Vehicular Instrumentation and Data Processing for the Study of Driver Intent

The primary goal of this thesis is to provide processed experimental data needed to determine whether driver intentionality and driving-related actions can be predicted from quantitative and qualitative analysis of driver behaviour. Towards this end, an instrumented experimental vehicle capable of recording several synchronized streams of data from the surroundings of the vehicle, the driver gaze with head pose and the vehicle state in a naturalistic driving environment was designed and developed. Several driving data sequences in both urban and rural environments were recorded with the instrumented vehicle. These sequences were automatically annotated for relevant artifacts such as lanes, vehicles and safely driveable areas within road lanes. A framework and associated algorithms required for cross-calibrating the gaze tracking system with the world coordinate system mounted on the outdoor stereo system was also designed and implemented, allowing the mapping of the driver gaze with the surrounding environment. This instrumentation is currently being used for the study of driver intent, geared towards the development of driver maneuver prediction models

Multi-Sensor Data Fusion for Robust Environment Reconstruction in Autonomous Vehicle Applications

In autonomous vehicle systems, understanding the surrounding environment is mandatory for an intelligent vehicle to make every decision of movement on the road. Knowledge about the neighboring environment enables the vehicle to detect moving objects, especially irregular events such as jaywalking, sudden lane change of the vehicle etc. to avoid collision. This local situation awareness mostly depends on the advanced sensors (e.g. camera, LIDAR, RADAR) added to the vehicle. The main focus of this work is to formulate a problem of reconstructing the vehicle environment using point cloud data from the LIDAR and RGB color images from the camera. Based on a widely used point cloud registration tool such as iterated closest point (ICP), an expectation-maximization (EM)-ICP technique has been proposed to automatically mosaic multiple point cloud sets into a larger one. Motion trajectories of the moving objects are analyzed to address the issue of irregularity detection. Another contribution of this work is the utilization of fusion of color information (from RGB color images captured by the camera) with the three-dimensional point cloud data for better representation of the environment. For better understanding of the surrounding environment, histogram of oriented gradient (HOG) based techniques are exploited to detect pedestrians and vehicles.;Using both camera and LIDAR, an autonomous vehicle can gather information and reconstruct the map of the surrounding environment up to a certain distance. Capability of communicating and cooperating among vehicles can improve the automated driving decisions by providing extended and more precise view of the surroundings. In this work, a transmission power control algorithm is studied along with the adaptive content control algorithm to achieve a more accurate map of the vehicle environment. To exchange the local sensor data among the vehicles, an adaptive communication scheme is proposed that controls the lengths and the contents of the messages depending on the load of the communication channel. The exchange of this information can extend the tracking region of a vehicle beyond the area sensed by its own sensors. In this experiment, a combined effect of power control, and message length and content control algorithm is exploited to improve the map\u27s accuracy of the surroundings in a cooperative automated vehicle system

LaneMapper: A City-scale Lane Map Generator for Autonomous Driving

Autonomous vehicles require lane maps to help navigate from a start to a goal position in a safe, comfortable and quick manner. A lane map represents a set of features inherent to the road, such as lanes, stop signs, traffic lights, and intersections. We present a novel approach to detect multiple lane boundaries and traffic signs to create a 3D city-scale map of the driving environment. We detect, recognize and track lane boundaries with multimodal sensory and prior inputs, such as camera, LiDAR, and GPS/IMU, to assist autonomous driving. We detect and classify traffic signs from the image considering high reflectivity of LiDAR points and further register the locations of traffic signs and lane boundaries together in the world coordinate frame. We have also made our code base open-source for the research community to tweak or use our algorithm for their purposes

CARLA+: An Evolution of the CARLA Simulator for Complex Environment Using a Probabilistic Graphical Model

