Automated driving and autonomous functions on road vehicles

In recent years, road vehicle automation has become an important and popular topic for research and development in both academic and industrial spheres. New developments received extensive coverage in the popular press, and it may be said that the topic has captured the public imagination. Indeed, the topic has generated interest across a wide range of academic, industry and governmental communities, well beyond vehicle engineering; these include computer science, transportation, urban planning, legal, social science and psychology. While this follows a similar surge of interest – and subsequent hiatus – of Automated Highway Systems in the 1990’s, the current level of interest is substantially greater, and current expectations are high. It is common to frame the new technologies under the banner of “self-driving cars” – robotic systems potentially taking over the entire role of the human driver, a capability that does not fully exist at present. However, this single vision leads one to ignore the existing range of automated systems that are both feasible and useful. Recent developments are underpinned by substantial and long-term trends in “computerisation” of the automobile, with developments in sensors, actuators and control technologies to spur the new developments in both industry and academia. In this paper we review the evolution of the intelligent vehicle and the supporting technologies with a focus on the progress and key challenges for vehicle system dynamics. A number of relevant themes around driving automation are explored in this article, with special focus on those most relevant to the underlying vehicle system dynamics. One conclusion is that increased precision is needed in sensing and controlling vehicle motions, a trend that can mimic that of the aerospace industry, and similarly benefit from increased use of redundant by-wire actuators

Lateral string stability of vehicle platoons

This internship report is part of a larger assignment which is an analysis of lateral string stability and the development of a controller design method with guaranteed lateral string stability. Lateral string stability is an issue when a look-ahead sensing method is used in combination with a vehicle-following control strategy. The coupling between vehicles enables errors to increase while they propagate upstream through a string of vehicles. Communicating desired yaw rate or lateral acceleration and use this information for controller design could be an option to achieved guaranteed lateral string stability. One of the applications of lateral control will be conducting maneuvers like merging or lane changes. During these maneuvers the side-slip angles of the tyres stay within theinterval of linear tyre response, this means that side-slip angles of the tyres are within 0:5. On this interval the non-linear and linearized tyre model have the same linear response. This makes is possible to use a linearized vehicle model with linear tyres for the modeling of the lateral and yaw dynamics of the vehicle. This is validated using experimental data and it is shown that the response of the linearized vehicle model is almost equal to the actual vehicle response

A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed

Ground vehicle platoons: aerodynamics and flow control: An experimental and computational investigation

