Adaptive and cooperative decision-making strategies for autonomous driving in urban environments

This thesis is framed within the area of intelligent control systems and automation. The main objective is the development of advanced strategies for decision making in automated vehicles, taking advantage of both the information available in the vehicle and V2X communications. The uncertainty inherent in the different sensors and V2X communications, as well as the variability of driving scenes in urban environments, make it necessary to design and develop a context-dependent adaptive control architecture. To this end, this doctoral thesis addresses the application of risk inference and motion planning systems capable of integrating uncertainty and heterogeneity in sensory information, while structurally incorporating the possibility of learning from complex and unpredictable situations. The cooperation between vehicles and with the infrastructure will be exploited in order to improve the safety of each vehicle, and in specific cases of complex resolution, with the aim of disbanding situations that today are unsolvable for an artificial decision system.Peer reviewe

Lateral control for autonomous vehicles: A comparative evaluation

The selection of an appropriate control strategy is essential for ensuring safe operation in autonomous driving. While numerous control strategies have been developed for specific driving scenarios, a comprehensive comparative assessment of their performance using the same tuning methodology is lacking in the literature. This paper addresses this gap by presenting a systematic evaluation of state-of-the-art model-free and model-based control strategies. The objective is to evaluate and contrast the performance of these controllers across a wide range of driving scenarios, reflecting the diverse needs of autonomous vehicles. To facilitate the comparative analysis, a comprehensive set of performance metrics is selected, encompassing accuracy, robustness, and comfort. The contributions of this research include the design of a systematic tuning methodology, the use of two novel metrics for stability and comfort comparisons and the evaluation through extensive simulations and real tests in an experimental instrumented vehicle over a wide range of trajectories.Comment: Video showcasing a real-world test of a model-free lateral controller in an automated vehicle: https://youtu.be/JtLfZzEdGC

Speed-Adaptive Model-Free Lateral Control for Automated Cars

In order to increase the number of situations in which an intelligent vehicle can operate without human intervention, lateral control is required to accurately guide it in a reference trajectory regardless of the shape of the road or the longitudinal speed. Some studies address this problem by tuning a controller for low and high speeds and including an output adaptation law. In this paper, a strategy framed in the Model-Free Control paradigm is presented to laterally control the vehicle over a wide speed range. Tracking quality, system stability and passenger comfort are thoroughly analyzed and compared to similar control structures. The results obtained both in simulation and with a real vehicle show that the developed strategy tracks a large number of trajectories with high degree of accuracy, safety and comfort.Comment: 8th Joint IFAC Conference: SSSC-TDS-LPV, September 27-30, 2022 -- Montreal, Canad

