1,227 research outputs found
Sensor fusion in driving assistance systems
Mención Internacional en el título de doctorLa vida diaria en los países desarrollados y en vías de desarrollo depende en
gran medida del transporte urbano y en carretera. Esta actividad supone un
coste importante para sus usuarios activos y pasivos en términos de polución
y accidentes, muy habitualmente debidos al factor humano. Los nuevos desarrollos
en seguridad y asistencia a la conducción, llamados Advanced Driving
Assistance Systems (ADAS), buscan mejorar la seguridad en el transporte, y
a medio plazo, llegar a la conducción autónoma.
Los ADAS, al igual que la conducción humana, están basados en sensores
que proporcionan información acerca del entorno, y la fiabilidad de los sensores
es crucial para las aplicaciones ADAS al igual que las capacidades
sensoriales lo son para la conducción humana. Una de las formas de aumentar
la fiabilidad de los sensores es el uso de la Fusión Sensorial, desarrollando
nuevas estrategias para el modelado del entorno de conducción gracias al uso
de diversos sensores, y obteniendo una información mejorada a partid de los
datos disponibles.
La presente tesis pretende ofrecer una solución novedosa para la detección
y clasificación de obstáculos en aplicaciones de automoción, usando fusión
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sensorial con dos sensores ampliamente disponibles en el mercado: la cámara
de espectro visible y el escáner láser. Cámaras y láseres son sensores
comúnmente usados en la literatura científica, cada vez más accesibles y listos
para ser empleados en aplicaciones reales. La solución propuesta permite la
detección y clasificación de algunos de los obstáculos comúnmente presentes
en la vía, como son ciclistas y peatones.
En esta tesis se han explorado novedosos enfoques para la detección y clasificación,
desde la clasificación empleando clusters de nubes de puntos obtenidas
desde el escáner láser, hasta las técnicas de domain adaptation para la creación
de bases de datos de imágenes sintéticas, pasando por la extracción inteligente
de clusters y la detección y eliminación del suelo en nubes de puntos.Life in developed and developing countries is highly dependent on road and
urban motor transport. This activity involves a high cost for its active and passive
users in terms of pollution and accidents, which are largely attributable to
the human factor. New developments in safety and driving assistance, called
Advanced Driving Assistance Systems (ADAS), are intended to improve
security in transportation, and, in the mid-term, lead to autonomous driving.
ADAS, like the human driving, are based on sensors, which provide information
about the environment, and sensors’ reliability is crucial for ADAS
applications in the same way the sensing abilities are crucial for human driving.
One of the ways to improve reliability for sensors is the use of Sensor
Fusion, developing novel strategies for environment modeling with the help of
several sensors and obtaining an enhanced information from the combination
of the available data.
The present thesis is intended to offer a novel solution for obstacle detection
and classification in automotive applications using sensor fusion with two
highly available sensors in the market: visible spectrum camera and laser
scanner. Cameras and lasers are commonly used sensors in the scientific
literature, increasingly affordable and ready to be deployed in real world
applications. The solution proposed provides obstacle detection and classification
for some obstacles commonly present in the road, such as pedestrians and bicycles.
Novel approaches for detection and classification have been explored in this
thesis, from point cloud clustering classification for laser scanner, to domain
adaptation techniques for synthetic dataset creation, and including intelligent
clustering extraction and ground detection and removal from point clouds.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Cristina Olaverri Monreal.- Secretario: Arturo de la Escalera Hueso.- Vocal: José Eugenio Naranjo Hernánde
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Predicting the future location of vehicles is essential for safety-critical
applications such as advanced driver assistance systems (ADAS) and autonomous
driving. This paper introduces a novel approach to simultaneously predict both
the location and scale of target vehicles in the first-person (egocentric) view
of an ego-vehicle. We present a multi-stream recurrent neural network (RNN)
encoder-decoder model that separately captures both object location and scale
and pixel-level observations for future vehicle localization. We show that
incorporating dense optical flow improves prediction results significantly
since it captures information about motion as well as appearance change. We
also find that explicitly modeling future motion of the ego-vehicle improves
the prediction accuracy, which could be especially beneficial in intelligent
and automated vehicles that have motion planning capability. To evaluate the
performance of our approach, we present a new dataset of first-person videos
collected from a variety of scenarios at road intersections, which are
particularly challenging moments for prediction because vehicle trajectories
are diverse and dynamic.Comment: To appear on ICRA 201
Smart driving assistance systems : designing and evaluating ecological and conventional displays
In-vehicle information systems have been shown to increase driver workload and cause distraction;
both are causal factors for accidents. This simulator study evaluates the impact that two designs for
a smart driving aid and scenario complexity has on workload, distraction and driving performance.
Results showed that real-time delivery of smart driving information did not increase driver workload
or adversely affect driver distraction, while having the effect of decreasing mean driving speed
in both the simple and complex driving scenarios. Important differences were also highlighted
between conventional and ecologically designed smart driving interfaces with respect to subjective
workload and peripheral detection
A new model-free design for vehicle control and its validation through an advanced simulation platform
A new model-free setting and the corresponding "intelligent" P and PD
controllers are employed for the longitudinal and lateral motions of a vehicle.
This new approach has been developed and used in order to ensure simultaneously
a best profile tracking for the longitudinal and lateral behaviors. The
longitudinal speed and the derivative of the lateral deviation, on one hand,
the driving/braking torque and the steering angle, on the other hand, are
respectively the output and the input variables. Let us emphasize that a "good"
mathematical modeling, which is quite difficult, if not impossible to obtain,
is not needed for such a design. An important part of this publication is
focused on the presentation of simulation results with actual and virtual data.
The actual data, used in Matlab as reference trajectories, have been obtained
from a properly instrumented car (Peugeot 406). Other virtual sets of data have
been generated through the interconnected platform SiVIC/RTMaps. It is a
dedicated virtual simulation platform for prototyping and validation of
advanced driving assistance systems. Keywords- Longitudinal and lateral vehicle
control, model-free control, intelligent P controller (i-P controller),
algebraic estimation, ADAS (Advanced Driving Assistance Systems).Comment: in 14th European Control Conference, Jul 2015, Linz, Austria. 201
Evaluation of Driving-Assistance Systems Based on Drivers\u27 Workload
This paper describes an experimental study concerning an evaluation of advanced driving-assistance systems using methods for estimating workload levels. The effects of such systems on drivers’ mental workload and driving performance were measured experimentally using the driving simulator. Six subjects were instructed to drive the simulator in a highway environment with and without Adaptive Cruise Control (ACC) and/or the collision-warning system (CWS). To assess the effectiveness of these systems on drivers’ performance, the subjects were asked to calculate sums of single- or double-digit figures displayed. The results show that higher accuracy was obtained under a condition with ACC than without it. To estimate the subjects’ mental workload levels, their electrocardiograms and respiration data were recorded during the sessions and the RRI, heart rate variance and respiration frequency were calculated. The results indicate that the provision of the CWS and ACC reduced the subjects’ mental workload compared with the situation without the systems
A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision
Advanced driving assistance systems (ADAS) form a complex multidisciplinary research field, aimed at improving traffic efficiency and safety. A realistic analysis of the requirements and of the possibilities of the traffic environment leads to the establishment of several goals for traffic assistance, to be implemented in the near future (ADASE, INVENT
Incorporating driver preferences Into eco-driving assistance systems using optimal control
Recently there have been several proposals for ‘ecodriving assistance systems’, designed to save fuel or electrical power by encouraging behaviours such as gentle acceleration and coasting to a stop. These systems use optimal control to find driving behaviour that minimises vehicle energy losses. In this paper, we introduce a methodology to account for driver preferences on acceleration, braking, following distances and cornering speed in such eco-driving optimal control problems. This consists of an optimal control model of acceleration and braking behaviour containing several physically-meaningful parameters to describe driver preferences. If used in combination with a model of fuel or energy consumption, this can provide an adjustable trade-off between satisfying those preferences and minimising energy losses. We demonstrate that the model gives comparable performance to existing car-following and cornering models when predicting drivers’ speed in these situations by comparison with real-world driving data. Finally, we present an example highway braking scenario for an electric vehicle, illustrating a trade-off between satisfying driver preferences on vehicle speed and acceleration and reducing electrical energy usage by up to 43%</div
Brook Auto: High-Level Certification-Friendly Programming for GPU-powered Automotive Systems
Modern automotive systems require increased performance to implement Advanced Driving Assistance Systems (ADAS). GPU-powered platforms are promising candidates for such computational tasks, however current low-level programming models challenge the accelerator software certification process, while they limit the hardware selection to a fraction of the available platforms. In this paper we present Brook Auto, a high-level programming language for automotive GPU systems which removes these limitations. We describe the challenges and solutions we faced in its implementation, as well as a complete evaluation in terms of performance and productivity, which shows the effectiveness of our method.This work has been partially supported by the Spanish Ministry of Science and Innovation under grant TIN2015-65316-P and the HiPEAC Network of Excellence.Peer ReviewedPostprint (author's final draft
Design and Electronic Implementation of Machine Learning-based Advanced Driving Assistance Systems
200 p.Esta tesis tiene como objetivo contribuir al desarrollo y perfeccionamiento de sistemas avanzados a la conducción (ADAS). Para ello, basándose en bases de datos de conducción real, se exploran las posibilidades de personalización de los ADAS existentes mediante técnicas de machine learning, tales como las redes neuronales o los sistemas neuro-borrosos. Así, se obtienen parámetros característicos del estilo cada conductor que ayudan a llevar a cabo una personalización automatizada de los ADAS que equipe el vehículo, como puede ser el control de crucero adaptativo. Por otro lado, basándose en esos mismos parámetros de estilo de conducción, se proponen nuevos ADAS que asesoren a los conductores para modificar su estilo de conducción, con el objetivo de mejorar tanto el consumo de combustible y la emisión de gases de efecto invernadero, como el confort de marcha. Además, dado que esta personalización tiene como objetivo que los sistemas automatizados imiten en cierta manera, y siempre dentro de parámetros seguros, el estilo del conductor humano, se espera que contribuya a incrementar la aceptación de estos sistemas, animando a la utilización y, por tanto, contribuyendo positivamente a la mejora de la seguridad, de la eficiencia energética y del confort de marcha. Además, estos sistemas deben ejecutarse en una plataforma que sea apta para ser embarcada en el automóvil, y, por ello, se exploran las posibilidades de implementación HW/SW en dispositivos reconfigurables tipo FPGA. Así, se desarrollan soluciones HW/SW que implementan los ADAS propuestos en este trabajo con un alto grado de exactitud, rendimiento, y en tiempo real
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