5,143 research outputs found
Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems
World-wide injuries in vehicle accidents have been on the rise in recent
years, mainly due to driver error. The main objective of this research is to
develop a predictive system for driving maneuvers by analyzing the cognitive
behavior (cephalo-ocular) and the driving behavior of the driver (how the vehicle
is being driven). Advanced Driving Assistance Systems (ADAS) include
different driving functions, such as vehicle parking, lane departure warning,
blind spot detection, and so on. While much research has been performed on
developing automated co-driver systems, little attention has been paid to the
fact that the driver plays an important role in driving events. Therefore, it
is crucial to monitor events and factors that directly concern the driver. As
a goal, we perform a quantitative and qualitative analysis of driver behavior
to find its relationship with driver intentionality and driving-related actions.
We have designed and developed an instrumented vehicle (RoadLAB) that is
able to record several synchronized streams of data, including the surrounding
environment of the driver, vehicle functions and driver cephalo-ocular behavior,
such as gaze/head information. We subsequently analyze and study the
behavior of several drivers to find out if there is a meaningful relation between
driver behavior and the next driving maneuver
Overview of Environment Perception for Intelligent Vehicles
This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The
state-of-the-art algorithms and modeling methods for intelligent
vehicles are given, with a summary of their pros and cons. A
special attention is paid to methods for lane and road detection,
traffic sign recognition, vehicle tracking, behavior analysis, and
scene understanding. In addition, we provide information about
datasets, common performance analysis, and perspectives on
future research directions in this area
Proactive Evaluation of Traffic Signs Using a Traffic Sign Simulator
Traffic signs and pavement markings are a crucial aspect of road design since they are essential sources of information for road users to calibrate their driving behavior, evaluate route possibilities and cope with unexpected events. A proactive evaluation of (the quality of) these road design elements will help to improve the safety performance of the roadway. This paper presents the Traffic Sign Simulator, an innovative research tool to study the influence of these elements on road users’ routing decisions, lane choice and visual behavior, to investigate road users’ comprehension of these signs, and to collect suggestions for improvements. Using a driving simulator mock-up, participants navigate through a full HD video from route(s) in which the planned traffic signs have been digitally implemented using specialized software for camera-tracking and 3D video-integration. Participants’ route and lane choice and their visual behavior (using eye tracking) are monitored while driving through the scenario(s). Laptop preand post-tests are applied to collect additional in-depth information concerning the participants’ processing, comprehension and general evaluation of the traffic signs and suggestions for improvement. The paper illustrates the possibilities of the Traffic Sign Simulator with a case study that examined the effectiveness of temporary work zone signalization (i.e., traffic signs, digital information panels and pavement markings) as it was used during the reconstruction works on the Vilvoorde fly-over near Brussels, one of the busiest interchanges in the Belgian motorway network
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 vehÃculos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logÃstica de mercancÃas 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 tecnologÃas de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehÃculo y la estimación de parámetros. Además, las tecnologÃas en el vehÃculo 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 tecnologÃas de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehÃculos 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 vehÃculos, generando un cambio significativo en la movilidad, que afectará profundamente el modo de vida de las personas, la logÃstica de mercancÃas 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 tecnologÃas de seguimiento de trayectorias son habilitadores esenciales, se deben aplicar verificaciones exhaustivas en aplicaciones relacionadas como el control de movimiento del vehÃculo y la estimación de parámetros. Además, las tecnologÃas en el vehÃculo 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 tecnologÃas de alta automatización de conducción. Luego, se propone un método de dos pasos que aborda la validación de ambos modelos de vehÃculos 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
Road Infrastructure Challenges Faced by Automated Driving: A Review
Automated driving can no longer be referred to as hype or science fiction but rather a technology that has been gradually introduced to the market. The recent activities of regulatory bodies and the market penetration of automated driving systems (ADS) demonstrate that society is exhibiting increasing interest in this field and gradually accepting new methods of transport. Automated driving, however, does not depend solely on the advances of onboard sensor technology or artificial intelligence (AI). One of the essential factors in achieving trust and safety in automated driving is road infrastructure, which requires careful consideration. Historically, the development of road infrastructure has been guided by human perception, but today we are at a turning point at which this perspective is not sufficient. In this study, we review the limitations and advances made in the state of the art of automated driving technology with respect to road infrastructure in order to identify gaps that are essential for bridging the transition from human control to self-driving. The main findings of this study are grouped into the following five clusters, characterised according to challenges that must be faced in order to cope with future mobility: international harmonisation of traffic signs and road markings, revision of the maintenance of the road infrastructure, review of common design patterns, digitalisation of road networks, and interdisciplinarity. The main contribution of this study is the provision of a clear and concise overview of the interaction between road infrastructure and ADS as well as the support of international activities to define the requirements of road infrastructure for the successful deployment of ADS
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