In an urban and uncontrolled environment, the presence of mixed traffic of autonomous vehicles, classical vehicles, vulnerable road users, e.g., pedestrians, and unprecedented dynamic events makes it challenging for the classical autonomous vehicle to navigate the traffic safely. Therefore, the realization of collaborative autonomous driving has the potential to improve road safety and traffic efficiency. However, an obvious challenge in this regard is how to define, model, and simulate the environment that captures the dynamics of a complex and urban environment. Therefore, in this research, we first define the dynamics of the envisioned environment, where we capture the dynamics relevant to the complex urban environment, specifically, highlighting the challenges that are unaddressed and are within the scope of collaborative autonomous driving. To this end, we model the dynamic urban environment leveraging a probabilistic graphical model (PGM). To develop the proposed solution, a realistic simulation environment is required. There are a number of simulators—CARLA (Car Learning to Act), one of the prominent ones, provides rich features and environment; however, it still fails on a few fronts, for example, it cannot fully capture the complexity of an urban environment. Moreover, the classical CARLA mainly relies on manual code and multiple conditional statements, and it provides no pre-defined way to do things automatically based on the dynamic simulation environment. Hence, there is an urgent need to extend the off-the-shelf CARLA with more sophisticated settings that can model the required dynamics. In this regard, we comprehensively design, develop, and implement an extension of a classical CARLA referred to as CARLA+ for the complex environment by integrating the PGM framework. It provides a unified framework to automate the behavior of different actors leveraging PGMs. Instead of manually catering to each condition, CARLA+ enables the user to automate the modeling of different dynamics of the environment. Therefore, to validate the proposed CARLA+, experiments with different settings are designed and conducted. The experimental results demonstrate that CARLA+ is flexible enough to allow users to model various scenarios, ranging from simple controlled models to complex models learned directly from real-world data. In the future, we plan to extend CARLA+ by allowing for more configurable parameters and more flexibility on the type of probabilistic networks and models one can choose. The open-source code of CARLA+ is made publicly available for researchers

