31,029 research outputs found
Human-Centered Autonomous Vehicle Systems: Principles of Effective Shared Autonomy
Building effective, enjoyable, and safe autonomous vehicles is a lot harder
than has historically been considered. The reason is that, simply put, an
autonomous vehicle must interact with human beings. This interaction is not a
robotics problem nor a machine learning problem nor a psychology problem nor an
economics problem nor a policy problem. It is all of these problems put into
one. It challenges our assumptions about the limitations of human beings at
their worst and the capabilities of artificial intelligence systems at their
best. This work proposes a set of principles for designing and building
autonomous vehicles in a human-centered way that does not run away from the
complexity of human nature but instead embraces it. We describe our development
of the Human-Centered Autonomous Vehicle (HCAV) as an illustrative case study
of implementing these principles in practice
Towards a Functional System Architecture for Automated Vehicles
This paper presents a functional system architecture for an automated
vehicle. It provides an overall, generic structure that is independent of a
specific implementation of a particular vehicle project. Yet, it has been
inspired and cross-checked with a real world automated driving implementation
in the Stadtpilot project at the Technische Universit\"at Braunschweig. The
architecture entails aspects like environment and self perception, planning and
control, localization, map provision, Vehicle-To-X-communication, and
interaction with human operators.Comment: Submitted for review to IEEE Transactions on Intelligent
Transportation Systems, 16 pages, 4 figure
Design and Implementation of Real-time Algorithms for Eye Tracking and PERCLOS Measurement for on board Estimation of Alertness of Drivers
The alertness level of drivers can be estimated with the use of computer
vision based methods. The level of fatigue can be found from the value of
PERCLOS. It is the ratio of closed eye frames to the total frames processed.
The main objective of the thesis is the design and implementation of real-time
algorithms for measurement of PERCLOS. In this work we have developed a
real-time system which is able to process the video onboard and to alarm the
driver in case the driver is in alert. For accurate estimation of PERCLOS the
frame rate should be greater than 4 and accuracy should be greater than 90%.
For eye detection we have used mainly two approaches Haar classifier based
method and Principal Component Analysis (PCA) based method for day time. During
night time active Near Infra Red (NIR) illumination is used. Local Binary
Pattern (LBP) histogram based method is used for the detection of eyes at night
time. The accuracy rate of the algorithms was found to be more than 90% at
frame rates more than 5 fps which was suitable for the application.Comment: Thesi
Data-Driven Vehicle Trajectory Forecasting
An active area of research is to increase the safety of self-driving
vehicles. Although safety cannot be guarenteed completely, the capability of a
vehicle to predict the future trajectories of its surrounding vehicles could
help ensure this notion of safety to a greater deal. We cast the trajectory
forecast problem in a multi-time step forecasting problem and develop a
Convolutional Neural Network based approach to learn from trajectory sequences
generated from completely raw dataset in real-time. Results show improvement
over baselines.Comment: Published in ECML KNOWMe: 2nd International Workshop on Knowledge
Discovery from Mobility and Transportation Systems 201
Learning to Detect Vehicles by Clustering Appearance Patterns
This paper studies efficient means for dealing with intra-category diversity
in object detection. Strategies for occlusion and orientation handling are
explored by learning an ensemble of detection models from visual and
geometrical clusters of object instances. An AdaBoost detection scheme is
employed with pixel lookup features for fast detection. The analysis provides
insight into the design of a robust vehicle detection system, showing promise
in terms of detection performance and orientation estimation accuracy.Comment: Preprint version of our T-ITS 2015 pape
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
Computer vision applications based on videos often require the detection of
moving objects in their first step. Background subtraction is then applied in
order to separate the background and the foreground. In literature, background
subtraction is surely among the most investigated field in computer vision
providing a big amount of publications. Most of them concern the application of
mathematical and machine learning models to be more robust to the challenges
met in videos. However, the ultimate goal is that the background subtraction
methods developed in research could be employed in real applications like
traffic surveillance. But looking at the literature, we can remark that there
is often a gap between the current methods used in real applications and the
current methods in fundamental research. In addition, the videos evaluated in
large-scale datasets are not exhaustive in the way that they only covered a
part of the complete spectrum of the challenges met in real applications. In
this context, we attempt to provide the most exhaustive survey as possible on
real applications that used background subtraction in order to identify the
real challenges met in practice, the current used background models and to
provide future directions. Thus, challenges are investigated in terms of
camera, foreground objects and environments. In addition, we identify the
background models that are effectively used in these applications in order to
find potential usable recent background models in terms of robustness, time and
