Reaktion menschlicher (Mit-)Fahrer auf hochautomatisierte Fahrzeuge im Mischverkehr auf der Autobahn und im urbanen Raum

In the future, highly automated vehicles will be introduced in road traffic, first on highways, and later in urban areas. The introduction will result in a long transition phase with mixed traffic. This transition phase poses new challenges for humans as passengers inside highly automated vehicles and for humans as drivers interacting with these vehicles in mixed traffic. Thus far, human drivers lack experience with highly automated vehicles and driving in mixed traffic. In addition, it can be expected that highly automated vehicles will drive in a more rule-compliant and defensive way than human drivers. This may cause conflicts with human drivers in mixed traffic, and lead to passenger discomfort and perceived risk. This dissertation investigated how humans react to highly automated vehicles in mixed traffic, taking both inside perspective of passengers and the outside perspective of human drivers into account. To this end, four psychological experiments. From the outside perspective, this dissertation investigated human drivers’ first contact with highly automated vehicles in dyadic interactions on the highway (Study 1) and repeated contact on longer highway sections (Study 2). Results showed that human drivers rate the rule-compliant automated driving behavior as pleasant and safe in dyadic interactions. However, human drivers feel slowed down by preceding highly automated vehicles on longer stretches of highway, which can be a potential hazard. Furthermore, an external labelling of highly automated vehicles may be recommendable in the long run. From the inside perspective of passengers, this dissertation investigated urban mixed traffic interactions with cyclists and pedestrians in longitudinal traffic (Study 3) and at a junction (Study 4). Results show that passengers do not accept any risk during highly automated driving and passengers want an early behavioral reaction of the highly automated vehicle to vulnerable road users in the driving environment. Across the four studies, the present dissertation shows that highly automated vehicles drive noticeably differently, which both passengers and manual drivers can perceive. However, highly automated driving behavior is perceived as unpleasant at maximum, but not as dangerous. When designing highly automated driving functions, both driver and passenger preferences should be considered equally. Future studies should examine the preferences of human road users regarding automated driving behavior.In Zukunft werden hochautomatisierte Fahrzeuge im Straßenverkehr eingeführt, zunächst auf der Autobahn, und später auch im urbanen Raum. Die Einführung dieser Technologie resultiert in einer langen Übergangsphase mit Mischverkehr. Dieser Übergang stellt Menschen als Passagiere und Fahrer vor neue Herausforderungen. Bislang fehlt Fahrern die Erfahrung mit hochautomatisierten Fahrzeugen und dem Fahren im Mischverkehr. Zudem ist zu erwarten, dass sich hochautomatisierte Fahrzeuge regelkonformer und defensiver fahren als menschliche Fahrer. Das könnte zu Konflikten mit anderen Verkehrsteilnehmern, und zu Diskomfort und Risikoerleben beim Passagier führen. Diese Dissertation untersuchte mithilfe von psychologischen Experimenten wie menschliche Fahrer auf hochautomatisierte Systeme aus der Passagiersicht und aus der Außensicht als manuelle Fahrer im Mischverkehr reagieren. Ein weiteres Ziel war es zu verstehen, wie hochautomatisierte Fahrzeuge fahren sollen, damit sich Passagiere sicher fühlen. Aus der Außensicht menschlicher Fahrer untersuchte diese Dissertation den Erstkontakt mit hochautomatisierten Fahrzeugen in dyadischen Interaktionen (Studie 1) und im wiederholten Kontakt (Studie 2) auf längeren Autobahnabschnitten. Die Ergebnisse zeigen, dass Fahrer das regelkonforme hochautomatisierte Fahrverhalten in dyadischen Interaktionen als angenehm und sicher bewerten. Allerdings fühlen sich Fahrer auf längeren Strecken ausgebremst, wodurch ein Gefahrenpotenzial entsteht kann. Weiterhin ist eine Außenkennzeichnung auf längere Sicht zu empfehlen. Aus der Passagiersicht untersuchte diese Dissertation urbane Mischverkehrsinteraktion im longitudinalen Verkehr (Studie 3) und an einer Einmündung (Studie 4). Die Ergebnisse zeigen, dass Passagiere keinerlei Risiko eingehen wollen und sich eine frühzeitige Verhaltensreaktion des hochautomatisierten Fahrzeugs auf schwächere Verkehrsteilnehmer in die Fahrumgebung wünschen. Studienübergreifend zeigt sich, dass hochautomatisierte Fahrzeuge merklich anders fahren, was Passagiere als auch für manuelle Fahrer wahrnehmen können. Automatisiertes Fahrverhalten wird aber maximal als unangenehm, nicht als gefährlich bewertet. Bei der technischen Auslegung automatisierter Fahrfunktionen sollten die Präferenzen von Fahrern und Passagieren gleichermaßen berücksichtigt werden. Zukünftige Studien sollten die Präferenzen anderer menschlicher Verkehrsteilnehmer im Hinblick auf das Verhalten automatisierter Fahrzeuge weiter untersuchen