Lidar-based scene understanding for autonomous driving using deep learning

With over 1.35 million fatalities related to traffic accidents worldwide, autonomous driving was foreseen at the beginning of this century as a feasible solution to improve security in our roads. Nevertheless, it is meant to disrupt our transportation paradigm, allowing to reduce congestion, pollution, and costs, while increasing the accessibility, efficiency, and reliability of the transportation for both people and goods. Although some advances have gradually been transferred into commercial vehicles in the way of Advanced Driving Assistance Systems (ADAS) such as adaptive cruise control, blind spot detection or automatic parking, however, the technology is far from mature. A full understanding of the scene is actually needed so that allowing the vehicles to be aware of the surroundings, knowing the existing elements of the scene, as well as their motion, intentions and interactions. In this PhD dissertation, we explore new approaches for understanding driving scenes from 3D LiDAR point clouds by using Deep Learning methods. To this end, in Part I we analyze the scene from a static perspective using independent frames to detect the neighboring vehicles. Next, in Part II we develop new ways for understanding the dynamics of the scene. Finally, in Part III we apply all the developed methods to accomplish higher level challenges such as segmenting moving obstacles while obtaining their rigid motion vector over the ground. More specifically, in Chapter 2 we develop a 3D vehicle detection pipeline based on a multi-branch deep-learning architecture and propose a Front (FR-V) and a Bird’s Eye view (BE-V) as 2D representations of the 3D point cloud to serve as input for training our models. Later on, in Chapter 3 we apply and further test this method on two real uses-cases, for pre-filtering moving obstacles while creating maps to better localize ourselves on subsequent days, as well as for vehicle tracking. From the dynamic perspective, in Chapter 4 we learn from the 3D point cloud a novel dynamic feature that resembles optical flow from RGB images. For that, we develop a new approach to leverage RGB optical flow as pseudo ground truth for training purposes but allowing the use of only 3D LiDAR data at inference time. Additionally, in Chapter 5 we explore the benefits of combining classification and regression learning problems to face the optical flow estimation task in a joint coarse-and-fine manner. Lastly, in Chapter 6 we gather the previous methods and demonstrate that with these independent tasks we can guide the learning of higher challenging problems such as segmentation and motion estimation of moving vehicles from our own moving perspective.Con más de 1,35 millones de muertes por accidentes de tráfico en el mundo, a principios de siglo se predijo que la conducción autónoma sería una solución viable para mejorar la seguridad en nuestras carreteras. Además la conducción autónoma está destinada a cambiar nuestros paradigmas de transporte, permitiendo reducir la congestión del tráfico, la contaminación y el coste, a la vez que aumentando la accesibilidad, la eficiencia y confiabilidad del transporte tanto de personas como de mercancías. Aunque algunos avances, como el control de crucero adaptativo, la detección de puntos ciegos o el estacionamiento automático, se han transferido gradualmente a vehículos comerciales en la forma de los Sistemas Avanzados de Asistencia a la Conducción (ADAS), la tecnología aún no ha alcanzado el suficiente grado de madurez. Se necesita una comprensión completa de la escena para que los vehículos puedan entender el entorno, detectando los elementos presentes, así como su movimiento, intenciones e interacciones. En la presente tesis doctoral, exploramos nuevos enfoques para comprender escenarios de conducción utilizando nubes de puntos en 3D capturadas con sensores LiDAR, para lo cual empleamos métodos de aprendizaje profundo. Con este fin, en la Parte I analizamos la escena desde una perspectiva estática para detectar vehículos. A continuación, en la Parte II, desarrollamos nuevas formas de entender las dinámicas del entorno. Finalmente, en la Parte III aplicamos los métodos previamente desarrollados para lograr desafíos de nivel superior, como segmentar obstáculos dinámicos a la vez que estimamos su vector de movimiento sobre el suelo. Específicamente, en el Capítulo 2 detectamos vehículos en 3D creando una arquitectura de aprendizaje profundo de dos ramas y proponemos una vista frontal (FR-V) y una vista de pájaro (BE-V) como representaciones 2D de la nube de puntos 3D que sirven como entrada para entrenar nuestros modelos. Más adelante, en el Capítulo 3 aplicamos y probamos aún más este método en dos casos de uso reales, tanto para filtrar obstáculos en movimiento previamente a la creación de mapas sobre los que poder localizarnos mejor en los días posteriores, como para el seguimiento de vehículos. Desde la perspectiva dinámica, en el Capítulo 4 aprendemos de la nube de puntos en 3D una característica dinámica novedosa que se asemeja al flujo óptico sobre imágenes RGB. Para ello, desarrollamos un nuevo enfoque que aprovecha el flujo óptico RGB como pseudo muestras reales para entrenamiento, usando solo information 3D durante la inferencia. Además, en el Capítulo 5 exploramos los beneficios de combinar los aprendizajes de problemas de clasificación y regresión para la tarea de estimación de flujo óptico de manera conjunta. Por último, en el Capítulo 6 reunimos los métodos anteriores y demostramos que con estas tareas independientes podemos guiar el aprendizaje de problemas de más alto nivel, como la segmentación y estimación del movimiento de vehículos desde nuestra propia perspectivaAmb més d’1,35 milions de morts per accidents de trànsit al món, a principis de segle es va predir que la conducció autònoma es convertiria en una solució viable per millorar la seguretat a les nostres carreteres. D’altra banda, la conducció autònoma està destinada a canviar els paradigmes del transport, fent possible així reduir la densitat del trànsit, la contaminació i el cost, alhora que augmentant l’accessibilitat, l’eficiència i la confiança del transport tant de persones com de mercaderies. Encara que alguns avenços, com el control de creuer adaptatiu, la detecció de punts cecs o l’estacionament automàtic, s’han transferit gradualment a vehicles comercials en forma de Sistemes Avançats d’Assistència a la Conducció (ADAS), la tecnologia encara no ha arribat a aconseguir el grau suficient de maduresa. És necessària, doncs, una total comprensió de l’escena de manera que els vehicles puguin entendre l’entorn, detectant els elements presents, així com el seu moviment, intencions i interaccions. A la present tesi doctoral, explorem nous enfocaments per tal de comprendre les diferents escenes de conducció utilitzant núvols de punts en 3D capturats amb sensors LiDAR, mitjançant l’ús de mètodes d’aprenentatge profund. Amb aquest objectiu, a la Part I analitzem l’escena des d’una perspectiva estàtica per a detectar vehicles. A continuació, a la Part II, desenvolupem noves formes d’entendre les dinàmiques de l’entorn. Finalment, a la Part III apliquem els mètodes prèviament desenvolupats per a aconseguir desafiaments d’un nivell superior, com, per exemple, segmentar obstacles dinàmics al mateix temps que estimem el seu vector de moviment respecte al terra. Concretament, al Capítol 2 detectem vehicles en 3D creant una arquitectura d’aprenentatge profund amb dues branques, i proposem una vista frontal (FR-V) i una vista d’ocell (BE-V) com a representacions 2D del núvol de punts 3D que serveixen com a punt de partida per entrenar els nostres models. Més endavant, al Capítol 3 apliquem i provem de nou aquest mètode en dos casos d’ús reals, tant per filtrar obstacles en moviment prèviament a la creació de mapes en els quals poder localitzar-nos millor en dies posteriors, com per dur a terme el seguiment de vehicles. Des de la perspectiva dinàmica, al Capítol 4 aprenem una nova característica dinàmica del núvol de punts en 3D que s’assembla al flux òptic sobre imatges RGB. Per a fer-ho, desenvolupem un nou enfocament que aprofita el flux òptic RGB com pseudo mostres reals per a entrenament, utilitzant només informació 3D durant la inferència. Després, al Capítol 5 explorem els beneficis que s’obtenen de combinar els aprenentatges de problemes de classificació i regressió per la tasca d’estimació de flux òptic de manera conjunta. Finalment, al Capítol 6 posem en comú els mètodes anteriors i demostrem que mitjançant aquests processos independents podem abordar l’aprenentatge de problemes més complexos, com la segmentació i estimació del moviment de vehicles des de la nostra pròpia perspectiva