memory requirements.Comment: Submitted to Computer Science Revie
Deep Learning Based Video System for Accurate and Real-Time Parking Measurement
Parking spaces are costly to build, parking payments are difficult to
enforce, and drivers waste an excessive amount of time searching for empty
lots. Accurate quantification would inform developers and municipalities in
space allocation and design, while real-time measurements would provide drivers
and parking enforcement with information that saves time and resources. In this
paper, we propose an accurate and real-time video system for future Internet of
Things (IoT) and smart cities applications. Using recent developments in deep
convolutional neural networks (DCNNs) and a novel vehicle tracking filter, we
combine information across multiple image frames in a video sequence to remove
noise introduced by occlusions and detection failures. We demonstrate that our
system achieves higher accuracy than pure image-based instance segmentation,
and is comparable in performance to industry benchmark systems that utilize
more expensive sensors such as radar. Furthermore, our system shows significant
potential in its scalability to a city-wide scale and also in the richness of
its output that goes beyond traditional binary occupancy statistics.Comment: Accepted for publication in IEEE Internet of Things Journal, Special
Issue on Enabling a Smart City: IoT Meets A
Self-Driving Cars: A Survey
We survey research on self-driving cars published in the literature focusing
on autonomous cars developed since the DARPA challenges, which are equipped
with an autonomy system that can be categorized as SAE level 3 or higher. The
architecture of the autonomy system of self-driving cars is typically organized
into the perception system and the decision-making system. The perception
system is generally divided into many subsystems responsible for tasks such as
self-driving-car localization, static obstacles mapping, moving obstacles
detection and tracking, road mapping, traffic signalization detection and
recognition, among others. The decision-making system is commonly partitioned
as well into many subsystems responsible for tasks such as route planning, path
planning, behavior selection, motion planning, and control. In this survey, we
present the typical architecture of the autonomy system of self-driving cars.
We also review research on relevant methods for perception and decision making.
Furthermore, we present a detailed description of the architecture of the
autonomy system of the self-driving car developed at the Universidade Federal
do Esp\'irito Santo (UFES), named Intelligent Autonomous Robotics Automobile
(IARA). Finally, we list prominent self-driving car research platforms
developed by academia and technology companies, and reported in the media
Fast detection of multiple objects in traffic scenes with a common detection framework
Traffic scene perception (TSP) aims to real-time extract accurate on-road
environment information, which in- volves three phases: detection of objects of
interest, recognition of detected objects, and tracking of objects in motion.
Since recognition and tracking often rely on the results from detection, the
ability to detect objects of interest effectively plays a crucial role in TSP.
In this paper, we focus on three important classes of objects: traffic signs,
cars, and cyclists. We propose to detect all the three important objects in a
single learning based detection framework. The proposed framework consists of a
dense feature extractor and detectors of three important classes. Once the
dense features have been extracted, these features are shared with all
detectors. The advantage of using one common framework is that the detection
speed is much faster, since all dense features need only to be evaluated once
in the testing phase. In contrast, most previous works have designed specific
detectors using different features for each of these objects. To enhance the
feature robustness to noises and image deformations, we introduce spatially
pooled features as a part of aggregated channel features. In order to further
improve the generalization performance, we propose an object subcategorization
method as a means of capturing intra-class variation of objects. We
experimentally demonstrate the effectiveness and efficiency of the proposed
framework in three detection applications: traffic sign detection, car
detection, and cyclist detection. The proposed framework achieves the
competitive performance with state-of- the-art approaches on several benchmark
datasets.Comment: Appearing in IEEE Transactions on Intelligent Transportation System
Real-time Prediction of Automotive Collision Risk from Monocular Video
Many automotive applications, such as Advanced Driver Assistance Systems
(ADAS) for collision avoidance and warnings, require estimating the future
automotive risk of a driving scene. We present a low-cost system that predicts
the collision risk over an intermediate time horizon from a monocular video
source, such as a dashboard-mounted camera. The modular system includes
components for object detection, object tracking, and state estimation. We
introduce solutions to the object tracking and distance estimation problems.
Advanced approaches to the other tasks are used to produce real-time
predictions of the automotive risk for the next 10 s at over 5 Hz. The system
is designed such that alternative components can be substituted with minimal
effort. It is demonstrated on common physical hardware, specifically an
off-the-shelf gaming laptop and a webcam. We extend the framework to support
absolute speed estimation and more advanced risk estimation techniques.Comment: Submitted to IV2019. 7 pages, 4 figures, 3 table
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