Situation-Aware Left-Turning Connected and Automated Vehicle Operation at Signalized Intersections

One challenging aspect of the Connected and Automated Vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive driving behavior, and CAVs expect other non-autonomous entities on the road will follow the traffic rules or common driving behavior. However, the presence of aggressive human drivers in the surrounding environment, who may not follow traffic rules and behave abruptly, can lead to serious safety consequences. In this paper, we have addressed the CAV and non-CAV interaction by evaluating a situation-awareness module for left-turning CAV operations in an urban area. Existing literature does not consider the intent of the following vehicle for a CAVs left-turning movement, and existing CAV controllers do not assess the following non-CAVs intents. Based on our simulation study, the situation-aware CAV controller module reduces up to 27% of the abrupt braking of the following non-CAVs for scenarios with different opposing through movement compared to the base scenario with the autonomous vehicle, without considering the following vehicles intent. The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800, and 1000 vehicle/hour/lane are 58%, 52%, and 62%, respectively, for the aggressive human driver following the CAV if the following vehicles intent is considered by a CAV in making a left turn at an intersection

Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems

Understanding the components of Transportation Cyber-Physical Systems (TCPS), and inter-relation and interactions among these components are key factors to leverage the full potentials of Connected and Automated Vehicles (CAVs). In a connected environment, CAVs can communicate with other components of TCPS, which include other CAVs, other connected road users, and digital infrastructure. Deploying supporting infrastructure for TCPS, and developing and testing CAV-specific applications in a TCPS environment are mandatory to achieve the CAV potentials. This dissertation specifically focuses on the study of current TCPS infrastructure (Part 1), and the development and verification of CAV applications for an urban TCPS environment (Part 2). Among the TCPS components, digital infrastructure bears sheer importance as without connected infrastructure, the Vehicle-to-Infrastructure (V2I) applications cannot be implemented. While focusing on the V2I applications in Part 1, this dissertation evaluates the current digital roadway infrastructure status. The dissertation presents a set of recommendations, based on a review of current practices and future needs. In Part 2, To synergize the digital infrastructure deployment with CAV deployments, two V2I applications are developed for CAVs for an urban TCPS environment. At first, a real-time adaptive traffic signal control algorithm is developed, which utilizes CAV data to compute the signal timing parameters for an urban arterial in the near-congested traffic condition. The analysis reveals that the CAV-based adaptive signal control provides operational benefits to both CVs and non-CVs with limited data from 5% CVs, with 5.6% average speed increase, and 66.7% and 32.4% average maximum queue length and stopped delay reduction, respectively, on a corridor compared to the actuated coordinated scenario. The second application includes the development of a situation-aware left-turning CAV controller module, which optimizes CAV speed based on the follower driver\u27s aggressiveness. Existing autonomous vehicle controllers do not consider the surrounding driver\u27s behavior, which may lead to road rage, and rear-end crashes. The analysis shows that the average travel time reduction for the scenarios with 600, 800 and 1000 veh/hr/lane opposite traffic stream are 61%, 23%, and 41%, respectively, for the follower vehicles, if the follower driver\u27s behavior is considered by CAVs

Classification of driver behavior using machine learning techniques and onboard monitoring with OBD ll in real road conditions