Shared control strategies for automated vehicles

188 p.Los vehículos automatizados (AVs) han surgido como una solución tecnológica para compensar las deficiencias de la conducción manual. Sin embargo, esta tecnología aún no está lo suficientemente madura para reemplazar completamente al conductor, ya que esto plantea problemas técnicos, sociales y legales. Sin embargo, los accidentes siguen ocurriendo y se necesitan nuevas soluciones tecnológicas para mejorar la seguridad vial. En este contexto, el enfoque de control compartido, en el que el conductor permanece en el bucle de control y, junto con la automatización, forma un equipo bien coordinado que colabora continuamente en los niveles táctico y de control de la tarea de conducción, es una solución prometedora para mejorar el rendimiento de la conducción manual aprovechando los últimos avances en tecnología de conducción automatizada. Esta estrategia tiene como objetivo promover el desarrollo de sistemas de asistencia al conductor más avanzados y con mayor grade de cooperatición en comparación con los disponibles en los vehículos comerciales. En este sentido, los vehículos automatizados serán los supervisores que necesitan los conductores, y no al revés. La presente tesis aborda en profundidad el tema del control compartido en vehículos automatizados, tanto desde una perspectiva teórica como práctica. En primer lugar, se proporciona una revisión exhaustiva del estado del arte para brindar una descripción general de los conceptos y aplicaciones en los que los investigadores han estado trabajando durante lasúltimas dos décadas. Luego, se adopta un enfoque práctico mediante el desarrollo de un controlador para ayudar al conductor en el control lateral del vehículo. Este controlador y su sistema de toma de decisiones asociado (Módulo de Arbitraje) se integrarán en el marco general de conducción automatizada y se validarán en una plataforma de simulación con conductores reales. Finalmente, el controlador desarrollado se aplica a dos sistemas. El primero para asistir a un conductor distraído y el otro en la implementación de una función de seguridad para realizar maniobras de adelantamiento en carreteras de doble sentido. Al finalizar, se presentan las conclusiones más relevantes y las perspectivas de investigación futuras para el control compartido en la conducción automatizada

