18,480 research outputs found
Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence
Vulnerable road users (VRUs, i.e. cyclists and pedestrians) will play an
important role in future traffic. To avoid accidents and achieve a highly
efficient traffic flow, it is important to detect VRUs and to predict their
intentions. In this article a holistic approach for detecting intentions of
VRUs by cooperative methods is presented. The intention detection consists of
basic movement primitive prediction, e.g. standing, moving, turning, and a
forecast of the future trajectory. Vehicles equipped with sensors, data
processing systems and communication abilities, referred to as intelligent
vehicles, acquire and maintain a local model of their surrounding traffic
environment, e.g. crossing cyclists. Heterogeneous, open sets of agents
(cooperating and interacting vehicles, infrastructure, e.g. cameras and laser
scanners, and VRUs equipped with smart devices and body-worn sensors) exchange
information forming a multi-modal sensor system with the goal to reliably and
robustly detect VRUs and their intentions under consideration of real time
requirements and uncertainties. The resulting model allows to extend the
perceptual horizon of the individual agent beyond their own sensory
capabilities, enabling a longer forecast horizon. Concealments,
implausibilities and inconsistencies are resolved by the collective
intelligence of cooperating agents. Novel techniques of signal processing and
modelling in combination with analytical and learning based approaches of
pattern and activity recognition are used for detection, as well as intention
prediction of VRUs. Cooperation, by means of probabilistic sensor and knowledge
fusion, takes place on the level of perception and intention recognition. Based
on the requirements of the cooperative approach for the communication a new
strategy for an ad hoc network is proposed.Comment: 20 pages, published at Automatisiertes und vernetztes Fahren (AAET),
Braunschweig, Germany, 201
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
iTV: Inferring Traffic Violation-Prone Locations with Vehicle Trajectories and Road Environment Data
Traffic violations like illegal parking, illegal turning, and speeding have
become one of the greatest challenges in urban transportation systems, bringing
potential risks of traffic congestions, vehicle accidents, and parking
difficulties. To maximize the utility and effectiveness of the traffic
enforcement strategies aiming at reducing traffic violations, it is essential
for urban authorities to infer the traffic violation-prone locations in the
city. Therefore, we propose a low-cost, comprehensive, and dynamic framework to
infer traffic violation-prone locations in cities based on the large-scale
vehicle trajectory data and road environment data. Firstly, we normalize the
trajectory data by map matching algorithms and extract key driving behaviors,
i.e., turning behaviors, parking behaviors, and speeds of vehicles. Secondly,
we restore spatiotemporal contexts of driving behaviors to get corresponding
traffic restrictions such as no parking, no turning, and speed restrictions.
After matching the traffic restrictions with driving behaviors, we get the
traffic violation distribution. Finally, we extract the spatiotemporal patterns
of traffic violations, and build a visualization system to showcase the
inferred traffic violation-prone locations. To evaluate the effectiveness of
the proposed method, we conduct extensive studies on large-scale, real-world
vehicle GPS trajectories collected from two Chinese cities, respectively.
Evaluation results confirm that the proposed framework infers traffic
violation-prone locations effectively and efficiently, providing comprehensive
decision supports for traffic enforcement strategies.Comment: 12 pages, 19 figures, accepted by IEEE System Journa
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
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video
In Intelligent Transportation System, real-time systems that monitor and
analyze road users become increasingly critical as we march toward the smart
city era. Vision-based frameworks for Object Detection, Multiple Object
Tracking, and Traffic Near Accident Detection are important applications of
Intelligent Transportation System, particularly in video surveillance and etc.
Although deep neural networks have recently achieved great success in many
computer vision tasks, a uniformed framework for all the three tasks is still
challenging where the challenges multiply from demand for real-time
performance, complex urban setting, highly dynamic traffic event, and many
traffic movements. In this paper, we propose a two-stream Convolutional Network
architecture that performs real-time detection, tracking, and near accident
detection of road users in traffic video data. The two-stream model consists of
a spatial stream network for Object Detection and a temporal stream network to
leverage motion features for Multiple Object Tracking. We detect near accidents
by incorporating appearance features and motion features from two-stream
networks. Using aerial videos, we propose a Traffic Near Accident Dataset
(TNAD) covering various types of traffic interactions that is suitable for
vision-based traffic analysis tasks. Our experiments demonstrate the advantage
of our framework with an overall competitive qualitative and quantitative
performance at high frame rates on the TNAD dataset.Comment: Submitted to ACM Transactions on Spatial Algorithms and Systems
(TSAS); Special issue on Urban Mobility: Algorithms and Systems. arXiv admin
note: text overlap with arXiv:1703.07402 by other author
Identifying Driver Behaviors using Trajectory Features for Vehicle Navigation
We present a novel approach to automatically identify driver behaviors from
vehicle trajectories and use them for safe navigation of autonomous vehicles.