La movilidad vial y el buen comportamiento del conductor en la carretera es de vital importancia para mantener una movilidad sin accidentes de tránsito y conductores prudentes en las vías. Los sistemas inteligentes de transporte (SIT) brindan la optimización de la estructura vial incrementando el control, la eficiencia, efectividad, la educación de los conductores al momento de la conducción, con el objetivo de gestionar el crecimiento demanda de movilidad y el comportamiento de los conductores en las vías. Un aporte crucial para los sistemas inteligentes de transporte son las campañas de monitoreo en condiciones reales de carretera que permitan la recolección de datos y su vez identificar el tipo de comportamiento del conductor. En el proyecto desarrollado se implementó una campaña de monitoreo abordo con un dispositivo ODB ll instalado en una muestra de 5 vehículos, que por medio de la conexión a bluetooth y una App instalada en el Smartphone se realiza la captura de los datos pertinentes para identificar el comportamiento de conducción. Para la identificación de los comportamientos de conducción se desarrolló un modelo de Machine Learning por medio de la técnica K-Means donde se clasificaron a los conductores en 3 grandes grupos (clúster): conductor normal, agresivo y peligroso. Con la identificación de los comportamientos de conducción se logra evidenciar que el conductor peligroso al ir a velocidad altas, tiene un mayor consumo de combustible y el riesgo de ocasionar accidenten en la malla vial.INTRODUCCIÓN...............................................................................................13 1.MARCO TEÓRICO O ESTADO DEL ARTE.................................................16 1.1 MARCO TEÓRICO......................................................................................16 1.1.1 Comportamiento de conducción..............................................................16 1.1.2 Estilos de conducción...............................................................................22 1.2 ESTADO DEL ARTE ...................................................................................25 1.2.1 Análisis Bibliométrico ...............................................................................25 1.2.2 Tipos de comportamiento del conductor.................................................29 1.2.3 Instrumentación para la recolección de datos ........................................30 1.2.4 Técnicas de clasificación para el comportamiento del conductor .........31 2.METODOLOGÍA.............................................................................................33 3.MONITOREO DE VARIABLES DE OPERACIÓN Y ACTUALIZACIÓN DE LA BASE DE DATOS...............................................................................34 3.1 CAMPAÑA DE MONITOREO.....................................................................34 3.1.1 Ruta Seleccionada ...................................................................................35 3.1.2 Datos técnicos de los vehículos monitoreados.......................................36 3.1.3 Datos sociodemográficos de los conductores ........................................37 3.1.4 Variables monitoreadas ...........................................................................38 3.1.5 Sistema de monitoreo ejecutado.............................................................39 3.1.6 Sistema de captura de los datos .............................................................40 3.1.7 Canal de Conectividad para él envió de la información.........................42 3.2. SISTEMA CAPTURAR DE DATOS...........................................................44 3.2.1 Almacenamiento de datos .......................................................................48 3.2.2 Captura de los datos ................................................................................50 3.2.3 Eliminación de Datos Atípicos .................................................................50 3.2.4 Registro de datos en la nube...................................................................54 3.3 BASE DE DATOS PROYECTO ACTUAL 2023 ........................................54 3.4 BASE DE DATOS CONCATENADA..........................................................56 4.TÉCNICA DE MACHINE LEARNING PARA LA CLASIFICACIÓN DE LOS COMPORTAMIENTOS DE CONDUCCIÓN ........................................58 7 4.1 METODOLOGÍA APLICADA PARA LA CLASIFICACIÓN DE LOS COMPORTAMIENTOS DE CONDUCCIÓN. ...................................................58 4.2 ELECCIÓN Y CONFIGURACIÓN DEL ENTORNO DE DESARROLLO .62 4.2.1 Entorno de desarrollo integrado IDE.......................................................62 4.2.2 Listado de IDE en el lenguaje de programación Python........................63 4.2.3 Cuadro comparativo de los IDE...............................................................64 4.3 CONSTRUCCIÓN DEL MODELO DE MACHINE LEARNING.................65 4.3.1 Paso a paso para la construcción del modelo de Machine Learning:...67 4.4 ANÁLISIS DE LOS DATOS ........................................................................70 4.5 MODELO DE MACHINE LEARNING.........................................................76 4.6 PREDICCIONES SEGÚN EL MODELO DE MACHINE LEARNING .......89 4.6.1 Pasos para realizar la predicción con el modelo de Machine Learning 90 4.7 RESULTADOS OBTENIDOS DE LAS PREDICCIONES DE LOS CONDUCTORES...............................................................................................98 4.8 ANÁLISIS DE LOS DIAGRAMAS SAFD..................................................102 5.VALIDACIÓN DE RESULTADOS POR MEDIO DE GUI (INTERFAZ GRÁFICA DE USUARIO) ............................................................................104 5.1 VALIDACIÓN DEL ALGORITMO .............................................................104 5.2 INTERFAZ GRÁFICA................................................................................109 5.2.1 Librerías implementadas en Python para la creación de la interfaz gráfica…...........................................................................................................110 5.2.2 Proceso de construcción de la GUI.......................................................112 6.CONCLUSIONES.........................................................................................118 7.RECOMENDACIONES Y TRABAJOS FUTUROS ....................................119 REFERENCIAS Y BIBLIOGRAFIA.................................................................120 LISTA DE ANEXOS.........................................................................................126 ANEXOS..........................................................................................................127MaestríaRoad mobility and good driver behavior on the road is of vital importance to maintain mobility without traffic accidents and prudent drivers on the roads. Intelligent transportation systems (ITS) provide optimization of the road structure by increasing control, efficiency, effectiveness, and driver education at the time of driving, with the aim of managing the growing demand for mobility and the behavior of drivers. drivers on the roads. A crucial contribution to intelligent transportation systems are monitoring campaigns in real road conditions that allow data collection and in turn identify the type of driver behavior. In the developed project, an on-board monitoring campaign was implemented with an ODB II device installed in a sample of 5 vehicles, which through a Bluetooth connection and an App installed on the Smartphone captures the relevant data to identify the driving behavior. To identify driving behaviors, a Machine Learning model was developed using the K-Means technique where drivers were classified into 3 large groups (cluster): normal, aggressive and dangerous driver. With the identification of driving behaviors, it is possible to show that the dangerous driver, when traveling at high speed, has greater fuel consumption and the risk of causing accidents on the road network.Modalidad Virtua

Driver aggressiveness detection via multisensory data fusion

Detection of driver aggressiveness is a significant method in terms of safe driving. Every year, a vast number of traffic accidents occur due to aggressive driving behaviour. These traffic accidents cause fatalities, severe disorders and huge economical cost. Therefore, detection of driver aggressiveness could help in reducing the number of traffic accidents by warning related authorities to take necessary precautions. In this work, a novel method is introduced in order to detect driver aggressiveness on vehicle. The proposed method is based on the fusion of visual and sensor features to characterize related driving session and to decide whether the session involves aggressive driving behaviour. Visual information is used to detect road lines and vehicle images, whereas sensor information provides data such as vehicle speed and engine speed. Both information is used to obtain feature vectors which represent a driving session. These feature vectors are obtained by modelling time series data by Gaussian distributions. An SVM classifier is utilized to classify the feature vectors in order for aggressiveness decision. The proposed system is tested by real traffic data, and it achieved an aggressive driving detection rate of 93.1%