Incident forecasting model for motorcycle driving based on IoT and artificial intelligence.

El Internet de las Cosas y la Inteligencia Artificial aportan cada vez más soluciones al ejercicio de captar datos de manera efectiva, llevándolos por etapas de procesamiento y análisis con el fin de extraer información valiosa. Actualmente, se aplican herramientas tecnológicas con el fin de contrarrestar los incidentes en conducción de motocicletas, ya sea que estas hagan parte del mismo vehículo o estén involucradas de manera externa en el entorno. Los incidentes en conducción de motocicletas van en aumento debido a la demanda de adquisición de estos vehículos, lo cual hace importante generar un enfoque hacia la disminución del riesgo de accidentalidad vial, basado en el análisis del comportamiento dinámico durante la conducción, el cual dará lugar a pronosticar incidentes. El desarrollo de esta investigación inició con la detección y almacenamiento de datos asociados a la variable dinámica de aceleración de una motocicleta en conducción, esto con ayuda de un sensor acelerómetro de 3 ejes generando un conjunto de datos, el cual fue procesado y analizado para posteriormente ser tomado por tres modelos predictivos de clasificación basados en Aprendizaje de Máquina los cuales fueron Árboles de Decisión, K – Vecinos más cercanos y Bosques Aleatorios. Se evaluó el desempeño de cada modelo en la tarea de clasificar mejor el nivel de riesgo de accidentalidad, concerniente con el estilo de conducción basado en ciertos niveles de aceleración. El modelo de Bosques Aleatorios mostró un desempeño ligeramente mejor comparado con el que mostraron los otros dos modelos, con un 97,24 % de exactitud y exhaustividad, un 97,16% de precisión y un 97,17 % de puntaje F1Internet of Things and Artificial Intelligence provide more and more solutions to the exercise of capturing data effectively, taking them through processing and analysis stages to extract valuable information. Currently, technological tools are applied to counteract incidents in motorcycle driving, whether they are part of the same vehicle or are externally involved in the environment. Incidents in motorcycle driving are increasing due to the demand for the acquisition of these vehicles, which makes it important to generate an approach towards reducing the risk of road accidents based on the analysis of dynamic behavior while driving. The development of this research began with the detection and storage of data associated with the dynamic acceleration variable of a motorcycle while driving, this with the help of a 3-axis accelerometer sensor generating a dataset, which was processed and analyzed for later be taken by three predictive classification models based on Machine Learning which were Decision Trees, K - Nearest Neighbors and Random Forests. The performance of each model was evaluated in the task of better classifying the level of accident risk, concerning the driving style based on certain levels of acceleration. The Random Forest model showed a slightly better performance compared to that shown by the other two models, with 97.24% accuracy and recall, 97.16% precision and 97.17% F1 score

Using Sensor Redundancy in Vehicles and Smartphones for Driving Security and Safety

The average American spends around at least one hour driving every day. During that time the driver utilizes various sensors to enhance their commute. Approximately 77% of smartphone users rely on navigation apps daily. Consumer grade OBD dongles that collect vehicle sensor data to monitor safe driving habits are common. Existing sensing applications pertaining to our drive are often separate from each other and fail to learn from and utilize the information gained by other sensing streams and other drivers. In order to best leverage the widespread use of sensing capabilities, we have to unify and coordinate the different sensing streams in a meaningful way. This dissertation explores and validates the following thesis: Sensing the same phenomenon from multiple perspectives can enhance vehicle safety, security and transportation. First, it presents findings from an exploratory study on unifying vehicular sensor streams. We explored combining sensory data from within one vehicle through pairwise correlation and across multiple vehicles through normal models built with principal component analysis and cluster analysis. Our findings from this exploratory study motivated the rest of this thesis work on using sensor redundancy for CAN-bus injection detection and driving hazard detection. Second, we unify the phone sensors with vehicle sensors to detect CAN bus injection attacks that compromise vehicular sensor values. Specifically, we answer the question: Are phone sensors accurate enough to detect typical CAN bus injection attacks found in literature? Through extensive tests we found that phone sensors are sufficiently accurate to detect many CAN-bus injection attacks. Third, we construct GPS trajectories from multiple vehicles nearby to find stationary and mobile driving hazards such as a bicyclist on the side of the road. Such a tool will effectively extend the repertoire of current navigation assistant applications such as Google Maps which detect and warn drivers about upcoming stationary hazards. Finally, we present an easy-to-use tool to help developers and researchers quickly build and prototype data-collection apps that naturally exploit sensing redundancy. Overall, this thesis provides a unified basis for exploiting sensing redundancy existing inside a single vehicle as well as between different vehicles to enhance driving safety and security.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155154/1/arungan_1.pd