121,048 research outputs found
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice
Today, one of the major challenges that autonomous vehicles are facing is the
ability to drive in urban environments. Such a task requires communication
between autonomous vehicles and other road users in order to resolve various
traffic ambiguities. The interaction between road users is a form of
negotiation in which the parties involved have to share their attention
regarding a common objective or a goal (e.g. crossing an intersection), and
coordinate their actions in order to accomplish it. In this literature review
we aim to address the interaction problem between pedestrians and drivers (or
vehicles) from joint attention point of view. More specifically, we will
discuss the theoretical background behind joint attention, its application to
traffic interaction and practical approaches to implementing joint attention
for autonomous vehicles
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
Goal-oriented Object Importance Estimation in On-road Driving Videos
We formulate a new problem as Object Importance Estimation (OIE) in on-road
driving videos, where the road users are considered as important objects if
they have influence on the control decision of the ego-vehicle's driver. The
importance of a road user depends on both its visual dynamics, e.g.,
appearance, motion and location, in the driving scene and the driving goal,
\emph{e.g}., the planned path, of the ego vehicle. We propose a novel framework
that incorporates both visual model and goal representation to conduct OIE. To
evaluate our framework, we collect an on-road driving dataset at traffic
intersections in the real world and conduct human-labeled annotation of the
important objects. Experimental results show that our goal-oriented method
outperforms baselines and has much more improvement on the left-turn and
right-turn scenarios. Furthermore, we explore the possibility of using object
importance for driving control prediction and demonstrate that binary brake
prediction can be improved with the information of object importance
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
Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving
With recent advances in learning algorithms and hardware development,
autonomous cars have shown promise when operating in structured environments
under good driving conditions. However, for complex, cluttered and unseen
environments with high uncertainty, autonomous driving systems still frequently
demonstrate erroneous or unexpected behaviors, that could lead to catastrophic
outcomes. Autonomous vehicles should ideally adapt to driving conditions; while
this can be achieved through multiple routes, it would be beneficial as a first
step to be able to characterize Driveability in some quantified form. To this
end, this paper aims to create a framework for investigating different factors
that can impact driveability. Also, one of the main mechanisms to adapt
autonomous driving systems to any driving condition is to be able to learn and
generalize from representative scenarios. The machine learning algorithms that
currently do so learn predominantly in a supervised manner and consequently
need sufficient data for robust and efficient learning. Therefore, we also
perform a comparative overview of 45 public driving datasets that enable
learning and publish this dataset index at
https://sites.google.com/view/driveability-survey-datasets. Specifically, we
categorize the datasets according to use cases, and highlight the datasets that
capture complicated and hazardous driving conditions which can be better used
for training robust driving models. Furthermore, by discussions of what driving
scenarios are not covered by existing public datasets and what driveability
factors need more investigation and data acquisition, this paper aims to
encourage both targeted dataset collection and the proposal of novel
driveability metrics that enhance the robustness of autonomous cars in adverse
environments
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
Connected and autonomous vehicles (CAVs) will form the backbone of future
next-generation intelligent transportation systems (ITS) providing travel
comfort, road safety, along with a number of value-added services. Such a
transformation---which will be fuelled by concomitant advances in technologies
for machine learning (ML) and wireless communications---will enable a future
vehicular ecosystem that is better featured and more efficient. However, there
are lurking security problems related to the use of ML in such a critical
setting where an incorrect ML decision may not only be a nuisance but can lead
to loss of precious lives. In this paper, we present an in-depth overview of
the various challenges associated with the application of ML in vehicular
networks. In addition, we formulate the ML pipeline of CAVs and present various
potential security issues associated with the adoption of ML methods. In
particular, we focus on the perspective of adversarial ML attacks on CAVs and
outline a solution to defend against adversarial attacks in multiple settings
RoadText-1K: Text Detection & Recognition Dataset for Driving Videos
Perceiving text is crucial to understand semantics of outdoor scenes and
hence is a critical requirement to build intelligent systems for driver
assistance and self-driving. Most of the existing datasets for text detection
and recognition comprise still images and are mostly compiled keeping text in