Road transport contributes approximately 20% to the United Kingdom’s greenhouse gas emissions, accelerating the effects of global warming. Since the United Kingdom, like many other countries, has pledged to reach net zero carbon emissions over the next two decades, reducing emissions from road vehicles has become a priority. A further adverse effect of road vehicle emissions is their link to serious health issues such as respiratory and cardiovascular diseases. To achieve the required substantial reduction in emissions, a multi-faceted approach will be required. In this project, one important aspect, the aerodynamics of ground vehicle platoons, is explored with the aim of expanding the understanding of road vehicle aerodynamics and exploring innovative solutions to improve road vehicle efficiency. Vehicle platooning is a form of cooperative travelling in which vehicles drive closely together, with the intention to reduce overall air resistance, fuel consumption and vehicle emissions. Platooning, i.e., the cooperative movement of a group of individuals, is a concept that is not unique to road vehicles, but can be commonly observed in nature (e.g., a school of fish) or in sport (e.g., cyclists riding their bikes in a train). Here the trailing individuals take advantage of the sheltering provided by the leading individuals of the group. As a continuation of this observation, it would be natural to assume that road platooning is always beneficial, and that the trailing vehicles of a platoon reliably experience a reduction in drag. However, there are several examples in the literature that report a rear vehicle in a platoon receiving a drag increase. With the wide range of vehicle geometries on the roads, it is vital that additional research is targeted at understand the fundamental aerodynamic principles that lead to such adverse platooning results and understand the role that geometry plays in influencing the effectiveness of a platoon. In the first stage of this project, the geometry dependence of platooning was explored by systematically altering the shape of a simplified ground vehicle to change its platooning behaviour from the ‘classical’ platooning behaviour, where the rear vehicle experiences a high drag reduction, to ‘inverted’ platooning behaviour, where the rear vehicle suffers an increase in drag. To this end, a large parameter study was completed using unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations. A key outcome of this study was that the combination of a more streamlined rear vehicle, coupled with strong wake-impingement caused by the lead vehicle results in the most adverse platooning outcome. The second stage of the project focused on establishing the potential of using passive flow control to alleviate the adverse platooning effects that were observed in a platoon composed of two Ahmed bodies with 25◦ rear slant angles. First, the potential of plasma actuators as flow control devices was explored by experimentally characterising the performance of a serrated dielectric barrier discharge (DBD) plasma actuator. This was followed by another set of URANS simulations which considered the application of flow control in the context of a platoon of two 25◦ Ahmed vehicles. This covered both plasma-actuator like induced jets as well as flaps as flow control devices. The flow control devices were located at the top of the rear slant of the front vehicle and were designed to induce flow separation to increase the size of the front vehicle’s wake. Using this technique a drag reduction for the rear vehicle of up to 25% compared to the configuration without flow control was achieved. In the final stage, the effectiveness of flow control was tested experimentally in the University of Glasgow’s Handley-Page wind tunnel. First the dependency of the drag coefficient of a platoon composed of two 25◦ Ahmed vehicles on inter-vehicle spacing and Reynolds number was investigated, showing that a significant dependency on both parameters exists. Then, flow control was introduced in the form of a flap, with the previous sets of experiments being repeated for three flap angles and two flap lengths. While the flap was not quite as effective as predicted by the URANS simulations, the flap still induced a significant reduction in drag (ca. 9%) when compared to the rear vehicle of the baseline case that was subject to inverted platooning conditions

Controller with Vehicular Communication Design for Vehicular Platoon System

PhD ThesisTracked Electric Vehicles (TEV) which is a new mass-transport system. It aims to provide a safe, efficient and coordinated traffic system. In TEV, the inter-vehicular distance is reduced to only a quarter of the regular car length and where drive at 200km/h enabling mass transport at uniform speed. Under this requirement, the design of the controller is particularly important. This thesis first developed an innovative approach using adaptive Proportion, integral and derivation (PID) controller using fuzzy logic theory to keep variable time-gap between dynamic cars for platooning system with communication delay. The simulation results presented show a significant improvement in keeping time-gap variable between the cars enabling a safe and efficient flow of the platooning system. Secondly, this thesis investigates the use of Slide Mode Control (SMC) for TEV. It studies different V2V communication topology structures using graph theory and proposes a novel SMC design with and without global dynamic information. The Lyapunov candidate function was chosen to study the impact which forms an integral part for current and future research. The simulation results show that this novel SMC has a tolerance ability for communication delay. In order to present the real time TEV platoon system, a similar PI controller has been utilized in a novel automated vehicle, based on Raspberry Pi, multi-sensors and the designed Remote Control (RC) car. Thirdly, in order to obtain precise positioning information for vehicles in platoon system, this thesis describes Inertial Measurement Unit (IMU)/Global Navigation Satellite System (GNSS) data fusion to achieve a highly precise positioning solution. The results show that the following vehicles can reach the same velocity and acceleration as the leading vehicle in 5 seconds and the spacing error is less than 0.1m. The practical results are in line with those from the simulated experiment

Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop

Reliable localization methods for intelligent vehicles based on environment perception