Decision-making strategies for automated driving in urban environments

La conducción automatizada está considerada como una de las tecnologías clave y uno de los principales avances tecnológicos que influyen y dan forma a nuestra movilidad y calidad de vida futuras. Tanto es así, que la mayor parte de la actividad actual de investigación y desarrollo en el campo de los Sistemas Inteligentes de Transporte (ITS) se centra en ella. La introducción de sistemas automáticos de control en vehículos puede mejorar enormemente la seguridad en la carretera, teniendo en cuenta que alrededor del 90% de los accidentes de tráfico son causados por errores humanos. En la actualidad, hay muchas soluciones, conocidas como sistemas avanzados de asistencia al conductor (ADAS), que son capaces de realizar diferentes tareas, como emitir alertas para advertir sobre posibles situaciones peligrosas, o incluso introducir control longitudinal y/o lateral en determinadas situaciones. Si bien se han realizado esfuerzos considerables en los ámbitos de la percepción y la localización, las representaciones digitales del entorno y la planificación en escenarios urbanos siguen siendo incompletas. Como resultado, comprender la relación espacio-temporal entre el vehículo en cuestión y las entidades pertinentes, a la vez que se encuentra limitado por la red de carreteras, es un reto muy difícil. Además, la planificación de movimiento se ve afectada significativamente en entornos urbanos, ya que el conocimiento sobre este entorno es generalmente incompleto y la incertidumbre asociada es alta. Asimismo, las predicciones de la ocupación futura de los vehículos cercanos deben influir de forma efectiva en la planificación de movimiento realizada por el vehículo. Con el objetivo de alcanzar el razonamiento abstracto a nivel humano y reaccionar con seguridad incluso en situaciones urbanas complejas, la conducción autónoma requiere métodos para generalizar situaciones impredecibles y razonar de forma oportuna. El creciente interés en niveles de automatización cada vez más altos conlleva el desarrollo de algoritmos capaces de reaccionar a los típicos escenarios de conducción de una manera segura y humana. En este sentido, esta tesis aborda el problema de la planificación de movimiento en entornos urbanos proponiendo una arquitectura de toma de decisiones y planificación con el objetivo de impulsar las capacidades de navegación de los vehículos automatizados, haciendo uso de mapas no detallados y libres. Para tal fin, el espacio navegable es obtenido dinámicamente a partir de la información de los mapas. Para hacer frente a su incertidumbre intrínseca y baja fidelidad, un sistema de detección de carriles basado en visión artificial actualiza y mejora dicho espacio navegable. A partir de ahí, un eficiente planificador local, que imita la conducción humana, determina una trayectoria segura, bajo un marco probabilístico. Un sistema de percepción basado en LiDAR es usado para identificar el escenario de conducción, y, eventualmente, replanificar la trayectoria, llevando en algunos casos a adaptar la ruta de alto nivel para alcanzar el destino dado. Las nuevas funcionalidades abarcadas por la arquitectura propuesta, presentan avances significativos con respecto a la arquitectura previa para conducción automatizada del Programa AUTOPIA, donde se ha desarrollado esta tesis. Además, con el fin de validar su diseño e implementación, la arquitectura ha sido ampliamente probada en uno de los vehículos prototipo de la flota de AUTOPIA en las instalaciones del Centro de Automática y Robótica (CAR) en Arganda del Rey, España. Numerosas pruebas realizadas en escenarios que imitan entornos urbanos reales y diferentes demostraciones prueban la robustez de la arquitectura propuesta al enfrentarse a diferentes situaciones complejas, tales como la evitación de obstáculos estáticos y dinámicos o el enrutamiento dinámico.Automated driving is considered to be one of the key technologies and major technological advances that influence and shape our future mobility and quality of life. So much so that most of the current activity in research and development in the field of Intelligent Transportation Systems (ITS) is focused on it. The introduction of automatic control systems in vehicles may greatly improve road safety, taking into account that around 90% of traffic crashes are caused by human errors. Nowadays, there are several solutions, known as advanced driver assistance systems (ADAS), that are able to perform different tasks such as providing warning signals to the driver, alerting about possible hazardous situations, or even introducing longitudinal and/or lateral control actions in specific cases. Whilst considerable efforts have been attained in the perception and localization domains, digital representations of the environment and planning in urban scenarios are still incomplete. As a result, understanding the spatio-temporal relationship between the subject vehicle and the relevant entities while being constrained by the road network is a very difficult challenge. Furthermore, urban motion planning is significantly affected since the knowledge about the environment is generally incomplete and the associated uncertainty is high. In addition, the predictions of future occupancy of nearby vehicles must effectively influence the motion plan calculated by the vehicle. With the aim of reaching human-level abstract reasoning and reacting safely even in complex urban situations, autonomous driving requires methods to generalize unpredictable situations and reason in a timely manner. The growing interest in ever higher levels of automation involves the development of algorithms capable of reacting to typical driving scenarios and making decisions to face increasingly complex driving situations in a safe and human-like manner. In this regard, this thesis addresses the problem of motion planning in urban environments by proposing a decision-making and planning architecture that aims at pushing the navigation capabilities of automated vehicles when only non detailed and open-source maps are available. For that purpose, road corridors are dynamically obtained from map information. To cope with their intrinsic uncertainty and low-fidelity, a camera-based lane detection system updates and enhances the navigable space. From that point, an efficient and human-like local planner determines, under a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations along it. LiDAR-based perception is then used to identify the driving scenario, and eventually re-plan the trajectory, leading in some cases to adapt the high-level route to reach the given destination. The new functionalities covered by the proposed architecture present significant advances with respect to the previous architecture for automated driving of the AUTOPIA Program, where this thesis has been developed. Furthermore, in order to validate its design and implementation, the architecture has been extensively tested in one of the prototype vehicles of the AUTOPIA’s fleet at the Centre for Automation and Robotics (CAR) facilities in Arganda del Rey, Spain. Extensive tests on real urban-like environments and different live demonstrations proved the robustness of the proposed architecture when dealing with different complex situations such as static and dynamic obstacle avoidance or dynamic rerouting.Peer reviewe

Consensus-Based Cooperative Control Approach Applied to Urban Traffic Networks

Nowadays many studies are conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is applied in order to provide solutions for mobility related problems in cities. The interconnected traffic lights controllers (TLC) network adapts traffic lights cycles, based on traffic and air pollution information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease of the pollution level. Then the problem becomes more complex. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach has been followed as design method and a distributed consensus-based control algorithm has been applied. The applied control law contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Furthermore, Discrete Event Systems Specification (DEVS) formalism is applied for modelling and simulation purpose. The proposed solution has been tested and validated in a simulated environment corroborating that the proposed solution is a powerful technique to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks

