops over time. As a crash likelihood is considered to be an uncertain context and develops over time, any usable technology must overcome this uncertainty in order to accurately predict crashes. This thesis presents a context-aware pre-crash collision prediction system, which captures information from the surrounding environment, the driver and other vehicles on the road. It utilises a Dynamic Bayesian Network as a reasoning model to predict crash likelihood and severity level, whether any crash will be fatal, serious, or slight. This is achieved by combining the above mentioned information and performing probabilistic reasoning over time. The thesis introduces novel context aware on-board unit architecture for crash prediction. The architecture is divided into three phases: the physical, the thinking and the application phase; these which represent the three main subsystems of a context-aware system: sensing, reasoning and acting. In the thinking phase, a novel Dynamic Bayesian Network framework is introduced to predict crash likelihood. The framework is able to perform probabilistic reasoning to predict uncertainty, in order to accurately predict a crash. It divides crash severity levels according to the UK department for transport, into fatal, serious and slight. GeNIe version 2.0 software was used to implement and verify the Dynamic Bayesian Network model. This model has been verified using both syntactical and real data provided by the UK department for transport in order to demonstrate the prediction accuracy of the proposed model and to demonstrate the importance of including a large amount of contextual information in the prediction process. The evaluation of the proposed system delivered high-fidelity results, when predicting crashes and their severity. This was judged by inputting different sensor readings and performing several experiments. The findings of this study has helped to predict the probability of a crash at different severity levels, accounting for factors that may be involved in causing a crash, thereby representing a valuable step towards creating a safer traffic network