A DATA-DRIVEN APPROACH TO SUPPORTING USERS’ ADAPTATION TO SMART IN-VEHICLE SYSTEMS

The utilization of data to understand user behavior and support user needs began to develop in areas such as internet services, smartphone apps development, and the gaming industry. This bloom of data-driven services and applications forced OEMs to consider possible solutions for better in-vehicle connectivity. However, digital transformation in the automotive sector presents numerous challenges. One of those challenges is identifying and establishing the relevant user-related data that will cover current and future needs to help the automotive industry cope with the digital transformation pace. At the same time, this development should not be sporadic, without a clear purpose or vision of how newly-generated data can support engineers to create better systems for drivers. The important issue is to learn how to extract the knowledge from the immense data we possess, and to understand the extent to which this data can be used.Another challenge is the lack of established approaches towards vehicle data utilization for user-related studies. This area is relatively new to the automotive industry. Despite the positive examples from other fields that demonstrate the potential for data-driven context-aware applications, automotive practices still have gaps in capturing the driving context and driver behavior. This lack of user-related data can partially be explained by the multitasking activities that the driver performs while driving the car and the higher complexity of the automotive context compared to other domains. Thus, more research is needed to explore the capacity of vehicle data to support users in different tasks.Considering all the interrelations between the driver and in-vehicle system in the defined context of use helps to obtain more comprehensive information and better understand how the system under evaluation can be improved to meet driver needs. Tracking driver behavior with the help of vehicle data may provide developers with quick and reliable user feedback on how drivers are using the system. Compared to vehicle data, the driver’s feedback is often incomplete and perception-based since the driver cannot always correlate his behavior to complex processes of vehicle performance or clearly remember the context conditions. Thus, this research aims to demonstrate the ability of vehicle data to support product design and evaluation processes with data-driven automated user insights. This research does not disregard the driver’s qualitative input as unimportant but provides insights into how to better combine quantitative and qualitative methods for more effective results.According to the aim, the research focuses on three main aspects:•\ua0\ua0\ua0\ua0\ua0 Identifying the extent to which vehicle data can contribute to driver behavior understanding.\ua0 •\ua0\ua0\ua0\ua0\ua0 Expanding the concepts for vehicle data utilization to support drivers.•\ua0\ua0\ua0\ua0\ua0 Developing the methodology for a more effective combination of quantitative (vehicle data-based) and qualitative (based on users’ feedback) studies. Additionally, special consideration is given to describing the drawbacks and limitations, to enhance future data-driven applications

Defining procedures and simulation tools to test high levels of automation for cars in realistic traffic, driving and boundary conditions

Il crescente livello di automazione nella guida dei veicoli su gomma rende sempre più complesse e articolate le procedure di testing e validazione dei dispositivi. La tendenza alla realizzazione di sistemi che sostituiscano il guidatore in tutto o in parte, determina un cambiamento paradigmatico nell'ambito della validazione, la quale non può più occuparsi esclusivamente del test del corretto funzionamento del dispositivo da validare, ma dovrà testare le logiche di guida e le "scelte" che opera al variare dei contesti. Come ampiamente evidenziato nella letteratura scientifica di settore1 i processi di validazione rappresenteranno il più grande ostacolo alla realizzazione e messa in produzione dei sistemi di quarto e quinto livello SAE2 di automazione. Numerose ricerche hanno dimostrato3 che il testing su strada non rappresenta una soluzione che possa dare risultati attendibili in tempi sufficientemente brevi, ma a tutt'oggi non esistono software sufficientemente complessi da realizzare simulazioni che tengano conto di tutte le variabili necessarie. La ricerca intende definire le corrette procedure di testing di veicoli ad elevato grado di automazione in condizioni di traffico realistiche, avvalendosi di software di simulazione specifici di ogni settore coinvolto nel processo, realizzando uno strumento di testing integrato sufficientemente efficace

Safety problems in urban cycling mobility. A quantitative risk analysis at urban intersections

The attention to the most vulnerable road users has grown rapidly in recent decades. The experience gained reveals an important number of cyclist fatalities due to road crashes; most of which occur at intersections. In this study, dispersion of trajectories in urban intersections has been considered to identify the whole conflict area and the largest conflict areas between cars and bicycles, and the speeds have been used to calculate exposure time of cyclists and reaction time available to drivers to avoid collision. These data allow the summary approach to the problem, while a risk probability model has been developed to adopt an elementary approach analysis. A quantitative damage model has been proposed to classify each conflict point, and a probabilistic approach has been defined to consider the traffic volume and the elementary unit of exposure. The combination of damage and probability, permitted to assess the risk of crash, at the examined intersection. Three types of urban four-arm intersection, with and without bike paths, were considered. For each scheme, the authors assessed the risk of collision between the cyclist and the vehicle. The obtained results allowed the identification of the most hazardous maneuvers and highlighted that geometry and kinematics of traffic movements cannot be overlooked, when designing an urban road intersection. The strategy proposed by the authors could have a significant impact on the risk management of urban intersections. The obtained results and the proposed hazard estimation methodology could be used to design safer intersections