mind. This paper introduces a new "RoadText-1K" dataset for text in driving
videos. The dataset is 20 times larger than the existing largest dataset for
text in videos. Our dataset comprises 1000 video clips of driving without any
bias towards text and with annotations for text bounding boxes and
transcriptions in every frame. State of the art methods for text detection,
recognition and tracking are evaluated on the new dataset and the results
signify the challenges in unconstrained driving videos compared to existing
datasets. This suggests that RoadText-1K is suited for research and development
of reading systems, robust enough to be incorporated into more complex
downstream tasks like driver assistance and self-driving. The dataset can be
found at http://cvit.iiit.ac.in/research/projects/cvit-projects/roadtext-1kComment: to be published in ICRA 202
A Methodological Review of Visual Road Recognition Procedures for Autonomous Driving Applications
The current research interest in autonomous driving is growing at a rapid
pace, attracting great investments from both the academic and corporate
sectors. In order for vehicles to be fully autonomous, it is imperative that
the driver assistance system is adapt in road and lane keeping. In this paper,
we present a methodological review of techniques with a focus on visual road
detection and recognition. We adopt a pragmatic outlook in presenting this
review, whereby the procedures of road recognition is emphasised with respect
to its practical implementations. The contribution of this review hence covers
the topic in two parts -- the first part describes the methodological approach
to conventional road detection, which covers the algorithms and approaches
involved to classify and segregate roads from non-road regions; and the other
part focuses on recent state-of-the-art machine learning techniques that are
applied to visual road recognition, with an emphasis on methods that
incorporate convolutional neural networks and semantic segmentation. A
subsequent overview of recent implementations in the commercial sector is also
presented, along with some recent research works pertaining to road detections.Comment: 14 pages, 6 Figures, 2 Tables. Permission to reprint granted from
original figure author
Reliable Smart Road Signs
In this paper, we propose a game theoretical adversarial intervention
detection mechanism for reliable smart road signs. A future trend in
intelligent transportation systems is ``smart road signs" that incorporate
smart codes (e.g., visible at infrared) on their surface to provide more
detailed information to smart vehicles. Such smart codes make road sign
classification problem aligned with communication settings more than
conventional classification. This enables us to integrate well-established
results in communication theory, e.g., error-correction methods, into road sign
classification problem. Recently, vision-based road sign classification
algorithms have been shown to be vulnerable against (even) small scale
adversarial interventions that are imperceptible for humans. On the other hand,
smart codes constructed via error-correction methods can lead to robustness
against small scale intelligent or random perturbations on them. In the
recognition of smart road signs, however, humans are out of the loop since they
cannot see or interpret them. Therefore, there is no equivalent concept of
imperceptible perturbations in order to achieve a comparable performance with
humans. Robustness against small scale perturbations would not be sufficient
since the attacker can attack more aggressively without such a constraint.
Under a game theoretical solution concept, we seek to ensure certain measure of
guarantees against even the worst case (intelligent) attackers that can perturb
the signal even at large scale. We provide a randomized detection strategy
based on the distance between the decoder output and the received input, i.e.,
error rate. Finally, we examine the performance of the proposed scheme over
various scenarios
Review on Computer Vision Techniques in Emergency Situation
In emergency situations, actions that save lives and limit the impact of
hazards are crucial. In order to act, situational awareness is needed to decide
what to do. Geolocalized photos and video of the situations as they evolve can
be crucial in better understanding them and making decisions faster. Cameras
are almost everywhere these days, either in terms of smartphones, installed
CCTV cameras, UAVs or others. However, this poses challenges in big data and
information overflow. Moreover, most of the time there are no disasters at any
given location, so humans aiming to detect sudden situations may not be as
alert as needed at any point in time. Consequently, computer vision tools can
be an excellent decision support. The number of emergencies where computer
vision tools has been considered or used is very wide, and there is a great
overlap across related emergency research. Researchers tend to focus on
state-of-the-art systems that cover the same emergency as they are studying,
obviating important research in other fields. In order to unveil this overlap,
the survey is divided along four main axes: the types of emergencies that have
been studied in computer vision, the objective that the algorithms can address,
the type of hardware needed and the algorithms used. Therefore, this review
provides a broad overview of the progress of computer vision covering all sorts
of emergencies.Comment: 25 page
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