Detección de carriles en caminos rurales no pavimentados

El trabajo presenta un procedimiento para la detección de caminos rurales, particularmente aquellos de tierra, grava, ripio o estabilizado, que por sus características constructivas no presentan delimitaciones laterales parejas, ni demarcación central o lateral. Este tipo de caminos es frecuente en varios países que, por su infraestructura vial, aún tienen un gran porcentaje de carreteras consideradas como pertenecientes a la red terciaria –sin pavimentar ni delinear-. El procedimiento propuesto de detección de bordes de los caminos utiliza una estrategia de análisis primario de los canales de matiz, saturación e intensidad de la imagen a fin de detectar cuál de ellos provee, en cada caso, mejor información. Posteriormente se aplica una secuencia de filtros de convolución que faciliten la detección de las curvas que mapean ambos bordes laterales mediante una transformada de Hough adaptativa.IX Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Detección de carriles en caminos rurales no pavimentados

El trabajo presenta un procedimiento para la detección de caminos rurales, particularmente aquellos de tierra, grava, ripio o estabilizado, que por sus características constructivas no presentan delimitaciones laterales parejas, ni demarcación central o lateral. Este tipo de caminos es frecuente en varios países que, por su infraestructura vial, aún tienen un gran porcentaje de carreteras consideradas como pertenecientes a la red terciaria –sin pavimentar ni delinear-. El procedimiento propuesto de detección de bordes de los caminos utiliza una estrategia de análisis primario de los canales de matiz, saturación e intensidad de la imagen a fin de detectar cuál de ellos provee, en cada caso, mejor información. Posteriormente se aplica una secuencia de filtros de convolución que faciliten la detección de las curvas que mapean ambos bordes laterales mediante una transformada de Hough adaptativa.IX Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Detección de carriles en caminos rurales no pavimentados

El trabajo presenta un procedimiento para la detección de caminos rurales, particularmente aquellos de tierra, grava, ripio o estabilizado, que por sus características constructivas no presentan delimitaciones laterales parejas, ni demarcación central o lateral. Este tipo de caminos es frecuente en varios países que, por su infraestructura vial, aún tienen un gran porcentaje de carreteras consideradas como pertenecientes a la red terciaria –sin pavimentar ni delinear-. El procedimiento propuesto de detección de bordes de los caminos utiliza una estrategia de análisis primario de los canales de matiz, saturación e intensidad de la imagen a fin de detectar cuál de ellos provee, en cada caso, mejor información. Posteriormente se aplica una secuencia de filtros de convolución que faciliten la detección de las curvas que mapean ambos bordes laterales mediante una transformada de Hough adaptativa.IX Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI

Model Predictive Control of Highway Emergency Maneuvering and Collision Avoidance

Autonomous emergency maneuvering (AEM) is an active safety system that automates safe maneuvers to avoid imminent collision, particularly in highway driving situations. Uncertainty about the surrounding vehicles’ decisions and also about the road condition, which has significant effects on the vehicle’s maneuverability, makes it challenging to implement the AEM strategy in practice. With the rise of vehicular networks and connected vehicles, vehicles would be able to share their perception and also intentions with other cars. Therefore, cooperative AEM can incor- porate surrounding vehicles’ decisions and perceptions in order to improve vehicles’ predictions and estimations and thereby provide better decisions for emergency maneuvering. In this thesis, we develop an adaptive, cooperative motion planning scheme for emergency maneuvering, based on the model predictive control (MPC) approach, for vehicles within a ve- hicular network. The proposed emergency maneuver planning scheme finds the best combination of longitudinal and lateral maneuvers to avoid imminent collision with surrounding vehicles and obstacles. To implement real-time MPC for the non-convex problem of collision free motion planning, safety constraints are suggested to be convexified based on the road geometry. To take advantage of vehicular communication, the surrounding vehicles’ decisions are incorporated in the prediction model to improve the motion planning results. The MPC approach is prone to loss of feasibility due to the limited prediction horizon for decision-making. For the autonomous vehicle motion planning problem, many of detected ob- stacles, which are beyond the prediction horizon, cannot be considered in the instantaneous de- cisions, and late consideration of them may cause infeasibility. The conditions that guarantee persistent feasibility of a model predictive motion planning scheme are studied in this thesis. Maintaining the system’s states in a control invariant set of the system guarantees the persis- tent feasibility of the corresponding MPC scheme. Specifically, we present two approaches to compute control invariant sets of the motion planning problem; the linearized convexified ap- proach and the brute-force approach. The resulting computed control invariant sets of these two approaches are compared with each other to demonstrate the performance of the proposed algorithm. Time-variation of the road condition affects the vehicle dynamics and constraints. Therefore, it necessitates the on-line identification of the road friction parameter and implementation of an adaptive emergency maneuver motion planning scheme. In this thesis, we investigate coopera- tive road condition estimation in order to improve collision avoidance performance of the AEM system. Each vehicle estimates the road condition individually, and disseminates it through the vehicular network. Accordingly, a consensus estimation algorithm fuses the individual estimates to find the maximum likelihood estimate of the road condition parameter. The performance of the proposed cooperative road condition estimation has been validated through simulations

Driver lane change intention inference using machine learning methods.