Young drivers’ pedestrian anti-collision braking operation data modelling for ADAS development

Smart cities and smart mobility come from intelligent systems designed by humans. Artificial Intelligence (AI) is contributing significantly to the development of these systems, and the automotive industry is the most prominent example of "smart" technology entering the market: there are Advanced Driver Assistance System (ADAS), Radar/LIDAR detection units and camera-based Computer Vision systems that can assess driving conditions. Actually, these technologies have become consumer goods and services in mass-produced vehicles to provide human drivers with tools for a more comfortable and safer driving. Nevertheless, they need to be further improved for progress in the transition to fully automated driving or simply to increase vehicle automation levels. To this end, it becomes imperative to accurately predict driver’s decisions, model human driving behaviors, and introduce more accurate risk assessment metrics. This paper presents a system that can learn to predict the future braking behavior of a driver in a typically urban vehicle-pedestrian conflict, i.e., when a pedestrian enters a zebra crossing from the curb and a vehicle is approaching. The algorithm proposes a sequential prediction of relevant operational indicators that continuously describe the encounter process. A car driving simulator was used to collect reliable data on braking behaviours of a cohort of 68 licensed university students, who faced the same urban scenario. The vehicle speed, steering wheel angle, and pedal activity were recorded as the participants approached the crosswalk, along with the azimuth angle of the pedestrian and the relative longitudinal distance between the vehicle and the pedestrian: the proposed system employs the vehicle information as human driving decisions and the pedestrian information as explanatory variables of the environmental state. In fact, the pedestrian’s polar coordinates are usually calculated by an on-board millimeter-wave radar which is typically used to perceive the environment around a vehicle. All mentioned information is represented in the form of time series data and is used to train a recurrent neural network in a supervised machine learning process. The main purpose of this research is to define a system of behavioral profiles in non-collision conditions that could be used for enhancing the existing intelligent driving systems, e.g., to reduce the number of warnings when the driver is not on a collision course with a pedestrian. Preliminary experiments reveal the feasibility of the proposed system

Fully automated urban traffic system

The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible

Design and validation of decision and control systems in automated driving

xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo

Design and validation of decision and control systems in automated driving

xxvi, 148 p.En la última década ha surgido una tendencia creciente hacia la automatización de los vehículos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logística de mercancías y otros sectores dependientes del transporte. En el desarrollo de la conducción automatizada en entornos estructurados, la seguridad y el confort, como parte de las nuevas funcionalidades de la conducción, aún no se describen de forma estandarizada. Dado que los métodos de prueba utilizan cada vez más las técnicas de simulación, los desarrollos existentes deben adaptarse a este proceso. Por ejemplo, dado que las tecnologías de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehículo y la estimación de parámetros. Además, las tecnologías en el vehículo deben ser lo suficientemente robustas para cumplir con los requisitos de seguridad, mejorando la redundancia y respaldar una operación a prueba de fallos. Considerando las premisas mencionadas, esta Tesis Doctoral tiene como objetivo el diseño y la implementación de un marco para lograr Sistemas de Conducción Automatizados (ADS) considerando aspectos cruciales, como la ejecución en tiempo real, la robustez, el rango operativo y el ajuste sencillo de parámetros. Para desarrollar las aportaciones relacionadas con este trabajo, se lleva a cabo un estudio del estado del arte actual en tecnologías de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehículos de simulación y ADS. Se introducen nuevas formulaciones predictivas basadas en modelos para mejorar la seguridad y el confort en el proceso de seguimiento de trayectorias. Por último, se evalúan escenarios de mal funcionamiento para mejorar la seguridad en entornos urbanos, proponiendo una estrategia alternativa de estimación de posicionamiento para minimizar las condiciones de riesgo