Real-Time Motion Planning Approach for Automated Driving in Urban Environments

Autonomous vehicles must be able to react in a timely manner to typical and unpredictable situations in urban scenarios. In this connection, motion planning algorithms play a key role as they are responsible of ensuring driving safety and comfort while producing human-like trajectories in a wide range of driving scenarios. Typical approaches for motion planning focus on trajectory optimization by applying computation-intensive algorithms, rather than finding a balance between optimatily and computing time. However, for on-road automated driving at medium and high speeds, determinism is necessary at high sampling rates. This work presents a trajectory planning algorithm that is able to provide safe, human-like and comfortable trajectories by using cost-effective primitives evaluation based on quintic Bézier curves. The proposed method is able to consider the kinodynamic constrains of the vehicle while reactively handling dynamic real environments in real-time. The proposed motion planning strategy has been implemented in a real experimental platform and validated in different real operating environments, successfully overcoming typical urban traffic scenes where both static and dynamic objects are involved.This work was supported in part by the Spanish Ministry of Science, Innovation and Universities with National Project COGDRIVE under Grant DPI2017-86915-C3-1-R, in part by the European Commission through the Projects PRYSTINE under Grant ECSEL-783190-2, in part by SECREDAS under Grant ECSEL-783119-2, and in part by the Community of Madrid through SEGVAUTO 4.0-CM Programme under Grant S2018-EMT-4362.Peer reviewe

A decision-making architecture for automated driving without detailed prior maps

Autonomous driving requires general methods to generalize unpredictable situations and reason in complex scenarios where safety is critical and the vehicle must react in a reliable manner. In this sense, digital maps are a crucial component for relating the location of the vehicle and identifying the different road features. In this work, we present a decision-making architecture which does not require detailed prior maps. Instead, OSM is used to plan a global route and an automatically generate driving corridors, which are adapted using a proposed vision-based algorithm. Moreover, a grid-based approach is also applied to consider the localization uncertainty. Those self-generated driving corridors are used by the local planner to plan the trajectories the vehicle will follow. Our approach integrates global, local and HMI components to provide the required functionalities for autonomous driving in a general manner.This work has been partially funded by the Spanish Ministry of Science, Innovation and Universities with National Project COGDRIVE (DPI2017- 86915-C3-1-R), and by the European Commission through the Project PRYSTINE (ECSEL-783190-2).Peer reviewe

Self-Generated OSM-Based Driving Corridors

Finding the boundaries of the drivable space is a key to the development of any advanced driver assistance systems with automated driving functions. A common approach found in the literature is to combine the information of digital maps with multiple on-board sensors for building a robust and accurate model of the environment from which to extract the navigable space. In this sense, the digital map is the crucial component for relating the location of the vehicle and identifying the different road features. This paper presents an automatic procedure for generating driving corridors from OpenStreetMap. The proposed method expands the original map representation, replacing polylines by polynomial-based roads, whose sections are defined using cubic Bézier curves. All curves are automatically adjusted from the original road description, thus generating an efficient and accurate road representation without human intervention. Finally, the driving corridors are generated as a concatenation of the modified road sections along a planned route. The proposed approach has been validated in a peri-urban environment, for which corridors where successfully generated in all cases.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness with National Project TCAP-AUTO under Grant RTC-2015-3942-4, in part by the Spanish Ministry of Science, Innovation and Universities with National Project COGDRIVE under Grant DPI2017-86915-C3-1-R, and in part by the European Commission through the Project PRYSTINE under Grant ECSEL-783190-2. The work of J. Godoy was supported by the Juan de la Cierva Fellowship Program through the Spanish Ministry of Economy, Industry and Competitiveness.Peer reviewe