Lane changing manoeuvre on highway is a highly interactive task for human drivers. The intelligent vehicles and the advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver. The ADAS also need to understand the driver potential intent correctly since it shares the control authority with the human driver. This study provides a research on the driver intention inference, particular focus on the lane change manoeuvre on highways. This report is organised in a paper basis, where each chapter corresponding to a publication, which is submitted or to be submitted. Part Ⅰ introduce the motivation and general methodology framework for this thesis. Part Ⅱ includes the literature survey and the state-of-art of driver intention inference. Part Ⅲ contains the techniques for traffic context perception that focus on the lane detection. A literature review on lane detection techniques and its integration with parallel driving framework is proposed. Next, a novel integrated lane detection system is designed. Part Ⅳ contains two parts, which provides the driver behaviour monitoring system for normal driving and secondary tasks detection. The first part is based on the conventional feature selection methods while the second part introduces an end-to-end deep learning framework. The design and analysis of driver lane change intention inference system for the lane change manoeuvre is proposed in Part Ⅴ. Finally, discussions and conclusions are made in Part Ⅵ. A major contribution of this project is to propose novel algorithms which accurately model the driver intention inference process. Lane change intention will be recognised based on machine learning (ML) methods due to its good reasoning and generalizing characteristics. Sensors in the vehicle are used to capture context traffic information, vehicle dynamics, and driver behaviours information. Machine learning and image processing are the techniques to recognise human driver behaviour.PhD in Transpor

Object-level fusion for surround environment perception in automated driving applications

Driver assistance systems have increasingly relied on more sensors for new functions. As advanced driver assistance system continue to improve towards automated driving, new methods are required for processing the data in an efficient and economical manner from the sensors for such complex systems. The detection of dynamic objects is one of the most important aspects required by advanced driver assistance systems and automated driving. In this thesis, an environment model approach for the detection of dynamic objects is presented in order to realize an effective method for sensor data fusion. A scalable high-level fusion architecture is developed for fusing object data from several sensors in a single system, where processing occurs in three levels: sensor, fusion and application. A complete and consistent object model which includes the object’s dynamic state, existence probability and classification is defined as a sensor-independent and generic interface for sensor data fusion across all three processing levels. Novel algorithms are developed for object data association and fusion at the fusion-level of the architecture. An asynchronous sensor-to-global fusion strategy is applied in order to process sensor data immediately within the high-level fusion architecture, giving driver assistance systems the most up-to-date information about the vehicle’s environment. Track-to-track fusion algorithms are uniquely applied for dynamic state fusion, where the information matrix fusion algorithm produces results comparable to a low-level central Kalman filter approach. The existence probability of an object is fused using a novel approach based on the Dempster-Shafer evidence theory, where the individual sensor’s existence estimation performance is considered during the fusion process. A similar novel approach with the Dempster-Shafer evidence theory is also applied to the fusion of an object’s classification. The developed high-level sensor data fusion architecture and its algorithms are evaluated using a prototype vehicle equipped with 12 sensors for surround environment perception. A thorough evaluation of the complete object model is performed on a closed test track using vehicles equipped with hardware for generating an accurate ground truth. Existence and classification performance is evaluated using labeled data sets from real traffic scenarios. The evaluation demonstrates the accuracy and effectiveness of the proposed sensor data fusion approach. The work presented in this thesis has additionally been extensively used in several research projects as the dynamic object detection platform for automated driving applications on highways in real traffic

Calibración de un algoritmo de detección de anomalías marítimas basado en la fusión de datos satelitales

La fusión de diferentes fuentes de datos aporta una ayuda significativa en el proceso de toma de decisiones. El presente artículo describe el desarrollo de una plataforma que permite detectar anomalías marítimas por medio de la fusión de datos del Sistema de Información Automática (AIS) para seguimiento de buques y de imágenes satelitales de Radares de Apertura Sintética (SAR). Estas anomalías son presentadas al operador como un conjunto de detecciones que requieren ser monitoreadas para descubrir su naturaleza. El proceso de detección se lleva adelante primero identificando objetos dentro de las imágenes SAR a través de la aplicación de algoritmos CFAR, y luego correlacionando los objetos detectados con los datos reportados mediante el sistema AIS. En este trabajo reportamos las pruebas realizadas con diferentes configuraciones de los parámetros para los algoritmos de detección y asociación, analizamos la respuesta de la plataforma y reportamos la combinación de parámetros que reporta mejores resultados para las imágenes utilizadas. Este es un primer paso en nuestro objetivo futuro de desarrollar un sistema que ajuste los parámetros en forma dinámica dependiendo de las imágenes disponibles.XVI Workshop Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI