2,633 research outputs found
Statistical modelling of algorithms for signal processing in systems based on environment perception
One cornerstone for realising automated driving systems is an appropriate handling of uncertainties in the environment perception and situation interpretation. Uncertainties arise due to noisy sensor measurements or the unknown future evolution of a traffic situation. This work contributes to the understanding of these uncertainties by modelling and propagating them with parametric probability distributions
Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.
Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation
leave a trace - A People Tracking System Meets Anomaly Detection
Video surveillance always had a negative connotation, among others because of
the loss of privacy and because it may not automatically increase public
safety. If it was able to detect atypical (i.e. dangerous) situations in real
time, autonomously and anonymously, this could change. A prerequisite for this
is a reliable automatic detection of possibly dangerous situations from video
data. This is done classically by object extraction and tracking. From the
derived trajectories, we then want to determine dangerous situations by
detecting atypical trajectories. However, due to ethical considerations it is
better to develop such a system on data without people being threatened or even
harmed, plus with having them know that there is such a tracking system
installed. Another important point is that these situations do not occur very
often in real, public CCTV areas and may be captured properly even less. In the
artistic project leave a trace the tracked objects, people in an atrium of a
institutional building, become actor and thus part of the installation.
Visualisation in real-time allows interaction by these actors, which in turn
creates many atypical interaction situations on which we can develop our
situation detection. The data set has evolved over three years and hence, is
huge. In this article we describe the tracking system and several approaches
for the detection of atypical trajectories
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
Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations
This paper highlights the role of ground manifold modeling for stixel calculations; stixels are medium-level data representations used for the development of computer vision modules for self-driving cars. By using single-disparity maps and simplifying ground manifold models, calculated stixels may suffer from noise, inconsistency, and false-detection rates for obstacles, especially in challenging datasets. Stixel calculations can be improved with respect to accuracy and robustness by using more adaptive ground manifold approximations. A comparative study of stixel results, obtained for different ground-manifold models (e.g., plane-fitting, line-fitting in v-disparities or polynomial approximation, and graph cut), defines the main part of this paper. This paper also considers the use of trinocular stereo vision and shows that this provides options to enhance stixel results, compared with the binocular recording. Comprehensive experiments are performed on two publicly available challenging datasets. We also use a novel way for comparing calculated stixels with ground truth. We compare depth information, as given by extracted stixels, with ground-truth depth, provided by depth measurements using a highly accurate LiDAR range sensor (as available in one of the public datasets). We evaluate the accuracy of four different ground-manifold methods. The experimental results also include quantitative evaluations of the tradeoff between accuracy and run time. As a result, the proposed trinocular recording together with graph-cut estimation of ground manifolds appears to be a recommended way, also considering challenging weather and lighting conditions
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
Moving objects segmentation at a traffic junction from vehicular vision
Automatic extraction/segmentation and the recognition of moving objects on a road environment is often problematic. This is especially the case when cameras are mounted on a moving vehicle (for vehicular vision), yet this remains a critical task in vision based safety transportation. The essential problem is twofold: extracting the foreground from the moving background, and separating and recognizing pedestrians from other moving objects such as cars that appear in the foreground. The challenge of our proposed technique is to use a single mobile camera for separating the foreground from the background, and to recognize pedestrians and other objects from vehicular vision in order to achieve a low cost and intelligent driver assistance system. In this paper, the normal distribution is employed for modelling pixel gray values. The proposed technique separates the foreground from the background by comparing the pixel gray values of an input image with the normal distribution model of the pixel. The model is renewed after the separation to give a new background model for the next image. The renewal strategy changes depending on if the concerned pixel is in the background or on the foreground. Performance of the present technique was examined by real world vehicle videos captured at a junction when a car turns left or right and satisfactory results were obtained
Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems
The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
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