We propose a novel set of features that can be easily extracted from car
trajectories. We derive a data-driven mapping between these features and six
driver behaviors using an elaborate web-based user study. We also compute a
summarized score indicating a level of awareness that is needed while driving
next to other vehicles. We also incorporate our algorithm into a vehicle
navigation simulation system and demonstrate its benefits in terms of safer
real-time navigation, while driving next to aggressive or dangerous drivers
Perceptual Attention-based Predictive Control
In this paper, we present a novel information processing architecture for
safe deep learning-based visual navigation of autonomous systems. The proposed
information processing architecture is used to support a perceptual
attention-based predictive control algorithm that leverages model predictive
control (MPC), convolutional neural networks (CNNs), and uncertainty
quantification methods. The novelty of our approach lies in using MPC to learn
how to place attention on relevant areas of the visual input, which ultimately
allows the system to more rapidly detect unsafe conditions. We accomplish this
by using MPC to learn to select regions of interest in the input image, which
are used to output control actions as well as estimates of epistemic and
aleatoric uncertainty in the attention-aware visual input. We use these
uncertainty estimates to quantify the safety of our network controller under
the current navigation condition. The proposed architecture and algorithm is
tested on a 1:5 scale terrestrial vehicle. Experimental results show that the
proposed algorithm outperforms previous approaches on early detection of unsafe
conditions, such as when novel obstacles are present in the navigation
environment. The proposed architecture is the first step towards using deep
learning-based perceptual control policies in safety-critical domains
Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, Applications, and Prospects
The past few decades have witnessed the great progress of unmanned aircraft
vehicles (UAVs) in civilian fields, especially in photogrammetry and remote
sensing. In contrast with the platforms of manned aircraft and satellite, the
UAV platform holds many promising characteristics: flexibility, efficiency,
high-spatial/temporal resolution, low cost, easy operation, etc., which make it
an effective complement to other remote-sensing platforms and a cost-effective
means for remote sensing. Considering the popularity and expansion of UAV-based
remote sensing in recent years, this paper provides a systematic survey on the
recent advances and future prospectives of UAVs in the remote-sensing
community. Specifically, the main challenges and key technologies of
remote-sensing data processing based on UAVs are discussed and summarized
firstly. Then, we provide an overview of the widespread applications of UAVs in
remote sensing. Finally, some prospects for future work are discussed. We hope
this paper will provide remote-sensing researchers an overall picture of recent
UAV-based remote sensing developments and help guide the further research on
this topic
Fast image-based obstacle detection from unmanned surface vehicles
Obstacle detection plays an important role in unmanned surface vehicles
(USV). The USVs operate in highly diverse environments in which an obstacle may
be a floating piece of wood, a scuba diver, a pier, or a part of a shoreline,
which presents a significant challenge to continuous detection from images
taken onboard. This paper addresses the problem of online detection by
constrained unsupervised segmentation. To this end, a new graphical model is
proposed that affords a fast and continuous obstacle image-map estimation from
a single video stream captured onboard a USV. The model accounts for the
semantic structure of marine environment as observed from USV by imposing weak
structural constraints. A Markov random field framework is adopted and a highly
efficient algorithm for simultaneous optimization of model parameters and
segmentation mask estimation is derived. Our approach does not require
computationally intensive extraction of texture features and comfortably runs
in real-time. The algorithm is tested on a new, challenging, dataset for
segmentation and obstacle detection in marine environments, which is the
largest annotated dataset of its kind. Results on this dataset show that our
model outperforms the related approaches, while requiring a fraction of
computational effort.Comment: This is an extended version of the ACCV2014 paper [Kristan et al.,
2014] submitted to a journal. [Kristan et al., 2014] M. Kristan, J. Pers, V.
Sulic, S. Kovacic, A graphical model for rapid obstacle image-map estimation
from unmanned surface vehicles, in Proc. Asian Conf. Computer Vision, 201
Computer Vision for Autonomous Vehicles
In this work, we try to implement Image Processing techniques in the area of
autonomous vehicles, both indoor and outdoor. The challenges for both are
different and the ways to tackle them vary too. We also showed deep learning
makes things easier and precise. We also made base models for all the problems
we tackle while building an autonomous car for Indian Institute of Space
science and Technology
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