32,547 research outputs found
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
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
A Survey of Deep Learning Techniques for Mobile Robot Applications
Advancements in deep learning over the years have attracted research into how
deep artificial neural networks can be used in robotic systems. This research
survey will present a summarization of the current research with a specific
focus on the gains and obstacles for deep learning to be applied to mobile
robotics
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
Online multi-object tracking (MOT) is extremely important for high-level
spatial reasoning and path planning for autonomous and highly-automated
vehicles. In this paper, we present a modular framework for tracking multiple
objects (vehicles), capable of accepting object proposals from different sensor
modalities (vision and range) and a variable number of sensors, to produce
continuous object tracks. This work is a generalization of the MDP framework
for MOT, with some key extensions - First, we track objects across multiple
cameras and across different sensor modalities. This is done by fusing object
proposals across sensors accurately and efficiently. Second, the objects of
interest (targets) are tracked directly in the real world. This is a departure
from traditional techniques where objects are simply tracked in the image
plane. Doing so allows the tracks to be readily used by an autonomous agent for
navigation and related tasks.
To verify the effectiveness of our approach, we test it on real world highway
data collected from a heavily sensorized testbed capable of capturing
full-surround information. We demonstrate that our framework is well-suited to
track objects through entire maneuvers around the ego-vehicle, some of which
take more than a few minutes to complete. We also leverage the modularity of
our approach by comparing the effects of including/excluding different sensors,
changing the total number of sensors, and the quality of object proposals on
the final tracking result
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
Road Detection through Supervised Classification
Autonomous driving is a rapidly evolving technology. Autonomous vehicles are
capable of sensing their environment and navigating without human input through
sensory information such as radar, lidar, GNSS, vehicle odometry, and computer
vision. This sensory input provides a rich dataset that can be used in
combination with machine learning models to tackle multiple problems in
supervised settings. In this paper we focus on road detection through
gray-scale images as the sole sensory input. Our contributions are twofold:
first, we introduce an annotated dataset of urban roads for machine learning
tasks; second, we introduce a road detection framework on this dataset through
supervised classification and hand-crafted feature vectors
Vulnerable road user detection: state-of-the-art and open challenges
Correctly identifying vulnerable road users (VRUs), e.g. cyclists and
pedestrians, remains one of the most challenging environment perception tasks
for autonomous vehicles (AVs). This work surveys the current state-of-the-art
in VRU detection, covering topics such as benchmarks and datasets, object
detection techniques and relevant machine learning algorithms. The article
concludes with a discussion of remaining open challenges and promising future
research directions for this domain
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
Machine Vision in the Context of Robotics: A Systematic Literature Review
Machine vision is critical to robotics due to a wide range of applications
which rely on input from visual sensors such as autonomous mobile robots and
smart production systems. To create the smart homes and systems of tomorrow, an
overview about current challenges in the research field would be of use to
identify further possible directions, created in a systematic and reproducible
manner. In this work a systematic literature review was conducted covering
research from the last 10 years. We screened 172 papers from four databases and
selected 52 relevant papers. While robustness and computation time were
improved greatly, occlusion and lighting variance are still the biggest
problems faced. From the number of recent publications, we conclude that the
observed field is of relevance and interest to the research community. Further
challenges arise in many areas of the field.Comment: 10 pages 5 figures, systematic literature stud
Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge
The main essence of this paper is to investigate the performance of RetinaNet
based object detectors on pedestrian detection. Pedestrian detection is an
important research topic as it provides a baseline for general object detection
and has a great number of practical applications like autonomous car, robotics
and Security camera. Though extensive research has made huge progress in
pedestrian detection, there are still many issues and open for more research
and improvement. Recent deep learning based methods have shown state-of-the-art
performance in computer vision tasks such as image classification, object
detection, and segmentation. Wider pedestrian detection challenge aims at
finding improve solutions for pedestrian detection problem. In this paper, We
propose a pedestrian detection system based on RetinaNet. Our solution has
scored 0.4061 mAP. The code is available at
https://github.com/miltonbd/ECCV_2018_pedestrian_detection_challenege.Comment: ECCV Wider pedestrian detection challenege submissio
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