Mención Internacional en el título de doctorIn the near past, we would see autonomous vehicles and Intelligent Transport Systems (ITS) as a potential future of transportation. Today, thanks to all the technological advances in recent years, the feasibility of such systems is no longer a question. Some of these autonomous driving technologies are already sharing our roads, and even commercial vehicles are including more Advanced Driver-Assistance Systems (ADAS) over the years. As a result, transportation is becoming more efficient and the roads are considerably safer. One of the fundamental pillars of an autonomous system is self-localization. An accurate and reliable estimation of the vehicle’s pose in the world is essential to navigation. Within the context of outdoor vehicles, the Global Navigation Satellite System (GNSS) is the predominant localization system. However, these systems are far from perfect, and their performance is degraded in environments with limited satellite visibility. Additionally, their dependence on the environment can make them unreliable if it were to change. Accordingly, the goal of this thesis is to exploit the perception of the environment to enhance localization systems in intelligent vehicles, with special attention to their reliability. To this end, this thesis presents several contributions: First, a study on exploiting 3D semantic information in LiDAR odometry is presented, providing interesting insights regarding the contribution to the odometry output of each type of element in the scene. The experimental results have been obtained using a public dataset and validated on a real-world platform. Second, a method to estimate the localization error using landmark detections is proposed, which is later on exploited by a landmark placement optimization algorithm. This method, which has been validated in a simulation environment, is able to determine a set of landmarks so the localization error never exceeds a predefined limit. Finally, a cooperative localization algorithm based on a Genetic Particle Filter is proposed to utilize vehicle detections in order to enhance the estimation provided by GNSS systems. Multiple experiments are carried out in different simulation environments to validate the proposed method.En un pasado no muy lejano, los vehículos autónomos y los Sistemas Inteligentes del Transporte (ITS) se veían como un futuro para el transporte con gran potencial. Hoy, gracias a todos los avances tecnológicos de los últimos años, la viabilidad de estos sistemas ha dejado de ser una incógnita. Algunas de estas tecnologías de conducción autónoma ya están compartiendo nuestras carreteras, e incluso los vehículos comerciales cada vez incluyen más Sistemas Avanzados de Asistencia a la Conducción (ADAS) con el paso de los años. Como resultado, el transporte es cada vez más eficiente y las carreteras son considerablemente más seguras. Uno de los pilares fundamentales de un sistema autónomo es la autolocalización. Una estimación precisa y fiable de la posición del vehículo en el mundo es esencial para la navegación. En el contexto de los vehículos circulando en exteriores, el Sistema Global de Navegación por Satélite (GNSS) es el sistema de localización predominante. Sin embargo, estos sistemas están lejos de ser perfectos, y su rendimiento se degrada en entornos donde la visibilidad de los satélites es limitada. Además, los cambios en el entorno pueden provocar cambios en la estimación, lo que los hace poco fiables en ciertas situaciones. Por ello, el objetivo de esta tesis es utilizar la percepción del entorno para mejorar los sistemas de localización en vehículos inteligentes, con una especial atención a la fiabilidad de estos sistemas. Para ello, esta tesis presenta varias aportaciones: En primer lugar, se presenta un estudio sobre cómo aprovechar la información semántica 3D en la odometría LiDAR, generando una base de conocimiento sobre la contribución de cada tipo de elemento del entorno a la salida de la odometría. Los resultados experimentales se han obtenido utilizando una base de datos pública y se han validado en una plataforma de conducción del mundo real. En segundo lugar, se propone un método para estimar el error de localización utilizando detecciones de puntos de referencia, que posteriormente es explotado por un algoritmo de optimización de posicionamiento de puntos de referencia. Este método, que ha sido validado en un entorno de simulación, es capaz de determinar un conjunto de puntos de referencia para el cual el error de localización nunca supere un límite previamente fijado. Por último, se propone un algoritmo de localización cooperativa basado en un Filtro Genético de Partículas para utilizar las detecciones de vehículos con el fin de mejorar la estimación proporcionada por los sistemas GNSS. El método propuesto ha sido validado mediante múltiples experimentos en diferentes entornos de simulación.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridSecretario: Joshué Manuel Pérez Rastelli.- Secretario: Jorge Villagrá Serrano.- Vocal: Enrique David Martí Muño