Decision-making strategies for automated driving in urban environments

La conducción automatizada está considerada como una de las tecnologías clave y uno de los principales avances tecnológicos que influyen y dan forma a nuestra movilidad y calidad de vida futuras. Tanto es así, que la mayor parte de la actividad actual de investigación y desarrollo en el campo de los Sistemas Inteligentes de Transporte (ITS) se centra en ella. La introducción de sistemas automáticos de control en vehículos puede mejorar enormemente la seguridad en la carretera, teniendo en cuenta que alrededor del 90% de los accidentes de tráfico son causados por errores humanos. En la actualidad, hay muchas soluciones, conocidas como sistemas avanzados de asistencia al conductor (ADAS), que son capaces de realizar diferentes tareas, como emitir alertas para advertir sobre posibles situaciones peligrosas, o incluso introducir control longitudinal y/o lateral en determinadas situaciones. Si bien se han realizado esfuerzos considerables en los ámbitos de la percepción y la localización, las representaciones digitales del entorno y la planificación en escenarios urbanos siguen siendo incompletas. Como resultado, comprender la relación espacio-temporal entre el vehículo en cuestión y las entidades pertinentes, a la vez que se encuentra limitado por la red de carreteras, es un reto muy difícil. Además, la planificación de movimiento se ve afectada significativamente en entornos urbanos, ya que el conocimiento sobre este entorno es generalmente incompleto y la incertidumbre asociada es alta. Asimismo, las predicciones de la ocupación futura de los vehículos cercanos deben influir de forma efectiva en la planificación de movimiento realizada por el vehículo. Con el objetivo de alcanzar el razonamiento abstracto a nivel humano y reaccionar con seguridad incluso en situaciones urbanas complejas, la conducción autónoma requiere métodos para generalizar situaciones impredecibles y razonar de forma oportuna. El creciente interés en niveles de automatización cada vez más altos conlleva el desarrollo de algoritmos capaces de reaccionar a los típicos escenarios de conducción de una manera segura y humana. En este sentido, esta tesis aborda el problema de la planificación de movimiento en entornos urbanos proponiendo una arquitectura de toma de decisiones y planificación con el objetivo de impulsar las capacidades de navegación de los vehículos automatizados, haciendo uso de mapas no detallados y libres. Para tal fin, el espacio navegable es obtenido dinámicamente a partir de la información de los mapas. Para hacer frente a su incertidumbre intrínseca y baja fidelidad, un sistema de detección de carriles basado en visión artificial actualiza y mejora dicho espacio navegable. A partir de ahí, un eficiente planificador local, que imita la conducción humana, determina una trayectoria segura, bajo un marco probabilístico. Un sistema de percepción basado en LiDAR es usado para identificar el escenario de conducción, y, eventualmente, replanificar la trayectoria, llevando en algunos casos a adaptar la ruta de alto nivel para alcanzar el destino dado. Las nuevas funcionalidades abarcadas por la arquitectura propuesta, presentan avances significativos con respecto a la arquitectura previa para conducción automatizada del Programa AUTOPIA, donde se ha desarrollado esta tesis. Además, con el fin de validar su diseño e implementación, la arquitectura ha sido ampliamente probada en uno de los vehículos prototipo de la flota de AUTOPIA en las instalaciones del Centro de Automática y Robótica (CAR) en Arganda del Rey, España. Numerosas pruebas realizadas en escenarios que imitan entornos urbanos reales y diferentes demostraciones prueban la robustez de la arquitectura propuesta al enfrentarse a diferentes situaciones complejas, tales como la evitación de obstáculos estáticos y dinámicos o el enrutamiento dinámico. ----------ABSTRACT---------- Automated driving is considered to be one of the key technologies and major technological advances that influence and shape our future mobility and quality of life. So much so that most of the current activity in research and development in the field of Intelligent Transportation Systems (ITS) is focused on it. The introduction of automatic control systems in vehicles may greatly improve road safety, taking into account that around 90% of traffic crashes are caused by human errors. Nowadays, there are several solutions, known as advanced driver assistance systems (ADAS), that are able to perform different tasks such as providing warning signals to the driver, alerting about possible hazardous situations, or even introducing longitudinal and/or lateral control actions in specific cases. Whilst considerable efforts have been attained in the perception and localization domains, digital representations of the environment and planning in urban scenarios are still incomplete. As a result, understanding the spatio-temporal relationship between the subject vehicle and the relevant entities while being constrained by the road network is a very difficult challenge. Furthermore, urban motion planning is significantly affected since the knowledge about the environment is generally incomplete and the associated uncertainty is high. In addition, the predictions of future occupancy of nearby vehicles must effectively influence the motion plan calculated by the vehicle. With the aim of reaching human-level abstract reasoning and reacting safely even in complex urban situations, autonomous driving requires methods to generalize unpredictable situations and reason in a timely manner. The growing interest in ever higher levels of automation involves the development of algorithms capable of reacting to typical driving scenarios and making decisions to face increasingly complex driving situations in a safe and human-like manner. In this regard, this thesis addresses the problem of motion planning in urban environments by proposing a decision-making and planning architecture that aims at pushing the navigation capabilities of automated vehicles when only non detailed and open-source maps are available. For that purpose, road corridors are dynamically obtained from map information. To cope with their intrinsic uncertainty and low-fidelity, a camera-based lane detection system updates and enhances the navigable space. From that point, an efficient and human-like local planner determines, under a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations along it. LiDAR-based perception is then used to identify the driving scenario, and eventually re-plan the trajectory, leading in some cases to adapt the high-level route to reach the given destination. The new functionalities covered by the proposed architecture present significant advances with respect to the previous architecture for automated driving of the AUTOPIA Program, where this thesis has been developed. Furthermore, in order to validate its design and implementation, the architecture has been extensively tested in one of the prototype vehicles of the AUTOPIA’s fleet at the Centre for Automation and Robotics (CAR) facilities in Arganda del Rey, Spain. Extensive tests on real urban-like environments and different live demonstrations proved the robustness of the proposed architecture when dealing with different complex situations such as static and dynamic obstacle avoidance or dynamic rerouting